Method for obtaining a set of compression rules for a data packet transmitted in a network

Abstract

The invention relates to a method for obtaining a set (RS) of compression rules able to compress a data packet (P) transmitted in a network (NET), the packet (P) comprising sections that take respective values expressed in the form of a bit string, the packet (P) being able to be compressed by applying compression rules that are able to delete bits of a value taken by a section of the packet (P), the method comprising, for multiple given packet sections, obtaining a compression potential (PC) for a given section, in relation to the values taken by the given section in the packets belonging to a set (E) of data packets, referred to as the training set, followed by obtaining the set (RS) of compression rules on the basis of the obtained compression potentials (PC).

Description

[0001]Description Title: Method for obtaining a set of compression rules for a data packet transmitted in a network Technical field The technical field is that of data compression and decompression. More specifically, the invention relates to a method for obtaining a set of compression rules capable of compressing a data packet transmitted in a network. It will be seen below that the set of compression rules obtained according to the method of the invention will be used during a phase of compression of a data packet transmitted in a network. Modern digital telecommunications operate on the principle of the exchange of data packets between transmitters and receivers according to protocols such as the IP protocol (acronym for Internet Protocol). A set of data packets relating to a communication is sent by the transmitter to a receiver.Packets contain the receiver's address so that they can be addressed to it using different routing algorithms, and the content of the communication is reconstructed by the receiver by grouping the data contained in the different packets. To achieve complete communication, many protocols must be used, each fulfilling a role from the physical sending of bits, the atomic elements constituting digital data, to the interpretation of data for computer applications. Bits are traditionally grouped into bytes, sets of eight bits. The different protocols are traditionally seen as being organized in layers, with a protocol at one layer relying on a protocol at a lower layer. The OSI (Open Systems Interconnection) model and the Internet model describe the articulation of the various protocols between different layers.For example, in a simplified version, the HTTP protocol (from the English Hypertext Transfer Protocol) is an Application layer protocol, which allows data to be transferred to or from a website. The data to be transferred is organized into data packets. The HTTP protocol uses the TCP protocol (from the English Transmission Control Protocol) which belongs to the Transport layer, for example to request the transfer of data packets representing the content of a website to a user. The TCP protocol, for example, operates in three phases: connection establishment, data transfer, and connection termination. Transport layer protocols will use a Network layer protocol, generally the IP protocol (Internet Protocol) in one of its IPv4 or IPv6 versions.This protocol is only concerned with the transmission of data organized in packets from one address to another, without taking care of the establishment of the connection or the order of arrival of the data packets forming a transmission, these considerations being managed by the protocol of the upper Transport layer. The IP protocol will use a protocol of the Link layer, for example the Ethernet protocol which will organize the sending of the elementary data constituting the packet, these elementary data being bits, transmitted using the Physical layer, whether using an optical fiber transmission, or by wireless communication depending on the case. Other protocols of the Application layer will use other protocols of the lower layers to carry out their functions.For example, the Constrained Application Protocol (CoAP) used in the Internet of Things domain generally relies on the User Datagram Protocol (UDP) of the Transport layer. A common point among all protocols at all layers is the organization of the exchanged data into data packets. A data packet is organized into two parts: the first part is the header of the packet, which groups together the information necessary for the protocol responsible for processing the packet; the second part is the payload, which groups together the data transported in the packet itself. At the Link layer, we speak of a frame rather than a packet, and, in addition to the header, there is a footer indicating the end of the frame. Packet headers include fields. A field is a continuous set of bits of a fixed or variable length.When a field is of variable length, mechanisms are used to determine the length of the field actually transmitted. When the value in a fixed-length field is of variable length, padding bits will complete the value up to the fixed length of the field. A field has an identifier that indicates the nature of the value present in the field. The values ​​present in the field are used by the protocol concerned to process the packet appropriately. For example, in the header of an IP packet, a field with the identifier Version will contain the version number of the protocol used. The size of this field is four bits. Other fields concern, for example, the source and destination IP addresses of the packet. A header is therefore naturally divided into several sections, namely the header fields.The packet payload can also be divided into several sections, for example by defining sections of a constant length expressed in number of bits. A packet analysis tool such as Wireshark or tcpdump can capture and analyze data packets. Analysis makes it possible to separate a data packet exchanged over a network into its header and the payload, and to read the information included in the header and the data included in the payload and to identify the various sections of a packet, both in its header and in its payload. We recall that, following the encapsulation of data packets, the typical header of a packet will consist of several concatenated headers, one for each encapsulated protocol. For example, an Ethernet frame encapsulating an IP packet will begin with the IP header, followed by the UDP header. The fields of the IP packet will therefore be followed in the frame by the fields of the UDP packet.All of these elements are well known in the state of the art and will not be described further. State of the art There is a clear need for compression of data exchanged in digital telecommunications to best save the hardware and energy resources consumed by telecommunications networks. This need explains the creation of protocols dedicated to the compression of exchanged data packets. The exchanged data packets consist, as we have seen, on the one hand of a header containing the information necessary for the transmission of the packet and on the other hand of a payload comprising the data transmitted by the packet as such. Compression can therefore relate to the payload, or to the headers of data packets.Since the values ​​of some fields in the headers of data packets will take constant values ​​or vary little, it is possible to carry out very efficient compression of data packets by seeking only to compress the values ​​of the fields in the headers of the data packets. A first example of a protocol compressing the values ​​of the header fields of data packets is the ROHC protocol (acronym for Robust Header Compression) which is defined by the IETF (Internet Engineering Task Force) standardization body in the RFC (Request For Comments, specification documents at the base of the Internet) document number 3095. The basic idea of ​​this protocol is that some values ​​of the header fields of the packets forming the same communication will not change from one communication to another. For example, the values ​​of the source and destination address fields will remain the same during a communication.After sending a first packet containing the values ​​of these fields, these values ​​will be erased in the headers of the following packets in order to compress the packet header, and therefore the packet itself, and will be inserted upon reception since they are known after sending the first packet. The erasure is performed by a compression module and the insertion of the value by a decompression module. Other types of compression can be applied. This results in very efficient compression of data packets even if the compression is limited to the headers. The ROHC protocol is used, for example, in mobile telecommunications networks. However, it requires that a context be built for each communication indicating which values ​​can be compressed, and in what way. Senders and receivers must therefore regularly update the contexts used for header compression.This update operation is very costly and other mechanisms will be preferred in certain usage contexts. The context of communication networks dedicated to connected objects imposes additional constraints. We are talking here about low-power wide area networks, also called LPWANs, an acronym for Low-Power Wide Area Networks. In such a network, we find connected objects that must save their power consumption as much as possible, such as meters deployed in locations without power supply and having to use a battery for several years. These objects communicate wirelessly with a gateway that covers a wide area. Updating the context with each communication would imply high bandwidth consumption, so the ROHC header compression protocol is not suitable in this context.In addition, the data exchanged by these objects is often very small, and may be limited to signaling that the object is still active, or the object may want to report a single value, such as a temperature for a thermometer, or a water level for a water meter. The size of the headers of the data packets proportionally to the size of the payload will therefore be proportionally larger in the context of LPWANs than in other contexts. There is therefore a need for efficient compression of data packets which, in the context of LPWANs, will focus on compressing the values ​​of the header fields. The SCHC protocol (acronym for Static Context Header Compression), defined by the IETF in RFC 8724, was designed for use in the context of LPWANs. It is based on a static context.For a given context, for example a type of connected object deployed in a given network, compression rules will be defined. These rules group together operations that can be applied to the values ​​of the fields in the packet headers. These operations can be of three types, all based on the idea that the values ​​of certain fields, or part of the values, can be erased before transmission and then reconstructed upon reception: • A first type of compression operation is erasure: the value of the field is read and will be erased by the compression module if the value is indeed the expected one. The compression module is for example located in the connected object. The erased value will be inserted upon reception by the decompression module which is located in the gateway and which has the same rules as the compression module. This type of operation is suitable for fields that take a constant value, such as the Version field of IP packets.If the field takes a value other than the expected one, the operation does not apply, the compression rule is ignored, and another rule will possibly be used or the field value can be transmitted unchanged. •A second type of compression operation uses a dictionary: a dictionary is built for certain field values ​​and if the field value is in the dictionary, the compression module will replace the field value with its index in the dictionary. The decompression module uses the dictionary to replace the index with the deleted value. Here again, a field value outside the dictionary can be transmitted unchanged if no rule applies. • A third type of operation consists of prefix deletion: the field value is a sequence of bits, and a more or less long prefix can be defined in a compression operation.If the value does indeed have this prefix, the prefix will be erased during compression and inserted during decompression, or the value will be transmitted unchanged if the prefix is ​​not recognized by the operation. For completion reasons, a fourth type of operation is defined which consists of ignoring the value of the field and transmitting it as is. Other combinations are possible, as well as other compression / decompression actions, for example to handle variable-length fields. A set of compression rules will then be defined for a given deployment context. For example, compression rules (and their symmetric decompression rules) can be defined for a gateway managing an LPWAN network and for all the equipment connected to this network and to the Internet through the gateway.The set of rules will then be able to process in compression and decompression all the data packets that are exchanged in the LPWAN network, the compression being limited to the values ​​of the header fields. The SCHC protocol makes it possible to determine which compression rule applies when a data packet circulates in the network. To do this, each compression rule present in the set is defined for a given packet header format and will consist of a set of operations to be applied to the values ​​of the header fields of a packet that respects the format. Several rules can cover the same data packet header format. For example, compression rules will be defined for the format of IP packets encapsulating a UDP packet, and these rules will include operations for all the fields of an IP packet header followed by a UDP packet header.A compression rule is part of a rule set and has a rule identifier. The rule set will seek to cover all possible data packet header formats that can be exchanged in a given context. The SCHC compression / decompression protocol is applied by a compression module that will process a packet before sending it to a decompression module. The compression and decompression modules have the same set of rules, defined for a given context. The protocol will consist, when a data packet must be processed by the compression module, of browsing the set of rules available to the compression module in order to retain one or more rules. If a rule does not match the format of the fields in the header of the processed packet, the rule is not retained.If the format of the fields matches, it is necessary to examine whether the field values ​​correspond to all the matching operators provided in the rule. If this is not the case, the rule is again not retained. If no rule in the set matches, the packet is sent without processing. In any case, the packet payload is not processed by the SCHC protocol. In the context of LPWANs, packet compression limited to the compression of the header field values ​​will still be effective, because the size of the headers is proportionally large. Here we see the interest of the operation which consists of ignoring the value of the field and transmitting it as is. This operation ensures that a rule can actually be applied to all the fields of a given header: for fields that are easy to compress, an efficient compression operation will be defined, and for the other fields the operation which does nothing will serve as completion.Finally, several rules can be retained for which the format of the fields on the one hand and the values ​​of the fields on the other hand correspond. One rule is then chosen, and only one, for example the first encountered in the set of rules, or chosen randomly, at the choice of the SCHC protocol implementation. The operations provided in the rule are applied by the compression module to the values ​​of the fields in the packet header. Then the packet is transmitted with the values ​​of its compressed header, as well as with the identifier of the rule that was applied. The decompression module receives the packet whose header values ​​have been compressed; uses the identifier of the rule to find the operations that have been applied; and applies the symmetric decompression operations.For example, if, for a value of a field, the compression operation used a dictionary, the decompression module will use the transmitted index value to replace it with the value expected in the dictionary. Since the identifier of the rule used for compression must be transmitted, as well as certain values ​​(indexes of the dictionaries), there will be a limit beyond which adding new compression rules in a given context will no longer be interesting because the possible gain brought by the additional rule is exceeded by the transmission cost induced by the increase in the size of the rule identifier as new rules are added. For example, beyond eight rules, the transmitted rule identifier will be coded on three bits instead of two. If adding a ninth rule only saves on average a single bit, adding this ninth rule is not interesting.Likewise if an added rule involves the use of additional bits to encode dictionary indexes. It is easy to see that the compression and decompression operations in the SCHC protocol can be lossless, that is, decompressing the data packet header according to the operations provided in the SCHC protocol reconstructs the header exactly as it was before compression. This is the general case even if there may be usage frameworks where this is not the case. The sets of compression rules are therefore defined, in the SCHC protocol, for a given, static context, and correspond to the packet formats expected in the exchanges between senders and receivers. They are defined by an expert who knows that, in a given deployment context, such or such protocol will be used, according to a given encapsulation, and who therefore knows which packet header formats are expected.The expert also knows that a given field of packets respecting a given format will take a constant value, or a value from a limited set, or a value that will often have the same prefix. For example, the expert knows that the IP-Version field that will appear in the headers of packets respecting several formats (IP only, or IP encapsulating UDP packets, or other…) will be constant. Or the expert knows that the IP address values ​​present in a given field belong to a limited range and will therefore have a common prefix. This knowledge allows the expert to choose the most appropriate operation for a given field in a given format and to integrate it into the rule. The expert therefore builds a set of rules that will be deployed, for example attached to compression and decompression modules embedded in a set of connected objects and a corresponding gateway, to implement the SCHC protocol.The SCHC protocol was initially designed in the context of LPWANs, but it could be used in other contexts, such as home networks, or for Ethernet frame header compression. The compression operations of the SCHC protocol could also be applied to sections of the payload of a data packet and not only to the header. In the current situation, the deployment of the SCHC protocol is slowed by the need to create new rules for each usage context. This situation results in easily accessible compression potentials not being used. This has negative impacts on the energy and hardware consumption of LPWANs or those of other networks in which SCHC header compression could be used.Another limitation of the current situation is that compression rules defined for a given deployment context will no longer work as well as that context evolves. For example, if a connected object reboots, its network address may change or the port numbers it uses may change, and compression rules that were effective may become ineffective. This introduces inefficiency into the compression that can be achieved. Furthermore, it is desirable to use the compression operations defined by the SCHC protocol, which provide very effective compression, in as many contexts as possible. For example, these compression operations should be able to apply to sections of the packet payload, not just header fields.However, there is currently no technical solution for applying the header compression principles defined in the SCHC protocol to a dynamic context, even if it is weakly dynamic. For example, if, in a deployment context, SCHC compression is applied to an IP address value, and this value changes even if at a very low frequency, the rule established in a static context becomes inoperative from the first change of IP address. Or, if a given section of a load varies, even slightly, the rules of the SCHC protocol will not apply. The invention improves the situation.Disclosure of the invention According to a first functional aspect, the invention relates to a method for obtaining a set of compression rules for a data packet transmitted in a network, the packet comprising sections taking respective values ​​expressed in the form of a sequence of bits, the packet being capable of being compressed by applying compression rules capable of removing bits from a value taken by a section of the packet, the method comprising, for several given sections of packets, obtaining a compression potential for a given section, relative to the values ​​taken by the given section in the packets belonging to a set of data packets, a so-called learning set, followed by obtaining the set of compression rules as a function of the compression potentials obtained. Thanks to the invention, a set of compression rules relevant in terms of compression is created automatically.The compression rules comprise compression operations that apply to values ​​taken by sections of data packets. The compression rules of the obtained set can be applied to the data packets transmitted in the network in order to achieve effective and efficient compression of the transmitted data packets. The advantage of the invention is that the compression rules can change automatically when the transmission context changes and remain effective. This makes it easier to deploy compression protocols and to use them in many contexts, including dynamic contexts. In the invention, so-called potential compression quantities are obtained from a set of packets, a so-called training set. This so-called training set is representative of the expected traffic, which will have to be compressed by the set of compression rules obtained.It will be seen below that obtaining compression potentials makes it possible to identify the most promising sections to be compressed in this representative set and thus to guide the creation of compression rules. Obtaining compression potentials is based on the frequencies of appearance of different values ​​in the so-called learning set, which is a quantity that is easy to obtain even for very large so-called learning sets. According to one embodiment, the method further comprises the application of at least one compression operation to a section value of said data packet, said operation being included in a compression rule belonging to the set of compression rules obtained, followed by the transmission of said data packet to which said at least one compression operation has been applied and of data representative of the compression rule to which said operation belongs.By means of this embodiment, the compression rules obtained are applied to data packets and enable effective compressions to be performed. The data packets are compressed by applying compression operations that erase portions of value, or even complete values, taken by sections of the data packet. To reconstruct the portions of value erased upon receipt of the compressed packet, the latter must be transmitted with data representative of the compression rule that has been applied to it. According to one embodiment, which may be implemented alternatively or cumulatively with the preceding embodiments, the section of the data packet to the value of which a compression operation is applied is a field of a header of the data packet.According to another embodiment, which may be implemented alternatively or cumulatively with the preceding embodiments, the section of the data packet to the value of which a decompression operation is applied is a previously defined delimitation of the payload of the data packet. These two embodiments make it possible to apply the invention to all the values ​​taken by the sections of the data packet, whether header fields or previously defined and delimited parts of the payload of the packet. In the latter case, the fact that the delimitation is defined beforehand makes it easier to apply the method. A given operation, found using the transmitted representative data, will for example be applied to the first 256 bits of the payload, and the packet may be restored to the original using the knowledge of this definition prior to the delimitation.Another pre-defined delimitation may, for example, consist of applying to the entire payload of the packet, or to the first or second half, or to any other pre-defined proportion. The fields of a header are themselves pre-defined sections since the structure of the header, and therefore its delimitation into several fields, is defined by the protocol, or the stack of protocols, to which the transmitted data packet belongs. According to another embodiment, which may be implemented alternatively or cumulatively with the preceding modes, the data packet to which said at least one compression operation has been applied is transmitted jointly with the data representative of the compression rule to which said compression operation belongs. Thanks to this embodiment, the data packet is transmitted jointly with the data representative of the compression rule that has been applied to it.This joint transmission will facilitate the subsequent decompression operation that will be applied to the data packet, by ensuring that, when a data packet is transmitted, it is transmitted with the information that makes it possible to know the compression operation that has been applied to it. According to another embodiment, which may be implemented alternatively or cumulatively with the preceding modes, the transmitted data packet is formed by concatenating data representative of a compression rule, said data being an identifier of the compression rule consisting of a sequence of bits, with the data packet to which at least one compression operation belonging to said compression rule has been applied, which belongs to a set of compression rules obtained by learning. Thanks to this embodiment, a precise format of the compressed data packet is defined which will facilitate the transmission thereof.The transmitted data packet is a new data packet formed by concatenating a compression rule identifier, namely a sequence of bits, with the original data packet to which the compression rule was applied. The compressed data packet is therefore transmitted together with a compression rule identifier that will make it possible to find the latter among the set of compression rules obtained by learning. According to another embodiment, which may be implemented alternatively or cumulatively with the previous modes, obtaining the set of compression rules comprises the implementation of a reinforcement learning algorithm. Reinforcement learning, unlike supervised learning, does not require any expert intervention.The technical effect is that compression rules can be obtained automatically to compress data packets transmitted over the network. A first advantage is that compression rules can be obtained cheaply and quickly since this does not require the intervention of an expert. Another advantage is that compression rules can change automatically when the transmission context changes and remain effective.According to another embodiment, which may be implemented alternatively or cumulatively with the preceding embodiments, the reinforcement learning algorithm implemented in obtaining the set of compression rules uses as a learning set the so-called learning set, formed of data packets collected beforehand; implements an environment which comprises the so-called learning set and the set of compression rules; and the change of state in the reinforcement learning algorithm comprises the addition of a new rule to the set of compression rules. As the invention relies in this embodiment on the use of a learning algorithm, the invention requires the use of a learning set which must be defined beforehand.This training set is a set of data packets for which the learning algorithm will seek to produce a set of compression rules that best compresses it. This training set is the so-called training set that is used to obtain the compression potentials of a given section of a packet according to the values ​​taken by the given section in the packets of the so-called training set. It is clear that the learning will be all the more relevant if the training set is representative of the expected traffic whose data packets must be compressed. If the training set is too small, in particular, the learning will not be able to obtain a set of effective compression rules in all cases, because examples of traffic that could have been compressed are not present in the training set.Reinforcement learning consists of evolving the set of compression rules that is learned, in an environment comprising the training set that the set of compression rules must compress. This evolution is done by adding a new rule to the set of compression rules. The training set, for its part, does not evolve during the training phase. According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes, the reinforcement learning algorithm comprises a reward function which is calculated according to the compression of the packets of the training set by the compression rules belonging to the set of compression rules. It is advantageous that several parameters of the reinforcement learning algorithm are defined in order to be able to execute it.Reinforcement learning consists of evolving an actor, here the set of compression rules that we learn, in an environment, here the union of the set of rules and the training set, and guiding the evolutions of the actor using a reward function. The reward function is the compression achieved by the set of compression rules on the packets in the training set. The better the compression achieved by the set of rules, the better this set is and therefore reinforcement learning will tend to produce a set of rules that produces better compression. As already seen, the change of state is the addition of a rule to the set of rules; the reward function will sanction whether this addition of a rule is relevant or not and allows the set of compression rules to evolve towards better compression.The advantage of this mode is therefore to specify how to execute a reinforcement learning algorithm to learn a set of compression rules and how to choose relevant parameters of the reinforcement learning to obtain good compression of the packets of the training set by the set of rules resulting from the training. According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes, the learning of a set of compression rules is carried out from an initial set consisting of a single rule not carrying out any compression. In this embodiment, the starting point is a set of rules formed by a single rule applying to all the packets of the collected training set.This set ensures that from the start, the set of rules will apply to the entire training set even if the initial rule does not perform compression on the section values ​​of the packets in the training set. In most embodiments of our invention, the set of compression rules evolves so as to ensure that, for all the packets in the training set, there exists in the set of compression rules a rule that can apply, which is indeed the case with this single rule performing no compression. The advantage of this embodiment is that it does not require the intervention of any expert to define the initial set of compression rules that is submitted to the learning algorithm.According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes, the learning of a set of compression rules is carried out from an initial set comprising a compression rule provided by an expert. The advantage of this embodiment is to allow an expert to ensure that a rule which seems very useful to him is indeed present in the set of compression rules. By placing it from the start of the learning, this result is obtained. The rest of the learning algorithm can be done by reinforcement, therefore without subsequent intervention of the expert. We therefore obtain here a mixed mode where the intervention of the expert is limited.According to another embodiment, which may be implemented alternatively or cumulatively with the preceding embodiments, the management method comprises the following steps executed when obtaining the set of compression rules: • a step of selecting a rule belonging to the set of compression rules using at least one compression potential among those obtained; • a step of creating a new rule from the selected rule using at least one compression potential among those obtained; • adding the new rule to the set of compression rules; and, furthermore, said steps are iterated, until a stopping criterion is satisfied, using at the following iteration the set of rules modified by the addition of the new rule. Thanks to this embodiment, obtaining a set of compression rules is guided efficiently.Indeed, the obtaining can work by randomly making state changes that will be retained or not according to a reward function. This way of proceeding could on the one hand take a lot of time and on the other hand lead to sets of compression rules comprising too many rules if the most efficient rules are not created first. However, we have seen that if too many compression rules are created, this becomes an obstacle to efficient compression because the index of the rule used during compression must be transmitted with the compressed packet. Too many rules imply an index coded on a large number of bits which makes compression ineffective. It is therefore necessary to guide the obtaining of compression rules. For this, the obtained compression potentials will first indicate which rule is selected to create a new rule and then how to create a new rule from the selected rule.Obtaining works by iteration until a stopping criterion is satisfied, and the iteration consists of applying the same algorithm to an environment formed by the new set of compression rules (with a new rule added) and the so-called training set. To ensure that obtaining takes into account the addition of the new rule, it may be useful to calculate at the next iteration compression potentials that take into account the addition of the new rule. This can be done by isolating in the so-called training set the subsets of packets for which the different rules apply. In this mode, the values ​​compressed by the new rule will not be taken into account in the compression potential calculations. Compression potentials will only be calculated for values ​​that are not compressed in the current state of the compression rule set that is being obtained.According to another embodiment, which may be implemented alternatively or cumulatively with the preceding embodiments, if the added rule applies to all the data packets of the so-called learning set to which the selected rule applies, then the selected rule is removed from the set of compression rules. It is advantageous that the addition of the new rule ensures that the set of rules that is being obtained will indeed apply to all the packets of the so-called learning set. This is why, in this embodiment, if the added rule does not cover all the packets covered by the selected rule from which the added rule was created, then the selected rule is retained.Since the obtaining algorithm can start, in certain modes, from a set comprising an initial rule applying to all the packets, it is then certain to have at the end a set of rules applying to all the packets of the so-called learning set. However, the deletion of the selected rule if the added rule covers the same packets makes it possible to ensure that useless rules are not retained and therefore to minimize the size of the set of compression rules resulting from the learning algorithm. According to another embodiment, which can be implemented alternatively or cumulatively with the preceding modes, the stopping criterion is satisfied when the compression obtained on the so-called learning set by the modified set of compression rules is less than or equal to the compression obtained by the previous set of rules.According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes, the stopping criterion is satisfied when the set of rules reaches a predetermined size expressed in number of rules or in number of compression operations included in the rules. These two modes have the advantage of proposing a stopping criterion that is easy to calculate. In the first mode, the compression carried out by the set of rules on the so-called learning set is calculated and the obtaining stops when the compression no longer progresses. This will happen for example when all the values ​​of the sections of the packets of the so-called learning set are compressed. In this case, it is no longer possible to add a compression operation.The compression achieved by the new set may be lower than the compression achieved by the previous set because it must be taken into account that the compression rules must be identified, and that the size of the index of the added rule may be larger than the compression achieved by this rule. In the second mode, an absolute size criterion of the set of compression rules is used, either in number of rules or in number of operations. This mode ensures that the size of the set of rules obtained always remains below a certain size, which is advantageous in a context where we want to save resources and where we want to ensure that the indexes of the compression rules can be encoded with a limited number of bits (from two to four for example).This embodiment is particularly advantageous when the set of compression rules obtained by the method is used by equipment with limited memory capacities, which will only be able to store a limited number of compression rules for data transmitted or received. The two modes can be combined, namely that the learning continues as long as the compression carried out by the set of rules progresses unless a predefined maximum size is reached. According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes, one of the compression potentials obtained is called the field compression potential and is calculated for a given section of fixed length according to the formula ^^( ^^ − ^^) in which N is the number of times that the section appears in the so-called learning set; L is the length of the section expressed in bits and H is a quantity called the field entropy estimator which. following the formula ^^ = − ∑ ^^∈[1, ^^]^^ ^^ ^^ ^^ ^^2( ^^ ^^ ) in which K is the number of different values ​​^^^^∈[1, ^^] that the section takes in the so-called learning set and ^^ ^^ is the frequency of occurrence of the value ^^ ^^. In many embodiments, the sections for the values ​​of which the obtaining algorithm will seek to produce compression operations belonging to rules are header fields. This explains the choice of the terms "field compression potential" and "field entropy estimator". However, the embodiment can be applied both to header fields and to delimited sections in the payload of a data packet. The calculation formula in this embodiment applies to a section whose length L, expressed in bits, is constant for all the values ​​taken by the section in the so-called training set. It will be seen later that another embodiment can be applied to sections of variable size.With this embodiment, the field compression potential is calculated for a given section, in order to detect which is the most interesting section to compress and thus guide the obtaining algorithm. For this, we use a quantity that estimates the entropy present in a delimited section of the charges of the packets of the so-called training set or in a given field of the headers of the packets of the so-called training set, which we call section in general. We recall that an entropy (in the sense of information theory) is calculated from probabilities. Here, we have a so-called training set, therefore observed values. This is why we calculate an entropy estimator, and not an entropy itself, from the frequencies of appearance ^^. ^^ values ​​^^ ^^that the section considered takes. These frequencies are strictly positive and less than or equal to 1 by definition. The logarithms of the frequencies are taken in base 2 to apply to values ​​expressed in bits. The logarithms are necessarily negative or zero since the frequencies are worth 1 at most. Negations make the entropy estimator necessarily positive. The entropy estimator can therefore be worth at least zero. This is the case when the given section presents a single value in the so-called learning set. The frequency of appearance of this unique value is therefore worth 1; and the logarithm is worth 0. Furthermore, the entropy estimator will always be less than the length of the section expressed in bits.If a delimited section, or a field, takes a large number of different values, and therefore if its entropy estimator increases, then the length of the section expressed in bits which is necessary to encode this large number of different values ​​will also increase and will always remain greater than the entropy estimator. The quantity called field compression potential is therefore always positive and will be maximum for a given section when its entropy estimator is zero. This happens when the section takes a single value in the so-called training set and it is easy to understand that a section which takes a single value will be easy to compress. Just erase this value! The field compression potential decreases as the entropy estimator increases.If a section with a zero entropy estimator appears only a few times in the training set, or if the length of the section is small, another section with a higher entropy estimator but which appears more often, or whose length is greater, may be more interesting to compress than the section with a zero entropy estimator. This is why the field compression potential takes into account the number of times the section appears as well as its length. The field compression potential therefore makes it possible to effectively guide the obtaining algorithm by indicating the sections of the training set whose compression will yield the most according to the reward function which is given by the compression carried out, said sections being able to be header fields of the packets or delimited sections of the packet payload.According to another embodiment, which may be implemented alternatively or cumulatively with the previous embodiments, the field compression potential is calculated for a given section of variable length according to the formula ∑ ^^ ^. ^=1 ^^ ^^ . ( ^^ ^^ − ^^ ^^ ) in which K is the number of values ​​^^ that takes the section in the so-called training set; ^^ the number of times the section ^^ ^^ in the so-called learning set; ^^ the length of the value ^^ ^^ expressed in bits and ^^ is a ^^ quantity called partial entropy estimator of a value, which is calculated for the value ^^ ^^ following the formula ^^ ^^ ^^ ^^ ^^ ^^ ^^2( ^^ ^^ ) in which ^^ ^^ is the frequency of occurrence of the value ^^in the so-called learning set. Thanks to this embodiment, the field compression potential can be calculated for a section of variable length. Several elements will therefore be taken into account in this calculation. A value that appears frequently, but which is of short length, will be able to contribute less to the field compression potential than a value that appears less frequently but whose length is greater. This embodiment therefore makes it possible to carry out an accurate calculation of the field compression potential even for a section of variable length. It can be noted that the formula used in this mode gives the same results as the formula given previously in the case where the section has a constant length L for all the values ​​taken by the section. According to another embodiment, which can be implemented alternatively or cumulatively with the ^^ previous modes, the field compression potential is calculated according to the formula in which ^^+ ^^ ^^ is a strictly positive constant. This embodiment provides a variant for obtaining a field compression potential. In this formula, the potential is indeed maximal when the entropy estimator is zero. The positive constant ensures the definition of the field compression potential in this case. This variant is suitable for the case where the section is of fixed length. According to another embodiment, which can be implemented alternatively or previous modes, the field compression potential is calculated according to the formula ^^=1 ^^ ^^ ^^ + ^^ danswhich ^^ is a strictly positive constant. This embodiment takes up the previous variant but adapts it to the case where the section is of variable length. According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes, the field compression potential is called corrected compression potential and is calculated for a given section by multiplying the field compression potential by a factor of ^^− ^^ correction ^^ − where N is the number of times that the section appears in the set called number of different values ​​that the section takes in the so-called learning set; and ^^, ^^ and ^^ are chosen positive coefficients. In our experience, the field compression potential helps guide the obtaining algorithm but has the following defect: when a section has different values ​​distributed uniformly, the entropy estimator increases too slowly as new values ​​are observed in the so-called learning set. As long as it has a large length, a section that has different values ​​each time in the so-called learning set may have a better field compression potential than a section with few values ​​but a smaller size. However, in practice, it is this section with few values ​​for which we wish to create one or more compression rules that will apply to the few observed values.Whereas the very long section with all different observed values ​​will be of little interest because it is likely that in use, the values ​​that appear in this section will still be new values, and therefore values ​​that will not be compressed by the compression rules learned on a training set where they do not appear. It is to correct this defect that we introduce a correction factor that allows us to obtain a corrected compression potential. The coefficients ^^, ^^ and ^^ are preferably positive to ensure that the correction factor makes the compression potential evolve in the desired direction. The idea is that a section that presents all different values ​​must be disadvantaged compared to a section presenting an observation that seems less random.According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes, the positive coefficients ^^, ^^ and ^^ are chosen to take constant values. According to a particular embodiment of this last mode, which can be implemented alternatively or cumulatively with the previous modes, the positive coefficients ^^, ^^ and ^^ take the values ​​^^ = 1, ^^ = 0 and ^^ = 0. ^^Thanks to this last mode, the correction factor takes the value 1 − and is therefore zero when all the ^^ values ​​of the section are different and we have ^^ = ^^. Such an observation in the so-called training set of a section whose values ​​are all different strongly indicates that the values ​​outside the so-called training set will also be different, and that such a section cannot be compressed efficiently.The correction factor will then cancel the field compression potential 1 for this section. When a section takes a single value, the correction factor is equal to 1 − and the corrected ^^ correction potential is therefore reduced compared to the initial value of the field correction potential, but is ultimately very little reduced. Other constant values ​​can of course be taken to refine the correction provided by the correction factor. According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes, the positive coefficients ^^, ^^ and ^^ take the values ​​^^ = 1, ^^ = 1 and ^^ = 0. ^^−1Thanks to this last mode, the correction factor takes the value 1 − and is therefore exactly 1 when ^^. the section takes a single value, that is to say for ^^ = 1. Conversely, the correction factor does not cancel when ^^ = ^^ which can be interesting to maintain a non-zero field compression potential even on a section which still seems to be very random since all the values ​​taken by the section in the training set are different. Thus, the field compression potential being non-zero, the obtaining algorithm will be able in certain contexts to add one or more compression rules relating to the section. According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes, the positive coefficients ^^, ^^ and ^^ are chosen as a function of K and N. This embodiment makes it possible to obtain other values ​​of the correction factor to refine its choice as a function of the sizes of the samples observed in the training set.For example, taking ^^ = 0.5 ^^ 1, ^^ = 0.5 ^^ and ^^ = ^^, we obtain a correction factor which is equal to 1 −. and which presents a differentiated influence according to the size of the sample of the values ​​observed for the section for which the corrected compression potential is calculated. According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes, one of the compression potentials obtained is called the value compression potential and is calculated for a value ^^ ^^ of a given section according to the formula ^^( ^^ − ^^ ^^ ) in which N is the number of the value ^^ ^^ appears in the so-called learning set; L is the length of the value ^^ ^^ in bits; and ^^ ^^ is a quantity called partial entropy estimator of a value which is calculated according to the formula ^^ ^^ = − ^^ ^^ log2( ^^ ^^ ) in which ^^ ^^is the frequency of occurrence of the value ^^ ^^This embodiment constructs, according to the same model as that seen previously for the field compression potential, and using the same quantities, a compression potential adapted to the values ​​that a given section takes in the training set, knowing that a section can be a field of a packet header or a delimited section of a packet payload. It can be noted that this value compression potential is expressed according to the length of the value and is therefore adapted to a section of variable length. A value of short length but which appears frequently may have a compression potential greater than a longer value but which appears less frequently. This potential will also guide the creation of compression rules by indicating the values ​​which are to be compressed as a priority.It will therefore not necessarily be the value that appears most frequently since, in the case of a section of variable length, the effective length of the value taken by the section is taken into account and can compensate for a lower frequency of appearance. As seen previously, the frequency of appearance of a value is strictly positive and is worth 1 at most. The partial entropy estimator of a value will therefore also be positive and will be worth 0 for a value that happens to be the unique value taken by a section in the so-called learning set and which would then have a frequency of appearance that would be worth 1. The partial entropy estimator of a value will always be strictly less than 1.The compression potential of a value will therefore always be positive and will be maximal for a value which would be the unique value observed for a given section and will be minimal for the value which is observed least often in this section in the so-called learning set. According to another embodiment, which may be implemented alternatively or cumulatively with the preceding modes, the value compression potential is calculated according to the formula in which. ^^ is a strictly positive constant. Thanks to this embodiment, an alternative formula is given for calculating a value compression potential. According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes, one of the compression potentials obtained is called the field bit compression potential and is calculated for a rank ^^ of a given section, the ranks ^^ varying from 0 to ^^ − 1, L being the minimum length of the values ​​taken by the section in the training set, said length L being expressed in number of bits, and said quantity is calculated according to the formula 1 − ^^( ^^ ^^ ), In is a quantity called the entropy estimator of a field bit which is calculated according to the formula ^^( ^^ ^^ ) = − ( ^^ ^^ . ^^ ^^ ^^2( ^^ ^^ ) + (1 − ^^ ^^ ). ^^ ^^ ^^2(1 − ^^ ^^ )), said formula being by convention 0 when ^^ ^^is 0 or 1, a formula in which the values ​​taken by the section in the so-called learning set are expressed in ordered bit sequences and in which ^^ ^^is the frequency of appearance of one of the values ​​0 or 1 for the bit of rank ^^ among the values ​​taken by the section in the so-called learning set. This embodiment constructs, according to the models seen previously, a compression potential to measure the compression potential of the bits of a section in a given rank. As for the potentials already seen, this quantity is calculated for a section which can be a header field or a delimited load section, and the term "field bit compression potential" is retained because it is used, in many embodiments, to apply to a header field. The principle is again to note frequencies of appearance, here the frequencies of the bit values ​​0 or 1 for a given rank of the section. If this frequency is 1, this means that, for a given rank, it is always the same bit value which appears, and the entropy estimator will then be minimal and will be worth 0 by convention.The field bit compression potential for this rank will then be 1, and this is the maximum value that this quantity can take. For a frequency of 0.5, the base 2 logarithm is -1 and the entropy estimator is 1, which gives a field bit compression potential for this rank that will be 0, which is the minimum value that this quantity can take. For frequencies of 0.25 and 0.75 for a given rank, the base 2 logarithms are -2 and -0.415 respectively. The field bit entropy estimator is then − (0.25. ( −2 ) + 0.75. ( −0.415 ) ) = − (−0.5 −0.31125) = 0.81125 and the field bit compression potential is then 0.18875, well between 0 and 1. We can also see how the compression potential, here for the ranks of the sections whose values ​​are expressed in a sequence of bits, will make it possible to guide the learning algorithm by indicating the most interesting values ​​to compress, taking into account here the representation of the values ​​in the form of a sequence of bits. According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes, the step of selecting a rule belonging to the set of compression rules comprises the partition of the so-called learning set into subsets attributed to a rule of the set of compression rules; the calculation of a compression potential for the rules of the set of compression rules and the selection of the rule according to its compression potential,the compression potential of a rule being calculated from field compression potentials calculated on the subset of the so-called training set assigned to the compression rule. With this embodiment, the field compression potentials are used to select a rule from the set of compression rules that is being obtained. The so-called training set is partitioned by assigning to a given rule the subset for which the rule achieves the best compression. The field compression potentials are calculated for the fields not compressed by the rule in question and then added together for all the packets of the so-called training set. In this way, all other things being equal,obtaining will seek to improve a rule that applies to more headers than another and will also avoid recreating rules already created. The starting point of the obtaining algorithm being a set formed by a single rule that applies to all values ​​(ignoring them) and that does not compress, this rule will be initially selected during the first iteration of the algorithm. A rule will then be created from this initial rule, which will for example erase a value that occurs frequently for a field. The created rule will therefore be the one that achieves the best compression for packets with this value, since it erases it, while the initial rule does nothing. The compression potential of the rule is then calculated by taking into account the erasure carried out for this value. The initial rule, which does nothing,may be selected later because it has a certain compression potential for packets that do not have the value erased by the created rule. According to another embodiment, which may be implemented alternatively or cumulatively with the previous modes, the calculation of a compression potential for a rule is carried out by adding the field compression potentials for the remaining sections of the identified subset. Thanks to this embodiment, all the compression potentials remaining to be exploited are taken into account since it is their sum which indicates which rule will be the most interesting to select. According to another embodiment, which may be implemented alternatively or cumulatively with the previous modes,the calculation of a compression potential for a rule is carried out by retaining the maximum of the field compression potentials for the remaining sections of the identified subset. In this mode, rather than carrying out an addition of the field compression potentials, the rule which presents a maximum is retained. This calculation variant can be more efficient in calculation operation, and faster, while giving good guidance to the obtaining algorithm. According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes,the step of selecting a rule belonging to the set of compression rules is carried out by randomly drawing a rule from the set of compression rules. This embodiment provides an alternative to the embodiment which consists of calculating a compression potential for each rule. The advantage of this embodiment is to propose an alternative which is simpler to carry out. According to another embodiment, which can be implemented alternatively or cumulatively with the previous embodiments, the step of creating a new rule from the selected rule is carried out by creating one or more candidate rules and by retaining a rule from among the candidate rules created according to the effectiveness of the compression carried out by this rule on packets of the training set. Thanks to this last embodiment,the obtaining is indeed guided by a reward function linked to the compression carried out by the set of compression rules which is being obtained. A rule is selected, from which one or more candidate rules will be created, and it is indeed the rule which carries out the most effective compression which is added to the set of compression rules. One possibility is to apply the already existing compression rules to the packets of the so-called training set before evaluating the compression carried out. This makes it possible to advance the obtaining of the set of compression rules by being guided by a reward for improving the compression, and by avoiding falling back on the creation of the same rules by applying the compressions of the selected rule before evaluating the compression of the candidate rules. According to another embodiment, which can be implemented alternatively or cumulatively with the preceding modes,the step of creating a new rule from the selected rule is performed by choosing a section according to its field compression potential and creating candidate rules formed by adding to the selected rule a compression operation applying to the values ​​taken by the section. Thanks to this embodiment, an obtaining algorithm is defined, which makes it possible to create compression rules from a selected rule while being guided by the compression potentials. Once the rule is selected, one or more candidate rules are created,first by selecting a section according to its field compression potential and then by adding to the selected rule a compression operation that applies to the values ​​taken by the section in question. A compression operation consists of erasing a complete value or a portion of a section when the section takes a value specified in the operation. The erased value may be replaced by a dictionary index. Knowledge of the applied compression rule may make it possible to reconstruct the value(s) (or portions of values) erased by the operation(s) included in the applied compression rule. The most effective rule among the candidate rules will be retained. According to another embodiment, which may be implemented alternatively or cumulatively with the previous modes, a compression operation added to the selected rule is a value erasure operation,the value being chosen according to the value compression potentials of the values ​​taken by the selected section. Thanks to this embodiment, a first candidate rule is created, which adds a very efficient compression operation, namely the erasure of a value. The value is chosen thanks to the value compression potentials, which we have seen take into account the potentially variable length of the values ​​taken by the section. According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes, a compression operation added to the selected rule is an operation of replacing a value taken by the selected section by an index of a dictionary in which, if the selected rule already includes, for the selected section, an operation of replacing the value by an index of a dictionary,the value with the highest value compression potential is added to the dictionary; otherwise, a dictionary is created comprising the two values ​​with the highest value compression potential. With this embodiment, another kind of candidate rule is created, or improved, which consists of replacing a value with a dictionary index. While the operation of erasing a value is effective in a context where a section takes a given value very frequently, the operation of replacing with a dictionary index may be more effective for values ​​whose frequency of occurrence is more distributed. According to another embodiment, which may be implemented alternatively or cumulatively with the previous embodiments, a compression operation added to the selected rule is a prefix erasure operation up to a rank ^^,the rank ^^ being determined by multiplying the field bit compression potentials of the selected section together starting from rank 0 to the maximum rank, until the product is less than a given threshold, for example as soon as the product becomes strictly less than 1. Thanks to this embodiment, another kind of candidate rule is created which consists of erasing the prefixes of the values ​​taken by a given section. The field bit compression potentials make it possible to detect the prefixes common to many values. Thanks to all of these embodiments, an obtaining algorithm is defined, which makes it possible to create compression rules from a selected rule while being guided by the compression potentials. The algorithm can create candidate rules at each step,each rule can correspond to a compression operation among the main operations defined by the SCHC protocol. To create the candidate rules, the obtaining algorithm begins by selecting the section with the strongest field compression potential. The candidate rules created will apply to one or more values ​​in this section. A first candidate rule consists of adding a value deletion operation to the selected rule. The deleted value is simply the one with the highest value compression potential, which is not necessarily the most frequent value in the case of a variable-length section. A second rule consists of adding an operation to replace a value with a dictionary index to the selected rule. If the selected rule already includes a dictionary index replacement operation for this section,the modification will consist of adding a new reference to the dictionary for the value with the highest compression potential. Otherwise, the modification will create a replace-by-dictionary-index operation; and since this operation only makes sense for at least two values, the dictionary created will include the two values ​​with the highest value compression potentials. Finally, a third rule consists of adding a bit prefix erasure operation. To define the rule, it is important to decide up to which rank to erase the bit prefix. For this, we use the field bit compression potentials. If a bit compression potential is 1 for a given rank, this means that the values ​​for this rank all have the same bit 0 or 1. Starting from the first rank, and multiplying the bit compression potentials with each other,we will decrease and the algorithm consists of choosing to stop when a given threshold is crossed by the product. If the values ​​for the first two ranks, for example, all have the same bit, the product of the bit compression potentials will be 1. If the next rank has a frequency of 0.5 for bits 0 and 1, the bit compression potential for this rank is zero, and the product will cancel. The passage of the product under a threshold is therefore obvious, and the created rule will indeed erase the prefix for the first two ranks and not for the third. By choosing that the product must remain at least equal to one, the algorithm will only create candidate prefix erasure rules for prefixes that take the same value expressed in sequences of bits up to the rank for which the product is less than 1. The advantage of this last embodiment, compared to an embodiment where, for example, the candidate rules would be created at random,is to ensure that the compression potentials guide the creation of new rules. In this way, the set of compression rules will be of limited size because the most effective compression rules will be created first to be added to the set of compression rules. According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes, a predefined order is applied allowing to choose the candidate rule to be added in case of equality between the compressions carried out by candidate rules. According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes, the predefined order allowing to choose the candidate rule consists of favoring the candidate rule carrying out a value deletion, then the candidate rule carrying out a replacement by a dictionary index,then the candidate rule performing a prefix deletion. Thanks to these embodiments, a predefined order makes it possible to ensure that a choice will be made between the candidate rules in the event of equality of the compression carried out by these rules. According to another embodiment, which can be implemented alternatively or cumulatively with the previous modes, the candidate rules are created by randomly adding or modifying a compression operation to the selected rule. Thanks to this mode, a variant of the algorithm is proposed which does not use guidance by compression potentials. An obtaining algorithm can also operate using random state changes, and it is the reward function which will make it possible to ensure that the obtaining progresses towards an acceptable result. The advantage of this embodiment is to propose a variant which is simpler to implement. According to another embodiment,which can be implemented alternatively or cumulatively with the previous modes, the so-called learning set is separated into several subsets of data packets, the packets grouped in a subset all having the same header format corresponding to a stack of protocols, and characterized in that the obtaining of a set of compression rules is applied separately to the subsets of the so-called learning set before grouping the sets of rules obtained for the subsets into a single set of rules. Thanks to this embodiment, the obtaining algorithm can be used on a so-called learning set formed of several distinct subsets, each corresponding to packets having a given header format, this format itself corresponding to a given stack of protocols. The obtaining is then done for a given header format,then the different sets of compression rules are combined into one. This provides an effective means of covering a deployment context of compression rules applying to packets of several protocols. According to another embodiment, which can be implemented alternatively or cumulatively with the previous embodiments, the compression rules of the set of compression rules obtained and the compression operations included in said compression rules are rules and compression operations as defined in the SCHC protocol. Thanks to this embodiment, the objective of proposing a technique for automatic learning of compression rules for the SCHC protocol is well met. The technical problem of having a means of deploying the SCHC protocol without requesting an expert to define the compression rules is well resolved. According to another functional aspect,the invention relates to a method for managing the transmission of a data packet in a network, the packet comprising sections taking respective values, characterized in that it comprises the following steps: • first obtaining a set of compression rules comprising compression operations which can be applied to the values ​​taken by the sections of said data packet; • applying at least one compression operation to a section value of said data packet,said operation being included in a compression rule belonging to said set of compression rules obtained; • the transmission of said data packet and of a data item representative of the compression rule to which said operation belongs; • the reception of said data packet and of said data item representative; • the application to said data packet of at least one symmetrical decompression operation of the at least one compression operation applied to a section value of said data packet. Thanks to this aspect of the invention, a complete transmission of the data packet is carried out, by transmitting the compressed data packet. Obtaining the compression rules makes it possible to make the process automatic, without expert intervention. The transmission of the data item representative of the compression rule which has been applied to the transmitted packet makes it possible to find this rule and therefore the compression operations included in the rule,and to restore the original packet by applying the symmetric decompression operations. A compressed packet was therefore transmitted instead of the original packet, which allows very significant savings in terms of hardware and energy resources. Since the compression rules are obtained automatically, there is no need to involve an expert, which makes it very easy to deploy the efficient transmission according to the invention. According to one embodiment, which can be implemented alternatively or cumulatively with the previous mode, the method further comprises a preliminary step of collecting transmitted packets in the network to form a so-called learning set used during the step of obtaining a set of compression rules and a step of synchronized deployment in the nodes of the network of the set of compression rules obtained. Thanks to this embodiment,the transmission of packets in the network is done by taking into account the packets actually transmitted. The preliminary obtaining of the set of compression rules generally uses a so-called learning set. This can be provided by an expert, or generated automatically according to predefined constraints. But the preferred embodiment is the one where the so-called learning set is collected among the packets transmitted in the network. The obtaining is then done with a so-called learning set directly representative of the packet traffic in the network and the compression rules obtained will therefore be much more adapted. Once the set of compression rules is obtained, it must be deployed in the nodes of the network in a synchronized manner so that a node which receives a compressed packet, and the data representative of the compression rule used, can find this rule and apply the symmetric decompression operation. In addition,thanks to this embodiment, the method can be implemented permanently. The collection of data packets used to form the so-called training set is permanent and makes it possible to capture context changes in the ongoing transmission of packets. A first set of compression rules can already be deployed and, if the context changes, for example if the IP addresses of the network nodes change, the compression rules become ineffective. But the collected packets make it possible to restart the obtaining of the compression rules with a new so-called training set, and the deployment of a new set of compression rules then makes it possible to adapt to the new context,and to perform efficient compressions again. The deployment must be synchronized to ensure that the node receiving a compressed packet is aware of the compression rule that has been applied in order to be able to perform the symmetric decompression operation to restore the data packet. According to another embodiment, which may be implemented alternatively or cumulatively with the previous embodiments, the compression rules of the set of compression rules obtained, the compression operations included in said compression rules, and the symmetric decompression operations are compression and decompression rules and operations as defined in the SCHC protocol. Thanks to this embodiment,the SCHC protocol can be deployed in dynamic contexts. The sets of compression rules will be updated as they are obtained using new so-called learning sets representative of the traffic between the different points of a network, and changes in the headers of packets actually circulating. The SCHC protocol is therefore made suitable for application to dynamic contexts thanks to the use of reinforcement learning to create the sets of compression rules. According to a first material aspect, the invention relates to an entity for managing the obtaining of a set of compression rules suitable for compressing a data packet transmitted in a network, the packet comprising sections taking respective values ​​expressed in the form of a sequence of bits, the packet being suitable for being compressed by applying compression rules suitable for removing bits from a value taken by a section of the packet,characterized in that the management entity comprises the following modules: •a module for obtaining a compression potential for a given section, relative to the values ​​taken by the given section in the packets belonging to a set of data packets, a so-called learning set; •a module for obtaining the set of compression rules as a function of the compression potentials obtained. According to another material aspect, the invention relates to a data packet formed by concatenating data representative of a compression rule, said data being an identifier of the compression rule consisting of a sequence of bits, with a data packet to which at least one compression operation belonging to said compression rule which belongs to a set of compression rules obtained by the method has been applied. According to another material aspect,the invention relates to network equipment comprising an entity for managing the obtaining of a set of compression rules according to the invention. According to another material aspect, the invention relates to a system for managing the transmission of a data packet in a network, the packet comprising sections taking respective values, characterized in that the system comprises the following modules: • a module for previously obtaining a set of compression rules comprising compression operations which can be applied to the values ​​taken by the sections of a data packet; • a module for applying at least one compression operation to a section value of a data packet,said operation being included in a compression rule belonging to a set of compression rules obtained; • a module for transmitting a data packet to which a compression operation has been applied and data representative of a compression rule to which said compression operation belongs; • a module for receiving a data packet and data representative of a compression rule; • a module for applying to a data packet a symmetrical decompression operation of a compression operation applied to a section value of said data packet. According to one embodiment,the transmission management system further comprises: • a module for collecting transmitted data packets in the network to form a so-called learning set used by a module for obtaining a set of compression rules; • a module for synchronised deployment in the nodes of the network of a set of compression rules obtained. Finally, according to another material aspect, the invention relates to data media on which computer programs are recorded comprising sequences of instructions for implementing the methods defined above. The data media may be any entity or device capable of storing the programs. For example, the media may comprise a storage means, such as a ROM, for example a CD ROM or a microelectronic circuit ROM, or a magnetic recording means such as a hard disk. Furthermore,the media may be transmissible media such as an electrical or optical signal, which may be conveyed via an electrical or optical cable, by radio or by other means. The programs according to the invention may in particular be downloaded from a network such as the Internet. Alternatively, the information medium may be an integrated circuit in which the program is incorporated, the circuit being adapted to execute or to be used in the execution of the method in question. Brief description of the figures The invention will be better understood from the following description, given by way of example,and made with reference to the appended drawings in which: [Fig 1] represents an entity for obtaining a set of compression rules as well as an entity for managing the compression of a data packet transmitted in a network and an entity for managing the decompression of a data packet. [Fig 2] represents an example of obtaining a set of compression rules by implementing a learning algorithm. Figure 2 is subdivided into four sub-figures 2.A, 2.B, 2.C and 2.D, corresponding to four learning steps. [Fig 3] represents an example of calculating field compression potentials explaining the benefit of using a corrected field compression potential. [Fig 4] represents an example of calculating value compression potentials explaining the difference between a compression potential of a value and its frequency of occurrence. [Fig 5] represents an example of a data packet transmission system according to the invention,showing the deployment of such a system in a network comprising a gateway and two terminals. [Fig 6] represents another example of a data packet transmission system according to the invention, showing the deployment of such a system in a network comprising two routers. Detailed descriptionFigure 1 represents an entity 300 for obtaining a set RS of compression rules as well as an entity 100 for managing the compression of a data packet P transmitted in a network NET and an entity 200 for managing the decompression of a data packet P. The entity 300 for obtaining a set RS of compression rules comprises a module 301 for obtaining a compression potential PC relative to the values ​​taken by the sections of data packets belonging to a set E of data packets,so-called learning set as well as an APP module for obtaining the set RS of compression rules as a function of the PC compression potentials obtained. In exemplary embodiments, the APP module implements a learning algorithm in order to obtain the set RS of compression rules. The examples presented in Figure 1 and the following figures correspond to these exemplary embodiments in which a learning algorithm is implemented. In these examples, the learning algorithm uses a learning set which is the so-called learning set. In addition to the obtaining entity 300,Figure 1 describes two entities for managing the compression 100 and the decompression 200 of a data packet P in order to illustrate the use of the set RS of compression rules obtained by the implementation of the method by the obtaining entity 300. The two entities for managing the compression 100 and the decompression 200 belong to the same network NET which is a communication network using protocols for transmitting data packets P. In the exemplary embodiment described in Figure 1, the obtaining entity 300 belongs to the same network NET but in other exemplary embodiments,the obtaining entity 300 may belong to different networks or even be isolated from the network NET. The set RS of compression rules obtained by the obtaining entity 300 may be deployed in the compression 100 and decompression 200 management entities by any means. The data packet P comprises sections taking respective values. The two compression 100 and decompression 200 management entities have the same set of compression rules RS, this set comprising compression rules which themselves comprise compression operations which apply to values ​​taken by the sections of a data packet P. The set RS of compression rules was obtained by the implementation of an obtaining algorithm by the entity 300. The compression management entity 100 comprises a module 101 for applying at least one compression operation to a section value of a data packet P,said operation being included in a compression rule belonging to a set of compression rules RS, said set RS being obtained by learning.The compression management entity 100 comprises a module 102 for transmitting a data packet P to which at least one compression operation has been applied and a data item representative of the compression rule to which said operation belongs. The decompression management entity 200 comprises a module 201 for receiving a data packet P to which at least one compression operation has been applied to a section value of said packet P, said operation being included in a compression rule belonging to a set of compression rules RS, said set RS being obtained by learning,and a data item representing the compression rule to which said operation belongs. The decompression management entity 200 comprises a module 202 for applying to a data packet P at least one symmetrical decompression operation of a compression operation applied to a section value of said packet P. The transmission of the data packet P by the module 102, and the reception of the same packet P by the module 201, is represented by an arrow connecting the module 102 to the module 201, labeled by the packet P. The transmission and reception of the data item representing the compression rule applied to the packet P are not represented in FIG. 1. In certain embodiments, the data packet P and the representative data item are transmitted and received jointly. As the compression management entity 100 and the decompression management entity 200 have the same set of compression rules RS obtained,the decompression management entity 200 will be able to use the data representative of the compression rule applied to the packet P to find it. It is then easy to determine the symmetrical decompression operation. The module 202 for applying a symmetrical decompression operation then makes it possible to restore the data packet P as it was before the application of a compression operation. The data packet P has therefore been transmitted in the network after application to the values ​​taken by the sections of the packet P of one or more compression operations by the compression management entity 100. The transmission of the packet P once compressed makes it possible to achieve significant savings in energy and material resources. The packet P is then restored by the decompression management entity 200 for later use. As the set of compression rules RS is obtained by learning,the method does not require expert intervention, and it can adapt to changing contexts by iterating the learning algorithm to have sets of compression rules RS adapted to the context. Figure 2, for its part, represents an example of execution of an embodiment of obtaining a set of compression rules according to an embodiment of the method of the invention. In this example, a learning algorithm is used. Figure 2 is subdivided into four sub-figures (2.A, 2.B, 2.C and 2.D) which correspond to four steps S0, S1, S2 and S2' of the learning according to this embodiment. Figure 2.A corresponds to the initial state S0 of the learning and to the first step. The learning set E consists of N=10000 packets. These N=10000 packets contain a header formed, in our example, of four fields C1, C2, C3, C4. In our example, each of the fields C1, C2, C3, C4 has a length of 8 bits,indicated by the mention L=8. For the first field C1, the N=10000 packets of the training set E all have the same value ^^1, 1 unspecified. The frequency ^^1 1 of appearance of the value ^^1 1 is therefore 100%. For the second field C2, the N=10000 packets of the training set E have three values ​​^^2 1 , ^^2 2 and ^^2 3 which are again unspecified. Only the frequencies ^^2 2 and ^^2 3 of these three values ​​are specified. They are respectively 80%, 10% and 10%. For the third field C3, the N=10000 packets of the training set have four values ​​^^3 1 , ^^3 2 , ^^3 3 and ^^3 4that appear in the training set with the same frequency of 25%. The values ​​are specified as a sequence of 8 bits, and we see that they are differentiated by the prefix. For the fourth field C4, the N=10000 packets in the training set have four ^^4 values 1 , ^^4 2 , ^^4 3 and ^^4 4which also appear in the training set with the same frequency of 25%. The values ​​are specified as a sequence of 8 bits, but here, unlike the values ​​in field C4, these four values ​​have an identical prefix on the first 6 bits. This example is of course fictitious, and chosen to illustrate the operation of the compression rule learning algorithm. The different frequencies of appearance and values ​​taken will make it possible to illustrate the different compression operations that can be added to the compression rules during training according to one embodiment.In the example presented here, the compression will only seek to compress the values ​​taken by the fields C1, C2, C3 and C4 of the headers of the data packets and not the delimited sections of the packet payloads, but the principles would remain the same in the case where one would seek to learn compression rules that apply to sections of the packet payloads. It is assumed that the frequencies of appearance of the different values ​​are independent of each other. For example, it is assumed that in the 8000 packets of the training set E for which the field C2 takes the value ^^2. 1 , the four values ​​of fields C3 and C4 appear equiprobably, as in the complete training set E. These values ​​therefore appear respectively each in 2000 packets of the 8000 packets where field C2 takes the value ^^2 1. This property is assumed to be true also when combining the different field values. For example, in the 2000 packets for which both the C2 field takes the value ^^2 1 and field C3 takes the value ^^3 1, then the frequencies of occurrence of the values ​​of the field C4 are also equiprobable, and we therefore find 500 packets for each of these four values. This assumption will allow us to simplify the presentation of an embodiment of the algorithm without losing generality. The frequencies of occurrence are presented as percentages in Figure 2.A and following, but, for the calculations to follow, the frequencies are strictly positive quantities, less than or equal to 1. A percentage of 100% corresponds to a quantity of 1; a percentage of 80% to 0.8; a percentage of 25% to 0.25 and a percentage of 10% to 0.1. It is possible to calculate field compression potentials PCC for the four fields C1, C2, C3 and C4.According to one embodiment of the invention, the field compression potential PCC is calculated, for a given section, according to the formula ^^( ^^ − ^^), in which N is the number of times that the section appears in the training set; L is the length of the section expressed in bits and H is a quantity called the field entropy estimator which is calculated according to the formula ^^ = − ∑^^∈[1, ^^]^^. ^^ ^^ ^^ ^^2( ^^ ^^ ) in which K is the number of different values ​​^^ takes the section in the training set ^^ ^^ is the frequency of occurrence of the value ^^ ^^ . In this embodiment and in this example, we use the formula in which the section, here one of the four fields C1, C2, C3 or C4, is of fixed length. This is indeed the case in this example, with a length L equal to 8 bits for the four fields C1, C2, C3, C4. For the field C1, the calculation therefore gives the following result: ^^ ^^ ^^( ^^1) = 10000 ∗ (8 − (− ^^1 1 log2( ^^1 1 )) = 10000 ∗ (8 − (−1 ∗ 0)) = 80000 For the field C2, the calculation therefore gives the following result: ^^ ^^ ^^( ^^2) = 10000 ∗ (8 − (− ^^2 1 log2( ^^2 1 ) − ^^2 2 log2( ^^2 2 ) − ^^2 3 log2( ^^2 3 ))) = 10000 ∗ (8 − (0.8 ∗ 0.3219 + 0.1 ∗ 3.322 + 0.1 ∗ 3.322)) = 10000 ∗ (8 − 0.92192) = 70781 For fields C3 and C4, the calculation gives the same result because the frequencies of occurrence of the values ​​are the same in both fields. The result is given below: ^^ ^^ ^^( ^^3) = 10000 ∗ (8 − (− ^^3 1 log2( ^^3 1) − ^^3 2 log2( ^^3 2 ) − ^^3 3 log2( ^^3 3 ) − ^^3 4 log2( ^^3 4 ))) = 10000 ∗ (8 − ( 4 ∗ 0.25 ∗ 2 ) ) = 60000 = ^^ ^^ ^^ ( ^^4 )At step S0, in the embodiment presented here, the compression rule set RS is initialized with a single rule R0. This rule is a rule that matches all possible values ​​of all fields of the packets in the training set E, but does not apply any compression to these values. These elements correspond to the initial state of the learning algorithm. We will now detail the first step of the reinforcement learning algorithm for the compression rule set RS, for one embodiment among several. This first step is also shown in Figure 2.A. The reinforcement learning algorithm for the compression rule set RS begins by selecting a rule from the set RS. Since the set RS includes only rule R0 at this stage, it is this rule R0 that will be selected.We will see in the later steps how the selection can be performed when there are several rules in the RS compression rule set. From rule R0, several candidate rules will be created in the example embodiment presented here. The first step to create the candidate rules consists in our example of choosing a field among the fields C1, C2, C3, C4. For this, the embodiment of the learning algorithm uses the field compression potentials PCC(C1), PCCC(C2), PCC(C3) and PCC(C4). In general, the algorithm will here choose a section using the field compression potentials for the set of sections whose values ​​are to be compressed. This calculation of the field compression potentials PCC must take into account the compressions carried out by the compression rules present in the RS compression rule set.For this, a possible embodiment is to partition the training set E according to the compression rules of the set RS, and to calculate the field compression potentials PCC for each rule of the set RS using the subsets of the training set E assigned to each rule. At this initial stage, only the rule R0, which does not perform any compression, is present. The calculation of the field compression potentials PCC is therefore the one presented above. Since the field compression potential PCC(C1) is the largest among the four potentials PCC(C1), PCC(C2), PCC(C3) and PCC(C4), it is this field C1 that will be chosen to create the candidate rules from the selected rule R0.In our exemplary embodiment, three candidate rules will be created, corresponding to the addition to the selected rule R0 of three possible compression operations on the values ​​taken by the field C1 in the packets of the training set E. The first candidate rule created consists of the addition of an erasure operation. To select the value to be erased, in our exemplary embodiment, PCV value compression potentials are. calculated. In this case, as field C1 takes a single value in the training set E, it is of course this value that will be deleted. The second candidate rule created consists of adding a replacement operation by an index of a dictionary. In this case, as there is only one value taken by field C1, the added operation consists replace this unique value ^^1 1by a dictionary index. Since the dictionary in question would be used for a single value, it would only include a single bit. The third candidate rule created consists of adding an operation to erase the prefixes common to the values ​​taken by the field. Here, since there is only one value taken by the field C1, the added operation consists of erasing the value itself, since all the prefix bits are obviously common for this single value. The next step of the learning algorithm will consist of retaining a rule from the candidate rules created to add it to the set of compression rules RS. For this, the reward function used in our implementation is the compression performed by the candidate rules on the packets of the training set E. The first candidate rule would perform the erasure of the value ^^1 1taken by field C1. As this field has a length of 8 bits, and the value ^^1 1 appears in the packets from the training set E (its frequency of occurrence ^^1 1 is 100%), the compression achieved by this candidate rule is 8*10000, or 80000 bits. The second candidate rule would achieve the replacement of the value ^^1 1 taken by field C1 by an index of a dictionary. Since this index is one bit long, the compression achieved by this rule for a packet would not be 8 bits, but only 7 bits. Multiplying by the 10,000 packets in the training set, the compression achieved by this candidate rule is 70,000 bits. The third candidate rule would perform the erasure of the common prefix for the values ​​taken by the field C1. Since the field C1, in the training set E, only takes the value ^^11, then this entire value would be erased by the common prefix erasure operation. This third candidate rule therefore achieves the same compression as the first candidate rule, namely 80,000 bits. In our embodiment, the chosen candidate rule is the one that achieves the best compression. In case of equality, a selection criterion can be defined.In our embodiment, the deletion operation is favored over the index replacement operation, which itself is favored over the common prefix deletion operation. This is therefore this compression rule R1, noted ^^1: [ ^^ ^^( ^^1. 1 )], which will be added to the RS set of compression rules. The notation ^^ uses the acronym ns for not sent to indicate a value not transmitted. A compression rule is noted in our figures as a sequence of operations placed between brackets. At this stage of the learning algorithm, the set of compression rules RS therefore includes two rules, the initial rule R0 and the rule R1 which has just been added to it. In the embodiment presented here, when an added rule applies to all the data packets in the training set to which the selected rule applies, then the selected rule is removed from the set of compression rules. This is indeed the case here: the initial rule R0, which does not perform any compression, applies to all the packets in the training set E and this is also the case for rule R1 which erases the value ^^1 1, since this value is taken by 100% of the fields C1 of the packets of the training set E. The initial rule R0 is therefore removed from the set RS, which is represented in Figure 2.A by the crossed-out box placed on it. We will discuss later the stopping criteria of reinforcement learning in our embodiment, and we assume that the learning algorithm will continue well. We will therefore now see the execution of the next step S1 of reinforcement learning in our embodiment, represented by subfigure 2.B. Figure 2.B corresponds to the state S1 of the learning, after the first step, and to the second step of reinforcement learning according to one embodiment. The training set E is not modified, and still includes 10000 packets, which include 4 fields C1, C2, C3, C4 of a length L of 8 bits each.However, the RS compression rule set being trained has been modified and now includes the R1 rule for erasing the ^^1 value. 1 , rule noted ^^1: [ ^^ ^^( ^^1 1 )]. This has an influence on the calculation of the PCC field compression potentials in our embodiment, and is represented in Figure 2.B by the square box crossing out the entire C1 field in the training set E. The calculation of the PCC field compression potentials uses the formula ^^( ^^ − ^^), in which N is the number of times the field appears in the training set. To take into account the modification of the set of compression rules, it is sufficient, in an exemplary embodiment, to apply the compression rules of the RS set to the training set E. Here, the application of the R1 rule will erase all occurrences of the C1 field, since this C1 field takes a unique value ^^1 1erased by the compression rule R1. Therefore, the calculation of the field compression potential PCC(C1) taking into account the addition of rule R1 to the set of compression rules RS will use a quantity N=0. We therefore have PCC(C1) = 0 in step S1. It is easy to understand that, since the unique value taken by the field C1 is erased by rule R1 for all the packets in the training set E, this field no longer has any compression potential. This intuition is well captured by the calculation of the field compression potential according to the embodiment presented here. We see that the formula for calculating a field compression potential PCC can be applied to several contexts and in particular here take into account the evolution of the set of compression rules RS during the successive training steps.The other field compression potential calculations PCC(C2), PCC(C3) and PCC(C4) are unchanged from the calculations performed in the first step S0, since rule R1 does not perform any operation on the values ​​taken by fields C2, C3 and C4. We will see in the description of the following steps how field compression potential calculations PCC taking into account the compression rules belonging to the set RS allow us to select a rule from the set RS of compression rules in order to create candidate rules. Since the set RS only includes one rule R1 at this step S1, it is this rule R1 that is selected in our example. As seen previously, once rule R1 is selected, a field must be selected to create one or more candidate rules. In our embodiment, it is the field that has the highest PCC compression potential that is chosen, and it is therefore field C2.As seen previously, three candidate rules will be created, adding to rule R1 a compression operation on the selected field C2. The first will be a value deletion operation, this value being chosen according to the value compression potentials. Since the field C2 is of fixed length, the value compression potentials give the same rankings as the frequency of occurrence. The value will be the one that appears most frequently and therefore will be the value ^^2. 1 which appears with a frequency of 80%. The second candidate rule will consist of adding a dictionary operation. Since there is no dictionary operation for field C2, it is therefore a creation that will replace two values ​​taken by field C2, namely the value ^^2 1 , which has the highest value compression potential and one of the two ^^2 values 2 and ^^2 3, whose value compression potentials are equal. Let us assume that the value ^^2 2 randomly selected. The third candidate rule consists of adding a prefix operation, and it is assumed that the values ​​taken by the C2 field are similar to those taken by the C3 field, and therefore do not have a common prefix. The next step of the learning algorithm will consist of retaining a rule from the created candidate rules to add it to the set of compression rules RS. For this, the reward function used in our implementation is the compression performed by the candidate rules on the packets of the training set E. The first candidate rule performs the erasure of the value which has a length of 8 bits, and which appears in 8000 packets of the training set E. The compression achieved by this candidate rule is therefore 64000 bits. The second candidate rule achieves the replacement of the ^^2 values 1 and ^^2 2 by an index of a dictionary, which must therefore use at least one bit. The second candidate rule therefore replaces a value of a length of 8 bits by an index of one bit. It therefore performs a compression of 7 bits for each packet where it is applied. In this case, this second rule is applied for 9000 packets, those for which the value of the C2 field is taken by ^^2 1 or ^^2 2. The compression achieved is therefore 7*9000 = 63000 bits. The first candidate rule therefore achieves better compression than the second. We assume that the third candidate rule is not effective because the values ​​taken by the C2 field do not have a common prefix. It is therefore the first candidate rule that will be retained and added to the set of compression rules RS, giving the rule R2. This is noted in figure 2.B as follows: ^^2: [ ^^ ^^( ^^1 1 ) + ^^ ^^( ^^2 1 )], in order to show that the operation of erasing the value ^^2 1 , noted ^^ ^^ ( ^^2 1) , is added to the operation of clearing the value ^^1 1 which was already present in R1. We recall that rule R2 was created by selecting rule R1 and adding a compression operation to it, here erasing the value ^^2 1. In this step S1, unlike step S0, the rule R2 that has just been added to the set RS of compression rules does not apply to all the packets in the training set E to which rule R1 applies. Indeed, rule R1 applies to all the packets in the training set E, while rule R2 only applies to the 8000 packets in the training set E in which the field C2 takes the value ^^2 1. For this reason, in the embodiment presented here, rule R1 is not deleted from the set RS of compression rules which will now include both rules R1 and R2. In the embodiment presented in this example, the reinforcement learning algorithm will use a partition of the training set E by assigning subsets of the training set E to the compression rules of the set RS. As long as there was only one rule R0 then R1 in the set RS, this was not important. Here, we will assign the 8000 packets of the training set E for which rule R2 applies to it, and create a dedicated subset, and assign the remainder of 2000 packets to rule R1.This assignment of packets from the training set E to rules from the RS set is done in our implementation example at the time of adding a new rule to the RS set of compression rules, when deciding whether or not to delete the selected rule from the RS set. Since the created rule is created from the selected rule by adding a compression operation to it, the created rule will necessarily apply to a subset of the packets to which the selected rule applies and is assigned. If the created rule applies to all packets to which the selected rule applied, the learning algorithm will assign all packets of the selected rule to the created rule, and it is not necessary to keep the selected rule in the RS set of compression rules.If the created rule does not apply to all the packets of the selected rule, the created rule is assigned the packets to which it applies and the created rule is kept in the set RS of compression rules and assigned the residue of packets to which the created rule does not apply. This partition will allow us to refine the calculation of the field compression potentials as we will see in the next steps. We assume here again that a stopping criterion is not satisfied and will present the next step of the reinforcement learning algorithm. Figure 2.C corresponds to the state S2 of the learning, as obtained after the second step, and to the third step of reinforcement learning according to one embodiment. At this stage S2, the set RS of compression rules includes two rules R1 and R2.To make a selection among these two rules in order to create candidate rules, a possible embodiment uses the calculation of field compression potentials PCC according to a partition of the training set E into subsets assigned to the rules R1 and R2. We have seen previously, during the description of the end of the second step, how to carry out this partition. In our embodiment, 8000 packets of the training set E have been assigned to the rule R2, those for which the rule R2 applies, and the residue of 2000 packets is assigned to the rule R1. The field compression potentials PCC(C1), PCC(C2), PCC(C3) and PCC(C4) are then calculated for the two subsets realizing the partition of the training set E, by applying to these packets the compression rule that has been assigned to them.For field C1, whether in the 2000 packets assigned to rule R1 or the 8000 packets assigned to rule R2, the respective application of rules R1 and R2 will clear the value ^^1. 1 taken by field C1. Field C1 therefore does not appear in any packet of the two subsets, and The magnitude N used in the formula for calculating the field compression potential PCC(C1) is therefore zero in both cases. In other words, whether for packets assigned to rule R1 or for those assigned to rule R2, the field compression potential PCC of field C1 is zero. This is easy to understand, since these two rules R1 and R2 erase the unique value ^^1 1taken by the field C1. For the field C2, it is necessary to make a differentiated calculation of the field compression potential PCC(C2) for the two subsets attributed respectively to the rule R1 and to the rule R2. For the rule R1, the packets of the training set E which are attributed to it are those to which the rule R2 does not apply. These are therefore the 2000 packets in which the field C2 does not take the value ^^2 1 but, equiprobably takes one of the two values ​​^^2 2 or ^^2 3 . In these 2000 packets, the field C2 appears well, and the quantity N is therefore worth 2000 here. The formula for calculating the field compression potential PCC(C2) is therefore: ^^ ^^ ^^( ^^2) = ^^ ∗ ( ^^ − (− ^^2 2 log2( ^^2 2 ) − ^^2 3 log2( ^^2 3))) = 2000 ∗ (8 − (−0.5(−1) − 0.5(−1))) = 2000 ∗ 7 = 14000 For rule R2, however, the 8000 packets of the training set E assigned to it are those to which rule R2 applies, a rule that includes the compression operation consisting of erasing the value ^^2 1. The field C2 is therefore completely erased in the subset of packets attributed to rule R2. In other words, the field C2 does not appear in this subset and here we have a zero magnitude N and therefore a zero field compression potential PCC(C2). The two values ​​for the field compression potential PCC(C2) are present in figure 2.C, namely 14000 for the subset attributed to rule R1 and 0 for the subset attributed to rule R2. For the calculation of the field compression potentials PCC(C3) and PCC(C4), we saw above that we made the assumption that the frequencies of appearance of the different values ​​taken by the fields C3 and C4 are the same in the two sub-rules R1 and R2. For four values equiprobable, the magnitude ^^( ^^) = − ∑^^∈[1, ^^]^^ ^^ ^^ ^^ ^^2( ^^ ^^) is −(4 ∗ (0.25 ∗ (−2)), or 2. In other words, for rule R1, which has been assigned to 2000 packets, the field compression potentials PCC(C3) and PCC(C4) are 2000*(8-2), or 12000; whereas for rule R2, which has been assigned to 8000 packets, the field compression potentials PCC(C3) and PCC(C4) are 8000*(8-2), or 48000. These two values ​​are well presented in Figure 2.C. All of these field compression potentials are used to determine the selected rule. One possible embodiment is to add the PCC field compression potentials for a given rule. In this case, rule R1 would be assigned a PCC field compression potential of 0 + 14000 + 12000 + 12000 = 38000 while rule R2 would be assigned a PCC field compression potential of 0 + 0 + 48000 + 48000 = 96000. In this case, rule R2 would be selected.Another possible embodiment is to calculate the PCC field compression potential of a rule by taking the maximum of the PCC field compression potentials for this rule rather than by summing them. This embodiment also leads us to select rule R2. The creation of candidate rules will therefore be done from rule R2. For rule R2, the two fields C3 and C4 have the same field compression potential PCC(C3) = PCC(C4) = 48000 while fields C1 and C2 have a zero PCC field compression potential. In these cases of equality, the selection between fields C3 and C4 to decide which candidate rules will be created can be done randomly or by following another criterion, for example order of occurrence. Let us assume that field C3 is selected.Three candidate rules are then created by starting from rule R2 and adding a compression operation working on the values ​​taken by field C3. We recall that the four values ​​taken by field C3 are equiprobable and do not have common prefixes, as can be seen in Figure 2.C. The candidate rule consisting of the deletion of a common prefix will therefore have no compression efficiency. For the candidate rule which consists of the addition to rule R2 of an operation of erasing a value taken by field C3, as the values ​​are equiprobable and the four values ​​have the same length, because field C3 is of fixed length, they will therefore have the same value compression potentials. Any value can therefore be selected to be deleted.Here again, a method of implementation in case of equality can be to make a random draw or to take the first or the last value occurring or any other method of choice. Similarly, for the candidate rule which consists of the addition to rule R2 of an operation of replacing a value by an index of a dictionary, the potentials of value compression will not guide the choice of the values ​​to be replaced by a dictionary index. Two values ​​will be selected randomly or according to an order criterion. The compression achieved by the erasure operation is calculated for the 8000 packets to which rule R2 applies. Whatever the value erased, it appears in a quarter of the 8000 packets, therefore 2000 packets. The compression achieved by the erasure operation would therefore be 2000 * 8 = 16000 bits. The compression achieved by the operation of replacing by a dictionary index is also calculated for the 8000 packets to which rule R2 applies.Two values ​​are replaced; that is, for half of the packets in question. The compression achieved by the dictionary index replacement operation is therefore 4000 * 7 = 28000 bits. Since the index is 1 bit, the compression for each packet is 7 bits, and not 8 bits as for an erasure. The best compression is that achieved by the index replacement operation. In Figure 2.C, such an erasure operation is noted ^^ ^^ ^^( ^^3. 1 , ^^3 2 ), the term "map" coming from the English mapping. We assume that the random drawing of the two values ​​to be replaced by a dictionary index gave the two values ​​^^3 1 and ^^3 2 . The complete rule R3 therefore comprises three operations, namely the two operations present in R2 to which we add the new operation, and is noted as follows: ^^3: [ ^^ ^^ ( ^^1 1) + ^^ ^^ ( ^^2 1)+ ^^ ^^ ^^( ^^3 1 , ^^3 2)]. Rule R3 is therefore added to the set of compression rules RS. It applies to 4000 packets from the training set E (half of those to which rule R2 applied) which will be assigned to it for the calculation of the field compression potentials PCC in the next step. Rule R2 keeps 4000 packets, those to which compression rule R3 does not apply, and is not removed from the set of compression rules RS. Finally, the 2000 packets assigned to rule R1 remain assigned to it. Figure 2.D corresponds to state S2' of the training. This state corresponds to the state obtained after the second step. We have seen that, in this state, rule R2 is selected to create candidate rules, and that, for this rule, fields C3 and C4 have the same field compression potential, namely PCC(C3) = PCC(C4) = 48000. We have seen in the previous step described in Figure 2.C how the learning algorithm would proceed if the candidate rules constructed from rule R2 sought to compress field C3. We will see here the progress of the learning algorithm according to an example of implementation if field C4 is chosen instead, knowing that these two fields have the same potential for compression of field PCC. In this case, we recall that the four values ​​taken by field C4 are equiprobable, and that the compression achieved by the candidate rules is estimated on the 8000 packets attributed to the selected rule R2. The first candidate rule will be rule R2 to which is added an operation of erasing a value. Knowing that the four values ​​taken by field C4 are equiprobable, the potentials for compression of values ​​will be equal.By choosing any of the four equally probable values, adding an erasure operation would result in an erasure of an 8-bit value appearing in 2000 packets (a quarter of the 8000 packets assigned to R2), i.e. a compression of 16000 bits. The second candidate rule will be rule R2 to which is added a value replacement operation by a dictionary index. As seen in Figure 2.C, the compression achieved by the replacement operation by a dictionary index is calculated for the 8000 packets to which rule R2 applies. Two values ​​are replaced; i.e. for half of the packets in question. The compression achieved by the replacement operation by a dictionary index is therefore 4000 * 7 = 28000 bits. Since the index is 1 bit, the compression for each packet is 7 bits, and not 8 bits as for an erasure.The third candidate rule will be rule R2 to which is added an operation of erasing a common prefix. This candidate rule is an opportunity to illustrate the operation of field bit compression potentials. These potentials are calculated for the 8 ranks numbered from 0 to 7, for the values ​​taken by the C4 field in the relevant packets of the training set. These four values ​​^^4. 1 , ^^4 2 , ^^4 3 and ^^4 4 sont equiprobable and have the same value for the first 6 bits, which are all 1. The frequency of appearance of bit 1 at rank 0 is therefore 1, and conversely, the frequency of appearance of bit 0 at rank 0 is 0. As a logarithm of a zero frequency is not defined, the entropy estimator of a field bit calculated according to the formula ^^ ( ^^ ^^ ) = − ( ^^ ^^ . ^^ ^^ ^^2 ( ^^ ^^ ) + ( 1 − ^^ ^^ ). ^^ ^^ ^^2 ( 1 − ^^ ^^ ) ) is conventionally 0 in this case, and the field bit compression potential for rank 0 is then 1. The field bit compression potentials are also 1 for the first 6 ranks, since the bits of the first six ranks of the values ​​taken by the C4 field are identical in all the packets considered. In this case, the bits are all 1, but the result is the same if, for a given rank, all the bits are 0. For ranks 7 and 8, bits 0 and 1 are equally probable since the four values ​​^^4 1 , ^^4 2 , ^^4 3 and ^^4 4 are also equiprobable. The field bit compression potentials for these ranks 7 and 8 are then: 1 − (− ( 0.5 ∗ log20.5 + ( 1 − 0.5 ) ∗ log2 ( 1 − 0.5 )) ) = 1 − (−(0.5 ∗ ( −1 ) + 0.5 ∗ ( −1 ))) = 1 − 1 = 0 The field bit compression potential actually cancels out for a rank where bits 0 and 1 are equiprobable. Using the method where the field bit compression potentials are multiplied together, starting from the rank 0 bit, and using a threshold, we see how it is possible to detect that packets have common prefixes for certain fields. In this case, in the example presented here, the product of the field bit compression potentials goes from 1 to 0 between rank 6 and rank 7. It is therefore easy to see that the candidate rule created by adding a prefix erasure operation to rule R2 will consist of erasing the first six bits of the values ​​taken by field C4. This rule R3 is noted as follows: ^^3: [ ^^ ^^ ( ^^1 1) + ^^ ^^ ( ^^2 1)+ ^^ ^^ ^^( ^^4,6)], the acronym "msb" meaning most significant bits, that is to say the English term to indicate the prefix bits, C4 indicating that the rule applies to the values ​​taken by the C4 field and 6, that the first 6 bits are concerned. The compression achieved by this candidate rule is the erasure of six bits of the values ​​taken by the C4 field in the 8000 packets allocated to rule R2, since the common 6-bit prefix is ​​present for the four values ​​taken by the C4 field. The compression achieved by this third candidate rule is therefore 8000*6 = 48000 bits and it is indeed this rule R3 which is added to the set of compression rules RS in this step S2', which differs from step S2 in that the creation of a candidate rule is done by targeting the C4 field instead of the C3 field.After step S2' as after step S2, other steps of the learning algorithm of the set of compression rules RS can take place which will add compression operations taking into consideration the remaining compression potentials to finally obtain an efficient compression of the training set E. Regarding the stopping of the learning algorithm, several possibilities exist. The first possibility consists of calculating the compression achieved by a new rule added to the set of compression rules RS and to add it and iterate the learning algorithm again only if the addition of this new rule improves the compression achieved on the packets of the training set.One might think that adding a compression rule to the set of compression rules RS will necessarily improve the compression achieved on the training set E, but it must be taken into account that an identifier of the compression rule used to compress a data packet header must be transmitted at the same time as the compressed packet in order to perform the decompression. For a set RS comprising two compression rules, a single bit is sufficient to identify the compression rule used. For four rules, two bits are necessary, and so on. We did not take this effect into account when specifying compression calculations to select among the candidate rules, but this effect must be taken into account when determining a stopping criterion for the training algorithm.Furthermore, this effect is retroactive, i.e., compression calculations that are performed when the RS set of compression rules includes four rules, which can be designated by two bits, must be repeated for all compression rules when the RS set increases to five rules, which therefore requires that all rules be designated by at least three bits. Another possibility is to give a maximum size to the RS set of compression rules. For example, it may be decided to limit the size of the RS set to 8 compression rules, in order to ensure that compression rules used to compress data packet headers can be identified with only 3 bits.These two possibilities can be combined, namely that a maximum number of compression rules can be defined, but the learning algorithm can stop before this maximum number is reached because the compression achieved by the last rule added is zero or less than a given threshold. Figure 3, on the other hand, represents another example of a calculation step of the reinforcement learning algorithm that is implemented in this example of obtaining a set RS of compression rules. This example illustrates the interest of calculating a field compression potential corrected by a factor. In the example of Figure 3, we are at step S0 of the reinforcement learning algorithm. The training set E includes N = 100 packets. The headers of these 100 packets are formed by two fields C1 and C2 of different lengths, unlike the example of Figure 2 where the four fields were of the same length.Field C1 has a length of L = 2; field C2 has a length of L = 10, both lengths being expressed in number of bits. As in the previous example, the sections of packets that we will try to compress are the two fields C1 and C2. As for the values ​​taken by the fields in the training set E, field C1 takes a single value, therefore the frequency of appearance of this value ^^1. 1 is worth ^^11 = 100%. For the field C2, on the other hand, it has a different value in the 100 packets of the training set E. For the field C2, we therefore have 100 training frequencies which are all worth 1%, which is noted in Figure 3: ^^2 1 = ^^2 2 = ⋯ = ^^2 100= 1%. We recall that, in our example of realization, the formula to calculate a field compression potential PCC is: ^^ ^^ ^^ = ^^( ^^ − ^^) where N is the size of the set, L the length of the section and H an estimator of the entropy. The formula to calculate the entropy estimator is: ^^ = − ∑^^∈[1, ^^]^^ ^^ ^^ ^^ ^^2( ^^ ^^ ) in which K is the number of different values takes the section in the training set and ^^ ^^ is the frequency of occurrence of the value ^^ ^^. The entropy estimator seeks, as its name suggests, to estimate the entropy that the values ​​taken by the section seem to manifest in view of the training set E. The lower the entropy, the more the values ​​taken by the section will be ordered and therefore the easier it will be to compress them, for example with a rule for erasing the value, and therefore the more the section is of interest to be compressed, and therefore the more interesting it is for the learning algorithm to seek to create candidate rules for this section, the best rule being added to the set of compression rules RS. For example, when a section takes a single value in the training set E, the frequency of appearance of this single value is therefore 100%, or 1, the logarithm of this frequency is therefore 0 and the entropy estimator too. The entropy estimator is therefore at its minimum.The field compression potential is then PCC = N * L, as is the case for the field C1 in the example of Figure 3. Here we have: PCC(C1) = 100 * 2 = 200, which is the maximum potential for a field of length 2 that appears in 100 packets. Indeed, the compression of the field C1 will be done by an erasure rule of the unique value taken by C1 in the training set E, i.e. a maximum compression of 200 bits. A defect of the entropy estimator, which appears in the example of Figure 3, is that this quantity grows at too low a rate even in the presence of maximum entropy. This is what we see in the field C2. The entropy taken by the values ​​of the field C2 in the training set E is maximum, since here we have 100 different values ​​for the 100 packets, the 100 values ​​therefore have the same frequency of appearance, namely 1%, that is to say 0.01. The logarithm of this frequency is -6.644.The calculation of the field compression potential is detailed in the figure and we see that the entropy estimator is finally worth 6.644, which is the maximum value it can take in a training set E of 100 packets. But since the field C2 is of length 10, the field compression potential of C2 is worth PCC(C2) = 100 * (10 – 6.644) = 335.6 which is therefore greater than the compression potential of the field C1, PCC(C1), which is worth 200. The entropy estimator does not grow enough to compensate for the effect of the greater length of the field C2 compared to that of the field C1. It can be noted that if the length of the C2 field were L = 8 bits instead of being 10, then the field compression potential of C2 would be PCC(C2) = 100 * (8 – 6.644) = 135.6, which is less than the field compression potential PCC(C1) = 200.In other words, the low score of the entropy estimator would not be counterbalanced by the length of the field, while the C2 field would still be 8 bits long, compared to the 2-bit length of C1. In the case where the length of the C2 field is 10 bits, using the PCC field compression potential as with its initial formula would therefore lead to valuing the C2 field, which would be inefficient, because the compression brought about by a first rule would be, for example, a rule of replacement by the index of a dictionary, which would therefore replace two 10-bit values ​​by a one-bit index, i.e. a compression of 18 bits in the training set E for the two values ​​concerned by the dictionary taken by the C2 field, among the 100 values ​​taken, compared to the 200 bits of compression obtained by the rule of erasure of the single value ^^1. 1taken by the field C1. To correct for this effect, it is preferable to introduce a correction factor CORR to the field compression potential taking into account the number K of different values ​​that the field C takes. The simplest formula for the correction factor CORR is CORR = 1 – K / N, where K is the number of different values ​​taken by the field and N is the size of the set concerned. Rather than using the field compression potential PCC as given by the formula to select the field to be compressed, we multiply it by the correction factor CORR. For the field C1, as this field takes a unique value in the training set E, the correction factor CORR is 1 – 1 / 100 or 0.99 and the corrected compression potential PCC(C1) * CORR is 198.For the C2 field, as this field takes 100 different values ​​in the 100 packets of the training set E, the correction factor CORR is 1 – 100 / 100 or 0 and the corrected compression potential PCC(C2) * CORR is also canceled. Figure 3 finally shows the impact of the choice of applying or not the CORR correction factor to the learning algorithm. We are at the initial step S0 where the set of compression rules only includes the rule R0 which does not apply any compression operation to the packets of the set E. If the choice is made not to use a CORR correction factor, which is indicated in Figure 3 by the No-corr line, we will see that after selecting the rule R0, it is the C2 field which will be selected to create the candidate rules.Then, if we assume that no significant common prefix appears among the 100 values ​​taken by the field C2 in the training set E, the best rule R1 among the candidates will be that of creating a dictionary and replacing two values ​​by their indexes. If the choice is made instead to use the correction factor CORR, which is indicated in Figure 3 by the line Corr, it is indeed the field C1 which will be selected, and the added rule R1' will be that of deleting the unique value ^^1. 1taken by the field C1. In this case, as for the calculation example in Figure 2, the rule R0 can be removed from the training set because all packets processed by the rule R0 are also processed by R1'. This is indicated by the crossed-out box placed on the rule R0 in the definition of the compression rule set RS. We have seen an example of a formula for calculating the correction factor CORR but many other examples are possible. For example, we can say in general that the correction factor CORR is equal to ^^(1 − ^^− ^^ where N is the number of times the section appears in the training set; K is the ^^+ number of different values ​​that the section takes in the training set; and ^^, ^^ and ^^ are chosen positive coefficients. The example seen previously where CORR is equal to 1 – K / N is then obtained for the values ​​^^ = 1, ^^ = 0 and ^^ = 0.Other choices of values ​​are possible for the positive coefficients ^^, ^^ and ^^ which can take for example the values ​​^^ = 1, ^^ = 1 and ^^ = 0. In this case, the correction factor takes the value 1 − ^^ and is therefore exactly 1 when the section takes a single value, that is to say for ^^ = 1. Conversely, the correction factor does not cancel when ^^ = ^^ which can be interesting to maintain a certain compression potential even on a section which still seems to be very random since all the values ​​taken by the section in the training set are different. More complex, non-affine formulas can be used to define the correction factor CORR by taking into account the values ​​K and N, ensuring that the correction factor CORR must always be positive, preferably between 0 and 1, and must be lower when the number K of different values ​​taken by the section among the N packets is large. Figure 4, for its part,represents another example of a computational step of the learning algorithm that is implemented in this example of obtaining a set RS of compression rules. This example illustrates the impact of the size of the values ​​on the compression potential of each value. In the example presented here, the training set E comprises N = 100 packets. The section we are interested in is a field C, which happens to be of variable length. The field C takes three values ​​V1, V2 and V3 in the training set E. The value V1 is of length L1 = 2 bits. It has an occurrence frequency of 80%, so it appears in N1 = 80 packets of the training set E. The value V2 is of length L2 = 100 bits. It has an occurrence frequency of 15%, so it appears in N2 = 15 packets of the training set E. The value V3 is of length L3 = 1000 bits. It has a frequency of occurrence of 5%,it therefore appears in N3 = 5 packets of the training set E. The general formula for calculating a value compression potential PCV according to the invention is PCV = N * (L – H) where H is a partial entropy estimator which is the product of the frequency of occurrence of the value and its logarithm in base 2. Since the field C is of variable length, we must use the length of each value V1, V2 and V3 taken by the field C. Figure 4 gives the calculation of the three value compression potentials PCV(V1), PCV(V2) and PCV(V3) for the three values ​​V1, V2 and V3. We see that the highest potential is that of V3. The greater length L3 = 1000 bits makes this value more interesting to compress, for example by erasing it, than the other values,even if V3 is the value with the lowest frequency of occurrence. This simple calculation example shows that it is indeed the length of the value that can be erased by a compression operation that is the main determinant of the value compression potential, and therefore that it is indeed an effective compression potential that is used in the learning algorithm according to the invention and not simple frequencies of occurrence. The calculation example also shows the importance of the training set E reflecting the actual traffic that must be compressed. With a very low frequency, a very long value will still be selected as having good compression potential. If the number of compression rules deployed is limited, and this very long value is ultimately almost never present,the compression rule that concerns it may end up taking the place of another compression rule that applies to a shorter but more frequent value, so that effective compressions will not be achieved due to the poor estimation of the frequencies in the training set E. Figure 5, for its part, represents a complete system SYS for transmitting data packets in a network NET that uses a set RS of compression rules obtained by a method according to the invention. The system SYS has an architecture where several network devices are found, namely a gateway GW and two terminals DVC1 and DVC2. The transmission system SYS according to the invention is formed of these three devices GW, DV1 and DVC2. These network devices are connected by the network NET. It can be for example a local network where the gateway GW allows terminals DVC1, DVC2 to access other wider networks,such as the Internet network. The communication protocols between the network equipment GW, DVC1, DVC2 can be of any type as long as they are based on the transmission of data packets P, P'. The network NET can therefore be a local home network using a WiFi communication link, or a network for deploying connected objects of the LPWAN type using a LoRa communication link or any other link suitable for connected objects or a mobile network where the gateway GW is a base station and the terminals DVC1, DVC2 are mobile terminals. The gateway GW comprises a compression management entity 100; a decompression management entity 200; as well as an obtaining entity 300 (not shown in the figure) which comprises a module 301 for obtaining a compression potential (not shown in the figure), an APP module for obtaining a set RS of compression rules, a collection module COL,and a DEP deployment module. The DVC1 terminal comprises a compression management entity 100 and a decompression management entity 200. The DVC2 terminal, for its part, only comprises a compression management entity 100 and no decompression management entity 200. It may have been decided, during the construction of the SYS system, that the DVC2 terminal almost exclusively transmitted P packets and received almost none. To save the memory size necessary for the construction of the DVC2 terminal, it may therefore have been decided not to integrate a decompression management entity 200 in the DVC2 terminal. It will therefore not be possible to transmit to the DVC2 terminal a compressed P packet according to the invention, because it could not decompress it and therefore restore it. We see that the SYS transmission system includes the following modules: • at least one APP module for obtaining a set of RS compression rules,present in the GW gateway; • at least one module 101 for applying a compression operation to a section value of a data packet P, the module 101 not being shown in FIG. 5 but included in a compression management entity 100 present in the GW gateway or the DVC1, DVC2 terminals; • at least one module 102 for transmitting a data packet P, the module 102 also being included in a management entity 100; • at least one module 201 for receiving a data packet P, the module 201 not being shown in FIG. 5 but included in a decompression management entity 200 present in the GW gateway or the DVC1 terminal; • at least one module 202 for applying a decompression operation, the module 202 also being included in a management entity 200; • at least one collection module COL in the network NET of packets P, P' to form a learning set E,present in the GW gateway; • at least one DEP deployment module synchronized in the network nodes of a set of compression rules RS, present in the GW gateway. The GW gateway uses its compression management entity 100 to compress and then transmit to the DVC1 terminal a data packet P. The packet P has been compressed using a compression rule whose identifier is I. In the embodiment illustrated here, the compression management entity jointly transmits the identifier I of the compression rule and the compressed packet P, which is illustrated by the arrow labeled I+P between the compression management entity 100 belonging to the GW gateway and the decompression management entity 200 belonging to the DVC1 terminal. The DVC2 terminal, for its part, addresses a compressed packet P',which has been compressed using a rule whose identifier is I'. The compression management entity 100 of the DVC2 terminal therefore sends a concatenation I'+P' to the decompression management entity 200 of the GW gateway. As all the compression management entities 100 and decompression 200 present in the SYS system have the same set of compression rules RS, it is easy for the network equipment which receives it to reestablish a compressed packet P, P' by applying to it the symmetrical decompression operations of the compression operations which were applied to the values ​​taken by the sections of the data packet P, P'. The identifiers I, I' transmitted jointly with the packets P, P' make it possible to find the rule and therefore the compression operations which were applied. In the architecture of the SYS system presented in figure 5,the GW gateway has a COL collection module that allows the GW gateway to gather the data packets P, P' that pass through the GW gateway. In a local network architecture, like the one presented here, the GW gateway is ideally placed to see all the P, P' packets passing through the NET network. The P, P' packets collected by the COL module will be used to create a training set E that will be used by the APP training module also present in the GW gateway. The APP training module learns the set of compression rules RS using P, P' packets actually collected in the NET network by the COL collection module,which is a point allowing to greatly improve the relevance of the learning and of the set of RS compression rules. The deployment module DEP will then deploy in a synchronized manner the set of RS compression rules in the gateway GW and the terminals DVC1, DVC2 of the system SYS. According to one embodiment, the compression 100 and decompression 200 management entities can have several sets of compression rules. This makes it possible to facilitate the synchronized deployment of new sets of RS compression rules by allowing to use in a concurrent manner several sets of RS compression rules while all the equipment present in the network NET has received the new sets of RS compression rules. The collection module COL will of course transmit to the learning module APP packets P, P' restored in their integrity, and not compressed packets,because only restored packets allow learning the information on the most interesting section values ​​to compress. The training set E will not necessarily be built with all the packets P, P' collected by the COL module. Sampling strategies can be used to build training sets that are as representative as possible of the traffic in the NET network, according to the transmission times or according to the actions of the terminals DVC1, DVC2 and the gateway GW. The transmission system SYS therefore fulfills the expected technical objective, namely the application of very effective compression rules to the packets P,P' using an APP learning module to perform automatic learning of the RS compression rule set. The COL collection module to feed the APP learning module with a learning set E representative of the traffic present in the NET network and the DEP deployment module in a synchronized manner of the RS compression rule set obtained by learning make it possible to ensure that this very efficient compression of the P, P' packets is obtained on the one hand without human intervention and on the other hand will adapt in the event of a change of context in the NET network, for example in the event of a change of IP addresses of the network equipment GW, DVC1, DVC2 or in the event of the addition of new equipment in the network. The placement of the COL collection modules,APP learning and DEP deployment in the GW gateway provide an efficient architecture where the functions related to learning are centralized in the GW gateway. This architecture is efficient because the GW gateway sees all the packets of the NET network transit and also has a direct link with the DVC1, DVC2 terminals present in the NET network. However, other architectures are possible. Figure 6, for its part, represents a complete SYS system for transmitting data packets in a NET network according to the invention, but having a different architecture from that seen in Figure 5. The SYS system here comprises three network devices, namely two routers RTR1 and RTR2 and a separate server SRV. The NET network can here be for example an IP core network and the two routers RTR1,RTR2 can form a branch of a long-distance network connecting two points. The communication link can then be on a very high-speed optical fiber, and the underlying protocol can be, for example, Gigabit Ethernet, often used in the core network, or any other suitable protocol. The two routers RTR1, RTR2 each include a compression 100 and decompression 200 management entity which allow the exchange of packets P, P', transmitted jointly with identifiers I, I' of compression rules belonging to the set RS. We saw in the presentation of figure 5 how the transmission of packets P, P' worked. We just note here that the transmission system SYS includes a server SRV dedicated to learning the set of compression rules RS. The router RTR1 includes a collection module COL which will address packets P1, P2,P3 to the SRV server. A COL collection module can be placed in a single router RTR1 since it sees, in transmission or reception, all the packets transiting on the NET network. But it is also possible to have several COL collection modules, one in each router RTR1, RTR2, depending on the practicality of this deployment. If the SRV server is co-located with the router RTR1 in the same data center or network room,it will be much more efficient to deploy a COL collection module only in the router RTR1 and not in the router RTR2 which can be very far away in the case of a long-distance transmission. The SRV server is dedicated to learning the set of compression rules RS. It includes a obtaining entity 300 (not shown in the figure) which includes an APP module which will carry out the actual learning according to the invention of the set of compression rules RS. The deployment module DEP ensures the synchronized deployment of the set of compression rules RS in the two routers RTR1 and RTR2. It can be seen that the invention lends itself to very wide use, in all kinds of network architectures. Other architectures of the transmission system SYS are possible to adapt to other architectures of the network NET, for example a star architecture, or a ring architecture. Similarly,the protocols that can be optimized using the compression presented here are very numerous, both in the core network or for local networks or for mobile networks. Finally, let us point out here that, in this text, the term "module" can correspond to a software component as well as to a hardware component or a set of hardware and software components, a software component itself corresponding to one or more computer programs or subroutines or more generally to any element of a program capable of implementing a function or a set of functions as described for the modules concerned. In the same way, a hardware component corresponds to any element of a hardware assembly capable of implementing a function or a set of functions for the module concerned (integrated circuit, smart card, memory card, etc.).,

Claims

Claims 1. Method for obtaining a set (RS) of compression rules capable of compressing a data packet (P) transmitted in a network (NET), the packet (P) comprising sections taking respective values ​​expressed in the form of a sequence of bits, the packet (P) being capable of being compressed by applying compression rules capable of removing bits from a value taken by a section of the packet (P), the method comprising, for several given sections of packets, obtaining a compression potential (PC) for a given section, relative to the values ​​taken by the given section in the packets belonging to a set (E) of data packets, a so-called learning set, followed by obtaining the set (RS) of compression rules as a function of the compression potentials (PC) obtained. 2.Method for obtaining a set (RS) of compression rules according to claim 1, characterized in that it comprises the following steps executed when obtaining the set (RS) of compression rules: • a step of selecting a rule belonging to the set (RS) of compression rules using at least one compression potential (PC) from among those obtained; • a step of creating a new rule from the selected rule using at least one compression potential (PC) from among those obtained; • adding the new rule to the set (RS) of compression rules; and in that said steps are iterated, until a stopping criterion is satisfied, using at the following iteration the set (RS) of rules modified by the addition of the new rule. 3.Method for obtaining a set (RS) of compression rules according to one of claims 1 or 2, characterized in that one of the compression potentials obtained is called field compression potential (PCC) and is calculated for a given section of fixed length according to the formula ^^( ^^ − ^^) in which N is the number of times that the section appears in the so-called learning set; L is the length of the section expressed in bits and H is a quantity called field entropy estimator which is calculated using the formula ^^ = − ∑^^∈[1, ^^]^^. ^^ ^^ ^^ ^^2( ^^ ^^ ) In where K is the number of different values ​​^^^^∈[1, ^^] that the section takes in the so-called learning set (E) and ^^ ^^ is the frequency of occurrence of the value ^^ ^^.

4. Method for obtaining a set (RS) of compression rules according to one of claims 1 to 3, characterized in that one of the compression potentials obtained is called field compression potential (PCC) and is calculated for a given section of following the formula ∑ ^^ ^ ^=1 ^^ ^^ . ( ^^ ^^ − ^^ ^^ ) in which K is of different values ​​^^^^∈[1, ^^] that the section takes in the so-called learning set (E); ^^ ^^ is the number of times the section takes the ^^ ^^ in the so-called learning set (E); ^^ ^^ is the length of the value ^^ ^^ expressed in bits and ^^ ^^ is a quantity called partial entropy of a value, which is calculated for the ^^ ^^ following the formula ^^ ^^ = − ^^ ^^ ^^ ^^ ^^2( ^^ ^^ ) in which ^^^^ is the frequency of occurrence of the value ^^ ^^ in the so-called learning set (E).

5. Method for obtaining a set (RS) of compression rules according to one of claims 1 to 4, characterized in that one of the compression potentials obtained is called corrected compression potential and is calculated for a given section by multiplying the field compression potential ^^− ^^ (PCC) by a correction factor^^which N is the number of times that the section appears in the so-called set; K is the number of different values ​​that the section takes in the so-called learning set (E); and ^^, ^^ and ^^ are chosen positive coefficients.

6. Method for obtaining a set (RS) of compression rules according to one of claims 1 to 5, characterized in that one of the compression potentials obtained is called the value compression potential (PCV) and is calculated for a given section value ^^ according to the formula ^^( ^^ − ^^ ^^ ) in which N is the number of times the value ^^ ^^ appears in the so-called learning set (E); L is the length of the value bits ; and ^^ ^^ is a quantity called partial entropy estimator of a value which is calculated according to the formula ^^ ^^ = − ^^ ^^ log2( ^^ ^^ ) In ^^ is the frequency of occurrence of the value ^^ ^^.

7. Method for obtaining a set (RS) of compression rules according to one of claims 1 to 6, characterized in that one of the calculated quantities is called field bit compression potential (PCB) and is calculated for a rank ^^ of a given section, the ranks ^^ varying from 0 to ^^ − 1, L being the minimum length of the values ​​taken by the section in the so-called learning set (E), said length L being expressed in number of bits, and said quantity (PCB) is calculated according to the formula 1 − ^^( ^^ ^^ ), in which ^^ a quantity called the entropy estimator of a field bit which is calculated according to the formula − ( ^^ ^^ . ^^ ^^ ^^2( ^^ ^^ ) + (1 − ^^ ^^ ). ^^ ^^ ^^2(1 − ^^ ^^ )), said formula being by convention 0 when ^^ ^^is 0 or 1, a formula in which the values ​​taken by the section in the so-called learning set (E) are expressed in ordered bit sequences and in which ^^ ^^is the frequency of appearance of one of the values ​​0 or 1 for the bit of rank ^^ among the values ​​taken by the section in the so-called learning set (E).

8. Method for obtaining a set (RS) of compression rules according to one of claims 2 to 7, characterized in that the step of selecting a rule belonging to the set (RS) of compression rules comprises the partition of the so-called learning set (E) into subsets assigned to a rule of the set (RS) of compression rules; the calculation of a compression potential (PC) for the rules of the set (RS) of compression rules and the selection of the rule according to its compression potential (PC), the compression potential (PC) of a rule being calculated from field compression potentials (PCC) calculated on the subset of the so-called learning set (E) assigned to the compression rule. 9.Method for obtaining a set (RS) of compression rules according to one of claims 2 to 8, characterized in that the step of creating a new rule from the selected rule is carried out by creating one or more candidate rules and retaining a rule from among the candidate rules created according to the effectiveness of the compression carried out by this rule on packets of the so-called learning set (E).

10. Method for obtaining a set (RS) of compression rules according to one of claims 2 to 9, characterized in that the step of creating a new rule from the selected rule. is done by choosing a section according to its field compression potential (FCP) and creating candidate rules formed by adding to the selected rule a compression operation applying to the values ​​taken by the section. 11.Management entity (300) for obtaining a set (RS) of compression rules capable of compressing a data packet (P) transmitted in a network (NET), the packet (P) comprising sections taking respective values ​​expressed in the form of a sequence of bits, the packet (P) being capable of being compressed by applying compression rules capable of removing bits from a value taken by a section of the packet (P), characterized in that the management entity (300) comprises the following modules: • a module (301) for obtaining a compression potential (PC) for a given section, relative to the values ​​taken by the given section in the packets belonging to a set (E) of data packets, called the learning set; • a module (APP) for obtaining the set (RS) of compression rules as a function of the compression potentials (PC) obtained. 12.Management entity (300) according to claim 11 further comprising the following modules: • a module (COL) for collecting in the network (NET) data packets transmitted to form a learning set (E) used to obtain the set (RS) of compression rules; • a module (DEP) for synchronized deployment in the nodes of the network (NET) of the set (RS) of compression rules.

13. Network equipment (GW, DVC1, DVC2, RTR1, RTR2) comprising a management entity (300) for obtaining a set (RS) of compression rules according to claim 11.

14. Computer program capable of being implemented by a management entity (300), the program comprising code instructions which, when executed by a processor, performs the steps of the obtaining method defined in claim 1. 15.Data carrier on which is recorded a computer program comprising a sequence of code instructions for implementing the obtaining method according to claim 1 when it is loaded into and executed by a processor.

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