A small packet based content distribution method

Abstract

The application provides a small data packet-based content distribution method and relates to the technical field of wireless communication. The method comprises the following steps: distributing a plurality of mobile nodes into a plurality of node sets in a distributed manner; updating the plurality of node sets in real time by using a node set update formula to obtain updated node sets; setting a reward function in each node set based on the updated node sets; determining real-time reward values of corresponding nodes according to the reward function; updating Q values of the corresponding nodes in real time according to the real-time reward values to obtain updated Q values; obtaining optimized relay nodes by using the updated Q values; and transmitting to-be-transmitted information by using the optimized relay nodes until the to-be-transmitted information is transmitted to target nodes, and returning to the Q value updating step if the to-be-transmitted information is not transmitted to the target nodes. The application solves the problem that the traditional algorithm in the prior art cannot perceive the context information of nodes and messages and cannot implement efficient opportunistic network routing.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications, and in particular to a content distribution method based on small data packets. Background Art

[0002] With the rapid development of modern science and technology, wireless communication has made significant progress over the past few decades and has become one of the most mature technologies in the world. The TCP / IP protocol provides a rich variety of applications and services, including but not limited to email, file transfer, and information sharing. To ensure the stable operation of the TCP / IP protocol, a stable end-to-end link must be established between the source and destination nodes. However, due to constraints such as node energy and geographic location, maintaining a stable end-to-end link is not easy in real-world environments.

[0003] To address the challenges of communicating in networks lacking stable end-to-end links, researchers have proposed a new networking paradigm: delay-tolerant networking (DTN). Its application scenarios extend beyond space to include terrestrial, marine, and even subsea environments, including wildlife tracking, post-disaster rescue, remote area communications, and social mobility networks. In these scenarios, network topology frequently changes and pre-existing infrastructure is lacking, making end-to-end paths relatively scarce. Opportunistic networking, a derivative of DTN, leverages the timing of node encounters to exchange information.

[0004] Routing is an essential foundational function in opportunistic networks and a hot topic of research. Efficient routing algorithms can reduce communication latency, minimize energy consumption, and improve data delivery efficiency. The core of opportunistic network routing is the selection of appropriate relay nodes for message forwarding and transmission. Traditional opportunistic network routing fails to effectively utilize the contextual information of network nodes and cannot accurately select appropriate relay nodes. In recent years, with the development of artificial intelligence (AI) technologies, they have been combined with opportunistic network routing to achieve more accurate contextual information perception and more efficient message routing.

[0005] Content distribution is an important application of message communication in opportunistic networks. Based on the size of the content to be distributed, it is mainly divided into small data packets and large data packets. Existing opportunistic network routing methods have the following main shortcomings:

[0006] For the transmission of small data packets, traditional algorithms have insufficient awareness of the contextual information of nodes and messages and cannot achieve efficient opportunistic network routing. Summary of the Invention

[0007] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a content distribution method based on small data packets. The present invention solves the problem that traditional algorithms in the prior art have insufficient perception of the context information of nodes and messages and cannot achieve efficient opportunistic network routing.

[0008] To achieve the above object, the present invention provides the following solutions:

[0009] A content distribution method based on small data packets, comprising:

[0010] Distributively dividing multiple mobile nodes into multiple node sets;

[0011] Using a node set update formula to update the multiple node sets in real time to obtain multiple updated node sets;

[0012] Based on the updated plurality of node sets, setting a reward function in each node set, the reward function including a transmission reward function and an encounter reward function;

[0013] Determine the real-time reward value of the corresponding node based on the reward function;

[0014] updating the Q value of the corresponding node in real time according to the real-time reward value to obtain an updated Q value, wherein the corresponding node is the source node of the current information and the current relay node;

[0015] Use the updated Q value to obtain the optimized relay node;

[0016] The optimized relay node is used to transmit the information to be transmitted until the information to be transmitted is transmitted to the target node. If the information is not transmitted to the target node, the process returns to the step of "updating the Q value of the corresponding node in real time according to the real-time reward value to obtain an updated Q value".

[0017] Preferably, the distributed division of the plurality of mobile nodes into a plurality of node sets includes:

[0018] Obtain the movement patterns of each mobile node and the historical encounter data of each mobile node;

[0019] The multiple mobile nodes are divided into multiple node sets according to the movement rules of the mobile nodes and the historical encounter data of the mobile nodes.

[0020] Preferably, the expression of the node set is:

[0021]

[0022] Among them, the SCS i(j) is a node set, MS(i) is the encounter set of node i, |MS(i)| is the cardinality of the set, and MS(i)∩MS(j) is the intersection of the encounter sets of nodes i and j.

[0023] Preferably, the node set update formula is expressed as:

[0024]

[0025] The SC i is the updated node set.

[0026] Preferably, updating the Q value of the corresponding node in real time according to the real-time reward value to obtain an updated Q value includes:

[0027] If a node encounter occurs at the source node and no information transmission occurs, determining a first reward value according to an encounter reward function;

[0028] If a node encounter occurs at the source node and information transmission occurs, determining a second reward value according to a transmission reward function;

[0029] The Q value of the corresponding node is updated in real time according to the first reward value and the second reward value to obtain an updated Q value.

[0030] Preferably, the expression of the encounter reward function is:

[0031]

[0032] Among them, R c (d,i) is the reward generated by the message with the target node d when the current node is connected to node i, R c (d,i) is the reward generated by the message with the target node d when the current node is connected to node i, g is a constant greater than 0, B size (i) is the cache size of node i, B occupied (i) refers to the size of the current buffer area occupied.

[0033] Preferably, the expression of the transmission reward function is:

[0034]

[0035] Among them, R t (d,i) is the transmission reward function, and hop refers to the number of hops the message has experienced so far.

[0036] The present invention discloses the following technical effects:

[0037] The present invention provides a content distribution method based on small data packets, comprising: distributing multiple mobile nodes into multiple node sets in a distributed manner; updating the multiple node sets in real time using a node set update formula to obtain multiple updated node sets; setting a reward function in each node set based on the updated multiple node sets, the reward function including a transmission reward function and an encounter reward function; determining a real-time reward value for a corresponding node according to the reward function; updating the Q value of the corresponding node in real time according to the real-time reward value to obtain an updated Q value, wherein the corresponding node is the source node of the current information and the current relay node; obtaining an optimized relay node using the updated Q value; and transmitting the information to be transmitted using the optimized relay node until the information to be transmitted reaches the target node. If the information to be transmitted does not reach the target node, returning to the step of "updating the Q value of the corresponding node in real time according to the real-time reward value to obtain an updated Q value." The present invention introduces Q learning into the routing of small data packets, achieving more accurate and intelligent relay node selection, and achieving goals such as targeted spraying and adaptive initial replica number, thereby improving the delivery success rate of small data packets and reducing network load. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 A flow chart of a content distribution method based on small data packets provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] like Figure 1 As shown, the present invention provides a content distribution method based on small data packets, comprising:

[0043] Step 100: Distributively divide the multiple mobile nodes into multiple node sets;

[0044] Step 200: using a node set update formula to update the plurality of node sets in real time to obtain a plurality of updated node sets;

[0045] Step 300: Based on the updated plurality of node sets, a reward function is set in each node set, wherein the reward function includes a transmission reward function and an encounter reward function;

[0046] Step 400: Determine the real-time reward value of the corresponding node according to the reward function;

[0047] Step 500: updating the Q value of the corresponding node in real time according to the real-time reward value to obtain an updated Q value, where the corresponding node is the source node of the current information and the current relay node;

[0048] Step 600: Utilize the updated Q value to obtain the optimized relay node; when the corresponding Q value of the encountered node is greater than its own, it is selected as the relay node to achieve directional propagation of the message.

[0049] Step 700: Use the optimized relay node to transmit the information to be transmitted until the information to be transmitted is transmitted to the target node. If it is not transmitted to the target node, return to the step of "updating the Q value of the corresponding node in real time according to the real-time reward value to obtain an updated Q value."

[0050] Furthermore, the distributed division of the plurality of mobile nodes into a plurality of node sets includes:

[0051] Obtain the movement patterns of each mobile node and the historical encounter data of each mobile node;

[0052] The multiple mobile nodes are divided into multiple node sets according to the movement rules of the mobile nodes and the historical encounter data of the mobile nodes.

[0053] Specifically, because mobile nodes in opportunistic networks are often vehicle-mounted or handheld devices, such as mobile phones and tablets, their movement mirrors the movements of vehicles or pedestrians. Rather than being completely random, they exhibit a certain regularity and periodicity. Furthermore, nodes possess social attributes: nodes belonging to the same geographical "community" may frequently encounter one another. Messages transmitted between nodes belonging to the same "community" have a greater probability of successful delivery. In addition to pre-determining geographical communities, the "social" attributes of nodes can also be characterized based on the movement patterns of mobile nodes or their encounter histories.

[0054] Furthermore, we define social circle similarity to represent the similarity between the encounter sets of two nodes. If two nodes have encountered many of the same nodes, that is, have many "common friends", then their social circles are considered to be highly similar. The specific definition method (node ​​set expression) is as follows:

[0055]

[0056] Among them, the SCS i (j) is the node set, MS(i) refers to the encounter set of node i, that is, the nodes that node i has previously encountered. |MS(i)| refers to the cardinality of the set, that is, the size of the set. MS(i)∩MS(j) refers to the intersection of the encounter sets of nodes i and j, that is, the nodes that both nodes have encountered. The more similar the social circles of two nodes are, the more likely they are to successfully deliver messages.

[0057] Furthermore, the expression of the node set update formula (the specific method for nodes to update social circles) is:

[0058]

[0059] The SC i is the updated node set. When node i meets node j, if SCS i If (j) is greater than the threshold, node j is added to node i's social circle. The threshold is a number between 0 and 1 and can be adjusted based on scenario requirements. Each node creates an empty social circle at the beginning of the simulation and continuously updates its social circle during the simulation using the above method. This property of a node's social circle is taken into account when setting the reward function of the ANSAW-Q algorithm and the number of initial message replicas.

[0060] Furthermore, updating the Q value of the corresponding node in real time according to the real-time reward value to obtain an updated Q value includes:

[0061] If a node encounter occurs at the source node and no information transmission occurs, determining a first reward value according to an encounter reward function;

[0062] If a node encounter occurs at the source node and information transmission occurs, determining a second reward value according to a transmission reward function;

[0063] The Q value of the corresponding node is updated in real time according to the first reward value and the second reward value to obtain an updated Q value. During the entire routing process, the node updates the corresponding Q value in a distributed manner using the two reward values ​​and stores it in its own Q table.

[0064] Specifically, there are two ways to trigger the Q value update, one is to establish a connection between two nodes, and the other is to complete the message transmission between two nodes.

[0065] When two nodes meet to establish a connection, the node will update the Q value according to the set connection reward function, because the nodes that meet frequently have more transmission opportunities and are more likely to deliver messages to the target node. Reasonably updating the corresponding Q value when the nodes meet can reflect this social relationship to some extent, so that the routing algorithm can more intelligently select relay nodes when performing directional spraying.

[0066] Further, the node Q value update expression when the nodes meet is:

[0067]

[0068] The above formula represents the Q value update when two nodes meet, where Q S (d,i) represents the Q value corresponding to the meeting node i that transmits the message of the target node d to the current node S in the perspective of the current node S. Alpha represents the learning rate, which is a parameter in Q learning. Gamma is the discount factor, which represents the importance of future rewards. In reinforcement learning, the weights of current rewards and rewards obtained by future actions are different, so the discount factor gamma between 0 and 1 is used to adjust the weight of future rewards. The greater the value of gamma, the more emphasis on future rewards. When gamma = 1, future rewards are equally important as current rewards. R c (d,i) is the connection reward function. is the maximum Q value obtained by transmitting the message of the target node d to all neighbor nodes of node i in the perspective of node i. N i represents the neighbor node set of node i.

[0069] When two nodes complete message transmission, the node will update the Q value according to the transmission reward function. In the current state, performing an action (i.e., selecting a relay node for message transmission) will obtain a reward, and the Q value will be updated according to this reward and state transition. The node Q value update expression when the node transmits is:

[0070]

[0071] The above formula represents the Q value update after successful message transmission between two nodes, and the only difference from the previous formula is R t (d,i), which is the transmission reward function.

[0072] Specifically, in reinforcement learning, the setting of rewards is crucial. Specifically, the value of the reward R directly affects the update of the Q value, thereby affecting the routing decision.

[0073] Further, the expression of the meeting reward function is:

[0074]

[0075] Among them, R c (d,i) is the reward generated by the message with the target node d when the current node is connected to node i. CT(i) refers to the number of times the current node encounters node i. β is a weight parameter between 0 and 1 that can be used to adjust the relative weight of the encounter frequency and the cache occupancy. size (i) is the cache size of node i, B occupied (i) refers to the size of the current cache area. g is a constant greater than 0. Its function is to generate an additional reward when the social circle of node i contains the message target node d. The size of g can be adjusted within a reasonable range, but it is necessary to ensure that R c (d,i) cannot be greater than the reward the node receives when it encounters the target node.

[0076] Furthermore, the expression of the transmission reward function is:

[0077]

[0078] Among them, R t (d,i) is the transmission reward function, which is calculated when a message is transmitted. The hop value is the number of hops the message has already traveled. A larger hop value indicates that the message has been forwarded and replicated more frequently, increasing its chances of being delivered to the target node. We prefer to prioritize the transmission of "new" messages with fewer hops, so when setting the transmission reward function, messages with larger hops receive smaller rewards.

[0079] If node i happens to be the target node for the message, a reward of 100 is received after successful transmission, indicating successful delivery and a significant reward. This value can also be set to other values, as long as the reward for successful delivery to the target node is significantly greater than the reward received in other situations. If node i is not the target node for the message, the reward value is determined by combining the number of hops of the message and the connection reward function between nodes.

[0080] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0081] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A content distribution method based on small data packets, characterized in that: include: Distributively dividing multiple mobile nodes into multiple node sets; Using a node set update formula to update the multiple node sets in real time to obtain multiple updated node sets; Based on the updated plurality of node sets, setting a reward function in each node set, the reward function including a transmission reward function and an encounter reward function; Determine the real-time reward value of the corresponding node based on the reward function; updating the Q value of the corresponding node in real time according to the real-time reward value to obtain an updated Q value, wherein the corresponding node is the source node of the current information and the current relay node; Use the updated Q value to obtain the optimized relay node; Utilize the optimized relay node to transmit the information to be transmitted until the information to be transmitted reaches the target node. If the information has not been transmitted to the target node, return to step "updating the Q value of the corresponding node in real time according to the real-time reward value to obtain an updated Q value"; Where Q is the Q value in Q learning; The expression of the node set is: ； Among them, the is a node set, the For nodes The encounter collection, For the momentum of the encounter, For nodes and the intersection of encounter sets; The expression of the node set update formula is: ； described is the updated node set; The expression of the encounter reward function is: ； in, For the current node and node When connecting, for the target node The reward generated by the message, For the current node and node When connecting, for the target node The reward generated by the message, is a constant greater than 0, is a node The cache size, Refers to the size of the current cache area occupied; The expression of the transfer reward function is: ； in, is the transmission reward function, and hop refers to the number of hops the message has experienced so far.

2. The content distribution method based on small data packets according to claim 1, characterized in that: The distributed division of the plurality of mobile nodes into a plurality of node sets includes: Obtain the movement patterns of each mobile node and the historical encounter data of each mobile node; The multiple mobile nodes are divided into multiple node sets according to the movement rules of the mobile nodes and the historical encounter data of the mobile nodes.

3. The content distribution method based on small data packets according to claim 2, characterized in that: The step of updating the Q value of the corresponding node in real time according to the real-time reward value to obtain an updated Q value includes: If a node encounter occurs at the source node and no information transmission occurs, determining a first reward value according to an encounter reward function; If a node encounter occurs at the source node and information transmission occurs, determining a second reward value according to a transmission reward function; The Q value of the corresponding node is updated in real time according to the first reward value and the second reward value to obtain an updated Q value.

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