Method and device for allocating time slots in wireless body area network based on Markov channel model

Abstract

The present invention discloses a method and device for allocating time slots of a wireless body area network based on a Markov channel model, wherein the method comprises: dividing the time axis into superframes of equal length, determining the basic communication process between nodes and a coordinator; obtaining the number of basic transmission time slots of each node, and allocating the number of time slots of the node according to the number of basic transmission time slots; completing the classification of nodes according to the Markov channel model; allocating the time slot interval of the node according to the classified category and the number of time slots of the node; and determining the transmission order of the node in the time slot interval according to the Markov channel model. The present invention allocates time slots in units of time slot intervals, firstly allocates the time slot intervals with a high probability of successful transmission to the emergency node, and determines the transmission order of each node in the time slot interval by a weight factor designed according to three characteristic factors of the channel model, thereby ensuring the success probability of emergency data transmission and improving the reliability of data transmission, and can be widely used in the field of wireless body area networks.

Description

Technical Field

[0001] The present invention relates to the field of wireless body area network, and in particular to a method and device for allocating time slots of a wireless body area network based on a Markov channel model. Background Art

[0002] Wireless body area network is the main underlying technology of healthcare IoT. It can continuously monitor the patient's physiological data without interfering with the normal activities of the human body, providing medical staff with more reliable and accurate diagnosis basis. Usually, a typical wireless body area network consists of a coordinator and multiple small smart sensors. The sensor nodes monitor and collect the vital signs data of the human body and send these data to the coordinator. After the coordinator collects and simply processes the data from the sensor nodes, it sends the data to the remote medical service center through other wireless network technologies.

[0003] Sensors in wireless body area networks have small battery capacity and are not easy to charge, so they are energy-constrained devices. In actual use, the energy efficiency of sensors should be improved as much as possible to extend the service life of sensors. In addition, in wearable wireless body area networks, the posture and movement state of the human body can change in real time. Wireless radio frequency signals exposed to the human body's surrounding environment will experience various attenuations of varying intensities due to factors such as tissue absorption, clothing blocking, human body shape and posture blocking. This causes the channel quality between the node and the coordinator to change in real time, thereby affecting the reliability of data transmission. The reduction in the reliability of data transmission will reduce the energy efficiency of the sensor. Therefore, it is very important to improve the reliability of data transmission.

[0004] The existing time slot allocation schemes either do not take into account the impact of the channel environment on data transmission, or although they take into account the channel quality, they are somewhat blind in selecting the data transmission time, and do not allow the node to transmit data when the channel quality is good. These problems will lead to the reliability of data transmission cannot be well guaranteed. In addition, in the wireless body area network scenario where the node link state change law is relatively stable, time slot allocation can be performed based on the Markov channel model. Existing studies have shown that the channel state is directly related to the human activity pattern. When the human body is in a stable motion mode under a given activity, each link has a Markov feature. Under different activity modes, the parameters of the Markov channel model are also different. At present, there are time slot resource allocation schemes based on the Markov channel model, but there are the following shortcomings. For example, the scheme determines the transmission order and the allocated time slot position of the node according to the time it takes for the probabilistic channel model of the node to meet the relevant probability threshold conditions, which may cause the emergency node to transmit data in a time slot with a relatively low probability of success. In addition, due to the restrictions of the node's traffic demand and the allocated time slot position, the allocated time slot position of the node in the scheme may conflict. Summary of the invention

[0005] In order to solve at least one of the technical problems existing in the prior art to a certain extent, the present invention aims to provide a method and device for allocating time slots in a wireless body area network based on a Markov channel model.

[0006] The technical solution adopted by the present invention is:

[0007] A time slot allocation method for a wireless body area network based on a Markov channel model comprises the following steps:

[0008] Divide the time axis into superframes of equal length and determine the basic communication flow between nodes and coordinators;

[0009] Obtain the number of basic transmission time slots of each node, and allocate the number of time slots of the node according to the number of basic transmission time slots;

[0010] Complete the classification of nodes according to the Markov channel model;

[0011] Allocate the time slot interval of the node according to the divided categories and the number of time slots of the node;

[0012] The transmission order of nodes within the time slot interval is determined according to the Markov channel model.

[0013] Furthermore, the basic communication process between the determination node and the coordinator includes:

[0014] Each superframe is divided into three parts: beacon phase, medium access phase and channel sounding phase;

[0015] In the beacon phase, the coordinator sends scheduling control information to the node by broadcasting Beacon frames. The scheduling control information includes the number of time slots allocated to the node, the transmission time slot position of the node, and the data transmission order of the node;

[0016] In the medium access phase, this phase is divided into K transmission time slots with the same time length. Each transmission time slot is divided into a DATA part and an ACK part. In the DATA part, the node transmits data to the coordinator. After receiving the data, the coordinator responds with an Ack frame within the ACK period.

[0017] In the channel detection phase, this phase is divided into N short probe time slots. Each node sends a probe signal to the coordinator in the probe time slot. The coordinator classifies the nodes according to whether the probe signal can be successfully received. If the coordinator can successfully receive the probe signal, the corresponding node is classified as a GOOD node. Otherwise, the corresponding node is classified as a BAD node.

[0018] Further, the obtaining of the number of basic transmission time slots of each node and allocating the number of time slots of the node according to the number of basic transmission time slots includes:

[0019] The coordinator determines the amount of new data generated by the node and the amount of cached old data according to the node data arrival rate and the node's historical data transmission status, thereby determining the number of basic transmission time slots required for each node;

[0020] The coordinator determines the number of time slots allocated to each node based on the node's basic transmission time slot number requirements;

[0021] The number of time slots is allocated in the following way:

[0022] If the sum of the basic transmission time slot requirements of all nodes is less than or equal to the preset number of transmission time slots provided, the coordinator allocates the corresponding number of time slots according to the basic transmission time slot requirements of each node;

[0023] If the sum of the basic transmission time slot requirements of all nodes is greater than the preset number of transmission time slots, the coordinator first initializes the allocation of the corresponding number of time slots according to the basic transmission time slot requirements of each node, and then reduces the number of time slots allocated to the corresponding node according to the node priority size and cache saturation.

[0024] Further, the process of reducing the number of time slots allocated to the corresponding node is as follows:

[0025] For nodes with different priorities, the number of time slots allocated to low-priority nodes is reduced first; when all low-priority nodes have only one allocated time slot left, the number of time slots allocated to high-priority nodes begins to be reduced; the priority of the nodes is divided into 1 and 2;

[0026] For nodes with the same priority, the number of time slots allocated to nodes with low cache saturation is reduced first; nodes with the same priority are sorted from large to small according to the cache saturation, and the time slots of the node with the lowest cache saturation are reduced first until the node has only one allocated time slot, and then the time slots of the node with the second lowest cache saturation are reduced until the total number of transmission time slots allocated to all nodes is equal to the preset number of time slots, or all nodes of the current priority have only one time slot left.

[0027] Furthermore, for node i, the calculation formula for the amount of newly generated data is:

[0028]

[0029] In the formula, s i is the average data arrival rate of node i, t SF is the duration of a superframe, b iis the number of bits occupied by a data packet. is the ceiling operator;

[0030] Combined with the amount of old data cached by node i, the basic transmission time slot number requirement of node i is as follows:

[0031]

[0032] In the formula, is the number of time slots required for node i to transmit all new and old data, is the number of packets cached by node i, R i is the data transmission rate of node i, t data It is the length of time occupied by the DATA part of a time slot.

[0033] Furthermore, the classification of nodes according to the Markov channel model includes:

[0034] Based on the Markov channel model, the channel between the segment node and the coordinator is judged to be in a good state or a bad state. If it is in a good state, the node is divided into a GOOD set; if it is in a bad state, the node is divided into a BAD set;

[0035] Combining the node priority, GOOD set and BAD set, all nodes are divided into four categories, namely: GOOD nodes with priority 1, GOOD nodes with priority 2, BAD nodes with priority 1 and BAD nodes with priority 2.

[0036] Further, judging whether the channel between the segment node and the coordinator is in a good state or a bad state based on the Markov channel model includes:

[0037] In the Markov channel model, the channel state between the node and the coordinator is divided into two states: good state and bad state. If the node can successfully transmit data to the coordinator, then the channel is in the good state in the current time slot, otherwise it is in the bad state.

[0038] If the probability that the link between the node and the coordinator is in the good state at a given time slot is denoted as p0, then if it is known that the channel state at a given time slot is good (i.e., p0 = 1) or bad (i.e., p0 = 0), and the human body is in the same activity mode, the probability that the link is still in the good state after τ time slots is:

[0039]

[0040] P ch =P GB +P BG

[0041] Where P BG represents the one-step transition probability of the channel state changing from bad to good, P GB represents the one-step transition probability of the channel state changing from good to bad; P ch The value of represents the convergence speed of p(τ); ch The larger the value of , the shorter the time it takes for p(τ) to converge from a good state or a bad state to a stable value; when the probability p0 is 1, the value of p(τ) shows a monotonically decreasing trend as τ increases; when the probability p0 is 0, the value of p(τ) shows a monotonically increasing trend as τ increases.

[0042] Furthermore, the allocating the time slot intervals of the nodes according to the divided categories and the number of time slots of the nodes includes:

[0043] Sum the number of time slots allocated to all nodes of the same category, and get the total number of time slots allocated to GOOD nodes with priority 2, GOOD nodes with priority 1, BAD nodes with priority 1, and BAD nodes with priority 2.

[0044] The coordinator combines the first time slot with the All time slots between the time slots are divided into the Channel-GOOD2 time slot interval, and the Channel-GOOD2 time slot interval is allocated to the GOOD node with a priority of 2 for the corresponding node to transmit data;

[0045] The coordinator will All time slots between the first time slot and the Kth time slot are divided into the Channel-BAD2 time slot interval, and the Channel-BAD2 time slot interval is allocated to the BAD node with a priority of 2 for the corresponding node to transmit data;

[0046] The coordinator will The time slot and All time slots between the time slots are divided into the Channel-GOOD1 time slot interval, and the Channel-GOOD1 time slot interval is allocated to the GOOD node with a priority of 1 for the corresponding node to transmit data;

[0047] The coordinator will The time slot and All time slots between the time slots are divided into the Channel-BAD1 time slot interval, and the Channel-BAD1 time slot interval is allocated to the BAD node with a priority of 1 for the corresponding node to transmit data;

[0048] If the sum of the basic transmission time slot number requirements of all nodes is less than the number of transmission time slots provided in the medium access phase of the superframe, the medium access phase of the superframe is divided into five intervals, namely: Channel-GOOD2 time slot interval, Channel-GOOD1 time slot interval, idle time slot interval, Channel-BAD1 time slot interval and Channel-BAD2 time slot interval;

[0049] If the sum of the basic transmission time slot number requirements of all nodes is greater than or equal to the number of transmission time slots provided during the medium access phase of the superframe, the medium access phase of the superframe is divided into four intervals, namely: Channel-GOOD2 time slot interval, Channel-GOOD1 time slot interval, Channel-BAD1 time slot interval and Channel-BAD2 time slot interval.

[0050] Further, determining the transmission order of the nodes in the time slot interval according to the Markov channel model includes:

[0051] Based on the Markov channel model, a comprehensive consideration of the initial value p of node i’s p(τ) in the time slot interval is designed. start,i , convergence speed p ch,i , convergence value p end,i The weighting factors of the three factors are i ;

[0052] According to the weight factor Λ i The value of determines the transmission order of each node in the time slot interval;

[0053] In the same time slot, the larger the weight factor, the earlier the corresponding node sends data.

[0054] The weight factor of node i is Λ i The specific settings are:

[0055]

[0056] The initial value p of p(τ) of the node i in the time slot interval start,i The specific settings are:

[0057] p start,i =p(x i )

[0058] In the formula, x i is the time slot distance between the first time slot in the time slot interval of node i and the time when the probe data was sent in the previous superframe;

[0059] The convergence value p of p(τ) of the node i in the time slot interval end,i The specific settings are:

[0060] p end,i =p(y i )

[0061] In the formula, y i is the time slot distance between the last time slot in the time slot interval of node i and the time when the probe data was sent in the previous superframe;

[0062] The convergence speed p of p(τ) of node i in the time slot interval ch,i The specific setting is the parameter P in the node channel model p(τ) ch The value of

[0063] p ch,i =P ch

[0064] Another technical solution adopted by the present invention is:

[0065] A time slot allocation device for a wireless body area network based on a Markov channel model, comprising:

[0066] at least one processor;

[0067] at least one memory for storing at least one program;

[0068] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0069] The beneficial effects of the present invention are as follows: the present invention allocates time slots in units of time slot intervals, first allocates time slot intervals with a high probability of successful transmission to emergency nodes, and determines the transmission order of each node in the time slot interval based on a weight factor designed according to three characteristic factors of a channel model, thereby ensuring the success probability of emergency data transmission, avoiding time slot allocation conflicts, and improving the reliability of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the embodiments of the present invention or the drawings of related technical solutions in the prior art are introduced below. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0071] Figure 1 It is a flow chart of a method for allocating time slots in a wireless body area network based on a Markov channel model in an embodiment of the present invention;

[0072] Figure 2is a structural diagram of a superframe in an embodiment of the present invention;

[0073] Figure 3 is a graph of the transmission success probability p(τ) when the channel state p0 of a given time slot is equal to 1 in an embodiment of the present invention;

[0074] Figure 4 is a graph of the transmission success probability p(τ) when the channel state p0 of a given time slot is equal to 0 in an embodiment of the present invention;

[0075] Figure 5 is a time slot interval distribution diagram when the number of time slots of all nodes is less than the number of time slots provided in the medium access phase in an embodiment of the present invention;

[0076] Figure 6 is a time slot interval distribution diagram when the number of time slots of all nodes is greater than or equal to the number of time slots provided in the medium access phase in an embodiment of the present invention;

[0077] Figure 7 is a curve diagram of the transmission success probability p(τ) of a GOOD node under different parameters in an embodiment of the present invention;

[0078] Figure 8 is a graph of the transmission success probability p(τ) of a BAD node under different parameters in an embodiment of the present invention;

[0079] Fig. 9 It is a schematic diagram of the allocation structure after dividing the superframe time slot interval in an embodiment of the present invention. DETAILED DESCRIPTION

[0080] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0081] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0082] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0083] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0084] This embodiment provides a method for allocating time slots in a wireless body area network based on a Markov channel model, comprising the following steps:

[0085] S101. Divide the time axis into superframes of equal length and determine the basic communication process between the node and the coordinator.

[0086] The specific process of step S101 is as follows:

[0087] The time axis is divided into superframes of equal length. Figure 2 As shown, each superframe is divided into three parts: beacon phase, medium access phase (Medium Access Phase, MAP) and channel detection phase (Channel Detection Phase, CDP).

[0088] In the beacon phase, the coordinator informs the node of scheduling control information such as the number of time slots allocated to the node, the node's transmission time slot position, and the node's data transmission order by broadcasting Beacon frames. The node obtains the above scheduling control information by receiving Beacon frames.

[0089] In the MAP phase, the node sends data to the coordinator in TDMA mode. The MAP phase is divided into K transmission slots with the same time length, and each transmission slot is divided into a DATA part and an ACK part. In the DATA part, the node transmits data to the coordinator. After receiving the data, the coordinator responds with an Ack frame within the ACK period.

[0090] In the CDP phase, the node sends a probe signal to the coordinator. The CDP phase is divided into N short probe slots. N is the total number of sensor nodes in the network. Each node sends a probe signal in the probe slot assigned to it. The coordinator classifies the nodes based on whether the probe signal can be successfully received. If the coordinator can successfully receive the probe signal, the corresponding node is classified as a GOOD node, otherwise it is classified as a BAD node.

[0091] S102: Obtain the number of basic transmission time slots of each node, and allocate the number of time slots of the node according to the number of basic transmission time slots.

[0092] The specific process of step S102 is as follows:

[0093] The coordinator determines the amount of newly generated data and cached old data based on the node data arrival rate and the node's historical data transmission status, thereby determining the number of basic transmission time slots required for each node. For node i, the calculation formula for the amount of newly generated data is:

[0094]

[0095] s i is the average data arrival rate of node i, t SF is the duration of a superframe, b i is the number of bits occupied by a data packet. is the round-up operator. Combined with the amount of old data cached by node i, we can get the basic transmission time slot number requirement of node i as follows:

[0096]

[0097] in, is the number of time slots required for node i to transmit all new and old data, is the number of packets cached by node i, R i is the data transmission rate of node i, t data It is the length of time occupied by the DATA part of a time slot.

[0098] The coordinator makes further adjustments based on the basic transmission time slot number requirements of the nodes and the number of transmission time slots that can be provided in the MAP phase of the superframe, thereby finally determining the number of time slots allocated to each node.

[0099] The process of adjusting the number of time slots is as follows:

[0100] If the sum of the basic transmission time slot number requirements of all nodes is less than or equal to the number of transmission time slots K that can be provided in the MAP phase, the coordinator allocates the corresponding number of time slots according to the basic transmission time slot requirement of each node.

[0101] If the sum of the basic transmission time slot requirements of all nodes is greater than the number of transmission time slots K that can be provided in the MAP time period, the coordinator first initializes the allocation of the corresponding number of time slots according to the basic transmission time slot requirements of each node, and then reduces the number of time slots allocated to the corresponding node according to the node priority size and cache saturation.

[0102] The process of reducing the number of time slots allocated to the corresponding node is as follows:

[0103] For nodes with different priorities, the number of time slots allocated to low-priority nodes is reduced first. When all low-priority nodes have only one allocated time slot left, the number of time slots allocated to high-priority nodes is reduced. The priority of nodes is divided into 1 and 2. If the node transmits normal vital sign data or non-vital sign data, the priority of the node is 1. If the node transmits abnormal vital sign data, the priority of the node is 2.

[0104] For nodes with the same priority, the number of time slots allocated to nodes with low cache saturation is reduced first. Nodes with the same priority are sorted from large to small according to the cache saturation, and the time slots of the node with the lowest cache saturation are reduced first until the node has only one allocated time slot, and then the time slots of the node with the second lowest cache saturation are reduced, and so on, until the sum of the number of transmission time slots allocated to all nodes is equal to the number of time slots K that can be provided in the MAP phase, or all nodes of the current priority have only one time slot left.

[0105] S103, completing the classification of nodes according to the Markov channel model.

[0106] The specific process of step S103 is as follows:

[0107] Based on the Markov channel model, if the node can successfully transmit the query data to the coordinator, then the channel between the node i and the coordinator is in a good state. Therefore, in the next superframe, the node i is classified into the GOOD set, that is, i∈GOOD. Otherwise, the node is classified into the BAD set, that is, i∈BAD.

[0108] The Markov channel model is:

[0109] In this model, the channel state between the node and the coordinator is divided into good and bad. If the node can successfully transmit data to the coordinator, then the channel is in a good state in the current time slot, otherwise it is in a bad state. If the probability that the link between the node and the coordinator is in a good state in a given time slot is represented as p0, then if it is known that the channel state in a given time slot is good (i.e., p0 = 1) or bad (i.e., p0 = 0), and the human body is in the same activity mode, the probability that the link is still in a good state after τ time slots is

[0110]

[0111] P ch =P GB +P BG

[0112] Where P BG represents the one-step transition probability of the channel state changing from bad to good, P GB P represents the one-step transition probability of the channel state changing from good to bad. ch The value of represents the convergence speed of p(τ). ch The larger the value of , the shorter the time it takes for p(τ) to converge from a good state or a bad state to a stable value. When the probability p0 is 1, the value of p(τ) decreases monotonically with the increase of τ, and its curve is as follows: Figure 3 When the probability p0 is 0, the value of p(τ) increases monotonically with the increase of τ, and its curve is as follows: Figure 4 shown.

[0113] Combining the node priority and the above classification, all nodes are finally divided into four categories, namely GOOD nodes with priority 1, GOOD nodes with priority 2, BAD nodes with priority 1 and BAD nodes with priority 2.

[0114] S104: Allocate time slot intervals of nodes according to the divided categories and the number of time slots of the nodes.

[0115] The specific process of step S4 is as follows:

[0116] Sum the number of time slots allocated to all nodes of the same category, and get the total number of time slots allocated to GOOD nodes with priority 2, GOOD nodes with priority 1, BAD nodes with priority 1, and BAD nodes with priority 2.

[0117] First, the coordinator combines the first time slot with the All time slots between the first and second time slots (including the first and second time slots) time slots) are divided into Channel-GOOD2 time slot intervals and allocated to GOOD nodes with a priority of 2 for data transmission.

[0118] The coordinator then sends All time slots between the Kth time slot and the Kth time slot (including the The 1st time slot and the Kth time slot) are divided into the Channel-BAD2 time slot interval and allocated to the BAD node with a priority of 2 for data transmission.

[0119] Next, the coordinator sends The time slot and All time slots between time slots (including time slot and time slots) are divided into Channel-GOOD1 time slot intervals and allocated to GOOD nodes with a priority of 1 for data transmission.

[0120] Finally, the coordinator will The time slot and All time slots between time slots (including time slot and time slots) are divided into Channel-BAD1 time slot intervals and allocated to BAD nodes with a priority of 1 for data transmission.

[0121] After the above division, the MAP part of the superframe is divided into several time slot intervals. If the sum of the basic transmission time slot number requirements of all nodes is less than the number of transmission time slots provided in the MAP phase, the MAP part of the superframe is divided into five intervals, namely, Channel-GOOD2 time slot interval, Channel-GOOD1 time slot interval, idle time slot interval, Channel-BAD1 time slot interval and Channel-BAD2 time slot interval, such as Figure 5 If the sum of the basic transmission time slots required by all nodes is greater than or equal to the number of transmission time slots provided in the MAP phase, the MAP part of the superframe is divided into four intervals, namely, Channel-GOOD2 time slot interval, Channel-GOOD1 time slot interval, Channel-BAD1 time slot interval and Channel-BAD2 time slot interval, as shown in Figure 6 shown.

[0122] S105 . Determine the transmission order of the nodes within the time slot interval according to the Markov channel model.

[0123] The specific process of step S105 is as follows:

[0124] Based on the Markov channel model, the present invention designs a method that comprehensively considers the initial value p of node i's p(τ) in the time slot interval. start,i , convergence speed p ch,i , convergence value p end,i The weighting factors of the three factors are i The transmission order of each node is determined according to the value of the weight factor. In the same time slot, the larger the value of the weight factor, the earlier the corresponding node sends data.

[0125] The weight factor of node i is Λ i The specific settings are

[0126]

[0127] The initial value p of p(τ) of the node i in the time slot interval start,iThe specific settings are

[0128] p start,i =p(x i )

[0129] x i It is the time slot distance between the first time slot in the time slot interval of node i (the left boundary of the time slot interval) and the time when the probe data was sent in the previous superframe.

[0130] The convergence value p of p(τ) of the node i in the time slot interval end,i The specific settings are

[0131] p end,i =p(y i )

[0132] y i It is the time slot distance between the last time slot in the time slot interval of node i (the right boundary of the time slot interval) and the time when the probe data is sent in the previous superframe.

[0133] The convergence speed p of p(τ) of node i in the time slot interval ch,i The specific setting is the parameter P in the node channel model p(τ) ch The value of .

[0134] p ch,i =P ch

[0135] The weight factor represents the degree to which a node should send data first. The larger its value, the earlier the node should send data. i It is positively correlated with the probability convergence value and the probability convergence speed. Whether it is a GOOD node or a BAD node, the larger the initial probability value, the earlier the corresponding node should send data, and the larger the weight factor. For a GOOD node, the faster the probability convergence speed, the faster its p(τ) value decreases, and the earlier the corresponding GOOD node should send data. For a BAD node, the faster the probability convergence speed, the faster its p(τ) value increases to a high probability, which allows the corresponding node to send data earlier and leave the subsequent time slots for those nodes with slow convergence speeds. Similarly, whether it is a GOOD node or a BAD node, the larger the probability convergence value, the later the corresponding node should be able to postpone sending data, and the smaller the corresponding weight factor. Under several different parameters, the p(τ) curves of GOOD nodes and BAD nodes are shown below. Figure 7 , Figure 8 shown.

[0136] The above method is explained in detail below in conjunction with specific embodiments.

[0137] like Figure 1As shown, this embodiment provides a method for allocating time slots in a wireless body area network based on a Markov channel model, which specifically includes the following steps:

[0138] Step S1: Determine the basic communication process of the node.

[0139] The time axis is divided into superframes of equal length. Figure 2 As shown in the figure, each superframe is divided into three parts: beacon phase, medium access phase and channel detection phase. In the beacon phase, the coordinator informs the node of the scheduling control information such as the number of time slots allocated to the node, the transmission time slot position of the node, and the data transmission order of the node by broadcasting Beacon frames. The node obtains the above scheduling control information by receiving the Beacon frame. In the MAP phase, the node sends data to the coordinator in TDMA mode. In the CDP phase, the node sends a probe signal to the coordinator. The coordinator classifies the nodes according to whether the probe signal can be successfully received.

[0140] Step S2: Determine the number of transmission time slots of the sensor node.

[0141] First, the coordinator determines the amount of new data generated by the node and the amount of old data cached according to the node's current data sampling rate and historical data transmission status, thereby determining the basic transmission time slot requirements of each node. Assume that there are eight nodes A, B, C, D, E, F, G, and H in the network. Among them, the priority of A, B, G, and H is 2, and the priority of C, D, E, and F is 1. According to the above principles, the coordinator calculates that the original basic transmission time slot requirements of the eight nodes A, B, C, D, E, F, G, and H are 2, 3, 3, 1, 4, 2, 3, and 2 respectively.

[0142] Next, the coordinator makes further adjustments based on the basic time slot requirements of the nodes and the number of transmission time slots that can be provided in the MAP phase of the superframe, and finally determines the number of time slots allocated to the nodes. Assume that there are only 18 time slots in the MAP phase of the superframe, and the total number of time slots allocated to all nodes is 2+3+3+1+4+2+3+2=20, 20 is greater than 18. At this time, the coordinator first initializes the allocation of the corresponding number of time slots according to the basic transmission time slot requirements of each node, that is, the eight nodes A, B, C, D, E, F, G, and H are first allocated 2, 3, 3, 1, 4, 2, 3, and 2 time slots respectively, and then reduces the number of time slots allocated to the corresponding nodes according to the node priority size and cache saturation.

[0143] For nodes with different priorities, the time slots allocated to low-priority nodes are reduced first. Based on this, the present invention first reduces the time slots allocated to nodes with a priority of 1. For nodes with the same priority, the number of time slots allocated to nodes with low cache saturation is reduced first. Assume that the coordinator calculates that the cache saturation levels of the four nodes C, D, E, and F with a priority of 1 are in the order of C>D>E>F. According to the saturation level relationship, the coordinator first reduces the time slots allocated to the F node. When the time slots allocated to the F node are reduced to only 1 time slot, the coordinator begins to reduce the time slots allocated to the node E with the second smallest cache saturation. When the time slots allocated to the E node are reduced to only 3 time slots, the number of transmission time slots allocated to all nodes is exactly equal to the number of time slots that can be provided in the MAP stage. At this point, the process of reducing time slots ends, and the final number of time slots allocated to the eight nodes A, B, C, D, E, F, G, and H are 2, 3, 3, 1, 3, 1, 3, and 2, respectively.

[0144] Step S3: Classify the sensor nodes.

[0145] Based on the Markov channel model, nodes can be divided into two sets, namely GOOD set and BAD set, according to whether they can successfully send the query data to the coordinator in the CDP stage. Assume that nodes A, B, C, and D are GOOD nodes, while nodes E, F, G, and H are BAD nodes. Combined with the priority of the nodes, all sensor nodes are finally divided into four categories: A and B are GOOD nodes with a priority of 2, C and D are GOOD nodes with a priority of 1, G and H are BAD nodes with a priority of 1, and E and F are BAD nodes with a priority of 2.

[0146] Step S4: Allocate node time slot intervals.

[0147] The coordinator divides the MAP time slot interval according to the total number of time slots allocated to each type of node. Since the final total number of time slots allocated to the eight nodes A, B, C, D, E, F, G, and H is exactly equal to the number of time slots provided in the MAP phase, the MAP part of the superframe is divided into four intervals, namely, Channel-GOOD2 time slot interval, Channel-GOOD 1 time slot interval, Channel-BAD 1 time slot interval, and Channel-BAD 2 time slot interval, as shown in Fig. 9 shown.

[0148] Step S5: Determine the node transmission order.

[0149] The coordinator calculates the initial probability value p of each node in its corresponding time slot interval according to p(τ). start,i , probability convergence speed p ch,i And the probability convergence value p end,i, and then further calculate the weight factor Λ of each node in its respective time slot interval i Then, the coordinator calculates i The size of determines the data transmission order within the time slot interval. For example, if in the time slot interval of A and B, the weight factor of A is A Greater than the weight factor Λ of B B , then in this time slot, node A sends data before B. Similarly, the transmission order of C and D, E and F, and G and H can be determined in the above way.

[0150] In summary, compared with the prior art, the method of this embodiment has the following advantages and beneficial effects:

[0151] (1) The present invention models the channel according to the Markov random process recommended by IEEE 802.15.6, and divides the time slot interval from both ends of the superframe to the middle, which is conducive to transmitting data in the time slot with a high probability of successful transmission of the node, and at the same time does not waste the time slot.

[0152] (2) The present invention uses time slot intervals as units and first allocates time slot intervals with high probability of successful transmission to emergency nodes, thereby ensuring that emergency nodes have a high probability of successful transmission and improving the reliability of emergency data transmission. Existing methods determine the transmission order and time slot position of nodes based on the length of time it takes for the p(τ) value of a node to meet the relevant probability threshold condition, which may cause the emergency node to transmit data in a time slot with a low probability of successful transmission.

[0153] (3) The present invention determines the transmission order of each node in the time slot interval by using weight factors designed based on the initial value, convergence value, and convergence speed of the channel model p(τ). Compared with the existing method of determining the transmission order and time slot position of the node based on the length of time that the p(τ) value of the node meets the relevant probability threshold condition, this method can avoid the time slot allocation conflict problem caused by the limitation of business volume demand. In addition, this method can avoid the problem that the node cannot be allocated to the time slot within the superframe of fixed time length due to the range limitation of the p(τ) value.

[0154] This embodiment also provides a wireless body area network time slot allocation device based on a Markov channel model, comprising:

[0155] at least one processor;

[0156] at least one memory for storing at least one program;

[0157] When the at least one program is executed by the at least one processor, the at least one processor implements Figure 1 The method shown.

[0158] A wireless body area network time slot allocation device based on a Markov channel model in this embodiment can execute a wireless body area network time slot allocation method based on a Markov channel model provided by the method embodiment of the present invention, and can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0159] The present application also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes Figure 1 The method shown.

[0160] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0161] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0162] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0163] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0164] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0165] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0166] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0167] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0168] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A time slot allocation method for a wireless body area network based on a Markov channel model, characterized in that: The following steps are involved: Divide the time axis into superframes of equal length and determine the basic communication flow between nodes and coordinators; Obtain the number of basic transmission time slots of each node, and allocate the number of time slots of the node according to the number of basic transmission time slots; Complete the classification of nodes according to the Markov channel model; Allocate the time slot interval of the node according to the divided categories and the number of time slots of the node; Determine the transmission order of nodes within the time slot interval according to the Markov channel model; The obtaining of the number of basic transmission time slots of each node and allocating the number of time slots of the node according to the number of basic transmission time slots includes: The coordinator determines the amount of new data generated by the node and the amount of cached old data according to the node data arrival rate and the node's historical data transmission status, thereby determining the number of basic transmission time slots required for each node; The coordinator determines the number of time slots allocated to each node based on the node's basic transmission time slot number requirements; The number of time slots is allocated in the following way: If the sum of the basic transmission time slot requirements of all nodes is less than or equal to the preset number of transmission time slots provided, the coordinator allocates the corresponding number of time slots according to the basic transmission time slot requirements of each node; If the sum of the basic transmission time slot requirements of all nodes is greater than the preset number of transmission time slots, the coordinator will first initialize the allocation of the corresponding number of time slots according to the basic transmission time slot requirements of each node, and then reduce the number of time slots allocated to the corresponding node according to the priority size of the node and the cache saturation level; The node classification is completed according to the Markov channel model, including: Based on the Markov channel model, the channel between the segment node and the coordinator is judged to be in good or bad state. If it is in good state, the node is divided into the GOOD set; if it is in bad state, the node is divided into the BAD set; combined with the node priority, GOOD set and BAD set, all nodes are divided into four categories, namely: GOOD nodes with priority 1, GOOD nodes with priority 2, BAD nodes with priority 1 and BAD nodes with priority 2; The allocating time slot intervals of the nodes according to the divided categories and the number of time slots of the nodes comprises: Sum the number of time slots allocated to all nodes of the same category, and get the total number of time slots allocated to GOOD nodes with priority 2, GOOD nodes with priority 1, BAD nodes with priority 1, and BAD nodes with priority 2. The coordinator combines the first time slot with the All time slots between the first time slot are divided into the Channel-GOOD2 time slot interval, and the Channel-GOOD2 time slot interval is allocated to the GOOD node with a priority of 2 for the corresponding node to transmit data; the coordinator divides the first All time slots between the first time slot and the Kth time slot are divided into the Channel-BAD2 time slot interval, and the Channel-BAD2 time slot interval is allocated to the BAD node with a priority of 2 for the corresponding node to transmit data; The coordinator will The time slot and All time slots between the time slots are divided into the Channel-GOOD1 time slot interval, and the Channel-GOOD1 time slot interval is allocated to the GOOD node with a priority of 1 for the corresponding node to transmit data; The coordinator will The time slot and All time slots between the time slots are divided into the Channel-BAD1 time slot interval, and the Channel-BAD1 time slot interval is allocated to the BAD node with a priority of 1 for the corresponding node to transmit data; If the sum of the basic transmission time slot number requirements of all nodes is less than the number of transmission time slots provided in the medium access phase of the superframe, the medium access phase of the superframe is divided into five intervals, namely: Channel-GOOD2 time slot interval, Channel-GOOD1 time slot interval, idle time slot interval, Channel-BAD1 time slot interval and Channel-BAD2 time slot interval; If the sum of the basic transmission time slot number requirements of all nodes is greater than or equal to the number of transmission time slots provided in the medium access phase of the superframe, the medium access phase of the superframe is divided into four intervals, namely: Channel-GOOD2 time slot interval, Channel-GOOD1 time slot interval, Channel-BAD1 time slot interval and Channel-BAD2 time slot interval; Determining the transmission order of the nodes within the time slot interval according to the Markov channel model includes: Based on the Markov channel model, a comprehensive consideration of the initial value p of node i’s p(τ) in the time slot interval is designed. start,i , convergence speed p ch,i , convergence value p end,i The weighting factors of the three factors are i ; According to the weight factor Λ i The value of determines the transmission order of each node in the time slot interval; In the same time slot, the larger the weight factor, the earlier the corresponding node sends data. The weight factor of node i is Λ i The specific settings are: The initial value p of p(τ) of the node i in the time slot interval start,i The specific settings are: p start,i =p(x i ) In the formula, x i is the time slot distance between the first time slot in the time slot interval of node i and the time when the probe data was sent in the previous superframe; The convergence value p of p(τ) of the node i in the time slot interval end,i The specific settings are: p end,i =p(y i ) In the formula, y i is the time slot distance between the last time slot in the time slot interval of node i and the time when the probe data was sent in the previous superframe; The convergence speed p of p(τ) of node i in the time slot interval ch,i The specific setting is the parameter P in the node channel model p(τ) ch The value of p ch,i =P ch 。 2. According to the method for allocating time slots in a wireless body area network based on a Markov channel model in claim 1, it is characterized in that: The basic communication process between the determination node and the coordinator includes: Each superframe is divided into three parts: beacon phase, medium access phase and channel sounding phase; In the beacon phase, the coordinator sends scheduling control information to the node by broadcasting Beacon frames. The scheduling control information includes the number of time slots allocated to the node, the transmission time slot position of the node, and the data transmission order of the node; In the medium access phase, this phase is divided into K transmission time slots with the same time length. Each transmission time slot is divided into a DATA part and an ACK part. In the DATA part, the node transmits data to the coordinator. After the coordinator receives the data, Respond to the Ack frame within the ACK period; In the channel detection phase, this phase is divided into N short probe time slots. Each node sends a probe signal to the coordinator in the probe time slot. The coordinator classifies the nodes according to whether the probe signal can be successfully received. If the coordinator can successfully receive the probe signal, the corresponding node is classified as a GOOD node. Otherwise, the corresponding node is classified as a BAD node.

3. The method for allocating time slots in a wireless body area network based on a Markov channel model according to claim 1, characterized in that: The process of reducing the number of time slots allocated to the corresponding node is as follows: For nodes with different priorities, the number of time slots allocated to low-priority nodes is reduced first; when all low-priority nodes have only one allocated time slot left, the number of time slots allocated to high-priority nodes begins to be reduced; the priority of the nodes is divided into 1 and 2; For nodes with the same priority, the number of time slots allocated to nodes with low cache saturation is reduced first; nodes with the same priority are sorted from large to small according to the cache saturation, and the time slots of the node with the lowest cache saturation are reduced first until the node has only one allocated time slot, and then the time slots of the node with the second lowest cache saturation are reduced until the total number of transmission time slots allocated to all nodes is equal to the preset number of time slots, or all nodes of the current priority have only one time slot left.

4. The method for allocating time slots in a wireless body area network based on a Markov channel model according to claim 1, characterized in that: For node i, the calculation formula for the amount of newly generated data is: In the formula, s i is the average data arrival rate of node i, t SF is the duration of a superframe, b i is the number of bits occupied by a data packet. is the ceiling operator; Combined with the amount of old data cached by node i, the basic transmission time slot number requirement of node i is as follows: In the formula, is the number of time slots required for node i to transmit all new and old data, is the number of packets cached by node i, R i is the data transmission rate of node i, t data It is the length of time occupied by the DATA part of a time slot.

5. The method for allocating time slots in a wireless body area network based on a Markov channel model according to claim 1, characterized in that: The method of judging whether the channel between the segment node and the coordinator is in a good state or a bad state based on the Markov channel model includes: In the Markov channel model, the channel state between the node and the coordinator is divided into two states: good state and bad state. If the node can successfully transmit data to the coordinator, then the channel is in the good state in the current time slot, otherwise it is in the bad state. If the probability that the link between the node and the coordinator is in the good state at a given time slot is denoted as p0, then if it is known that the channel state at a given time slot is good (i.e., p0 = 1) or bad (i.e., p0 = 0), and the human body is in the same activity mode, the probability that the link is still in the good state after τ time slots is: P ch =P GB +P BG Where P BG represents the one-step transition probability of the channel state changing from bad to good, P GB represents the one-step transition probability of the channel state changing from good to bad; P ch The value of represents the convergence speed of p(τ); ch The larger the value of , the shorter the time it takes for p(τ) to converge from a good state or a bad state to a stable value; when the probability p0 is 1, the value of p(τ) shows a monotonically decreasing trend as τ increases; when the probability p0 is 0, the value of p(τ) shows a monotonically increasing trend as τ increases.

6. A time slot allocation device for a wireless body area network based on a Markov channel model, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 5.

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