CN112235846A - Method for realizing energy perception routing of wireless body area network based on fuzzy control - Google Patents
Method for realizing energy perception routing of wireless body area network based on fuzzy control Download PDFInfo
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Abstract
A method for realizing energy perception routing of a wireless body area network based on fuzzy control comprehensively considers a plurality of parameters such as hop count, residual energy and link quality, establishes a fuzzy control model consisting of the residual energy and the link quality of a sensor, and selects an optimal forwarding node and an optimal path for transmitting data to Sink after fuzzification, fuzzy reasoning and defuzzification path benefit calculation. The method proposed herein extends the network lifetime and improves the reliability of data transmission.
Description
Technical Field
The invention belongs to the technical field of wireless body area networks, and particularly relates to a method for realizing energy perception routing of a wireless body area network based on fuzzy control.
Background
With the continuous development of the intelligent health field, attention is paid to a Wireless Body Area Network (WBAN) extended from a Wireless Sensor Network (WSN). WBANs belong to one branch of WSNs, but their unique characteristics and application requirements are different from WSNs. Sensors of WBANs are placed on the body, and therefore, the biocompatibility of the sensors is more demanding than WSNs. In addition, WBANs have unique network characteristics, such as human mobility, and if important physiological data is lost or delayed during transmission due to mobility, the WBAN may pose a huge life threat to patients.
The WBAN is mainly applied to the health medical fields of remote medical treatment, remote monitoring and the like. As shown in fig. 1, a basic architecture of a WBAN is that a plurality of sensor nodes and a single Sink node are deployed on a human body, the sensor nodes are used to sense physiological data of the human body and send the sensed data to the Sink node, the Sink node receives the data, integrates the data, and sends the integrated data to an external network, and a terminal of the network can monitor or diagnose transmitted medical data.
For any network, the routing protocol is designed to solve the data transmission problem in the network, the routing technology is also one of the core technologies of the WBAN, and the routing protocol design in the WBAN still has the unsolved problems of unstable network topology, limited battery energy of the sensor nodes, limited transmission power and the like. In the process of designing a routing protocol, considering that sensor nodes of the WBAN need to have the characteristics of comfort and convenience no matter the sensor nodes are deployed on the body surface or in the body, the small size of the sensor leads to the limited battery capacity, and the energy of the nodes is very deficient. In order to ensure that more data is transmitted under the condition of limited resources, the energy efficiency of the nodes is an important way for prolonging the service life of the network. In addition, WBANs are required to meet different medical scenario applications, for example, real-time reliability of data transmission is very important in emergency medical scenarios, while real-time reliability is relatively less important in general medical scenarios. Different types of data transmissions also differ in their QoS requirements, and therefore the routing design of a WBAN should also meet the QoS requirements of the data transmissions.
Sensors in a WBAN consume the greatest percentage of energy during communication, and therefore, optimizing communication can improve energy efficiency. However, the dynamic nature of WBAN may result in sensors not communicating directly with Sink, so multi-hop communication is more suitable for WBAN energy saving research. The multi-hop communication means that the sensor forwards data to a middle forwarding node, and the data is transmitted to the Sink through the forwarding node. In multi-hop communications, the selection of an appropriate forwarding node is critical to the routing protocol. However, in the existing routing protocol, the shortest path is often considered as a main influencing parameter when calculating the routing cost, and the influence of link quality on preventing data packet loss is ignored, thereby causing high power consumption of WBAN. In addition, to meet the characteristics of WBAN, the calculation of path cost also requires a logical and objective method.
Disclosure of Invention
In order to solve the technical content, the invention provides a method for realizing the energy-aware routing of the wireless body area network based on fuzzy control, which comprehensively considers a plurality of parameters such as hop count, residual energy, link quality and the like, establishes a fuzzy control model consisting of the residual energy of a sensor and the link quality, and selects an optimal forwarding node and an optimal path for transmitting data to Sink after fuzzification, fuzzy reasoning and defuzzification path benefit calculation.
In order to realize the technical purpose, the adopted technical scheme is as follows: the method for realizing the energy-aware routing of the wireless body area network based on the fuzzy control comprises the following steps:
The wireless body area network consists of N sensor nodes and a Sink node, and is based on a network topology structure with the hop number of at most three hops, wherein the Sink node is positioned in the middle of the network topology structure;
Normalizing the residual energy RE by the residual energy E of the sensor noderesEnergy threshold E of the sensor nodethAnd initial energy E of the sensor nodeinitialCalculating to obtain; normalizing link quality LQ minimum signal strength RSSI transmitted by data packetsminAnd signal strength RSSIi,jCalculating to obtain;
3.1, judging whether the sensor node i can be directly transmitted to the Sink node or not, if so, directly transmitting data to the Sink node, and if not, executing the step 3.2;
3.2, selecting the candidate forwarding node with the maximum path benefit value as an optimal forwarding node through path benefit calculation, transmitting the data of the sensor node i to the Sink node by using the optimal forwarding node, and repeating the step 3.2 until all data packets are transmitted to the Sink node;
the specific method for calculating the path benefit comprises the following steps:
step 3.2.1, taking a neighbor node of the sensor node i with the shortest hop count to Sink as a candidate forwarding node j;
step 3.3.2, normalizing residual energy RE of candidate forwarding node jjAnd normalized link quality LQjAnd as an input variable, fuzzifying the input variable to obtain a fuzzy input quantity, obtaining a fuzzy output quantity after the fuzzy input quantity passes through a fuzzy reasoning process, and defuzzifying the fuzzy output quantity to obtain the path benefit.
The method for obtaining the fuzzy input quantity by fuzzifying the normalized residual energy RE and the normalized link quality LQ comprises the steps of quantizing the normalized residual energy RE and the normalized link quality LQ into 3 levels { L, M, H }, wherein the three levels are low, medium and high, { L, M, H }, RE is [0,1], LQ is [0,1], and membership functions corresponding to the normalized residual energy RE and the normalized link quality LQ are triangular membership functions and establishing membership function expressions of the residual energy.
The fuzzy reasoning process comprises the following steps: a group of fuzzy rules are preset according to the relation between fuzzy input and fuzzy output by adopting a Mamdani type fuzzy reasoning method, and the reasoning calculation from input to output is realized; dividing the sensor data into periodic data and urgent data, satisfying periodic data fuzzy rules and urgent data fuzzy rules, quantizing fuzzy output quantity into { VL, L, M, H, VH }, { VL, L, M, H, VH } and expressing as five levels of very low, medium, high and very high;
the fuzzy rule of the periodic data is
The fuzzy rule of the urgent data is
The calculation method of the normalized residual energy RE comprises the following steps
Wherein E isresIs the residual energy, E, of the sensor nodethEnergy threshold for sensor node, EinitialIs the initial energy of the sensor node.
The method for calculating the normalized link quality LQ comprises the following steps
Wherein the RSSIminFor minimum signal strength, RSSI, of packet transmissioni,jIs the signal strength.
The invention has the beneficial effects that: the method considers the mobility of the sensor node, establishes a fuzzy control model consisting of the residual energy of the sensor and the link quality, and determines the optimal forwarding node and the optimal transmission path for transmitting data to the Sink after the fuzzification, fuzzy reasoning and defuzzification processes. Simulation analysis shows that compared with the performance of the existing EERDT and M-TSIMPLE protocols, the method provided by the invention prolongs the service life of the network and improves the reliability of data transmission.
Drawings
FIG. 1 is a basic architecture diagram of a wireless body area network;
FIG. 2 is a diagram of a network topology of the present invention;
FIG. 3 is a diagram of the fuzzy control process of the present invention;
FIG. 4 is a graph of membership functions for input variables of the present invention;
FIG. 5 is a graph of membership functions for output variables of the present invention;
FIG. 6 is a flow chart of a method of the present invention;
FIG. 7 is a network lifetime analysis diagram of the present invention;
FIG. 8 is an energy efficiency analysis graph of the present invention;
FIG. 9 is a throughput diagram according to the present invention;
FIG. 10 is a graph of packet delivery rate analysis according to the present invention.
Detailed Description
A method for realizing energy perception routing of a wireless body area network based on fuzzy control comprehensively considers a plurality of parameters such as hop count, residual energy and link quality, establishes a fuzzy control model consisting of the residual energy and the link quality of a sensor, and selects an optimal forwarding node and an optimal path for transmitting data to Sink after fuzzification, fuzzy reasoning and defuzzification path benefit calculation.
1. Establishing a wireless body area network model
A Wireless Body Area Network (WBAN) considered herein is composed of N sensor nodes and a Sink node, and the Sink node is located at the waist of the human body, i.e., the middle of the network topology, as shown in fig. 2, which is a network topology structure diagram. The sensor nodes are deployed on the surface of a human body and are mainly responsible for monitoring data, and the acquired data are directly forwarded to the Sink or forwarded through the forwarding nodes. The Sink node is composed of mobile intelligent devices, is a hub for connecting the WBAN with an external network, and is mainly responsible for forwarding data. Two types of nodes are also defined herein, static and mobile nodes respectively. The static nodes represent sensor nodes placed at positions unrelated to the movement of the human body, while the mobile nodes represent sensors placed at limbs of the human body, and the mobile nodes have the characteristic of storing and forwarding data.
1.1 model assumptions:
(1) all sensor nodes are placed at different positions of a human body, acquire corresponding physiological parameter information and have specific IDs.
(2) All sensor nodes have the same initial energy and transmission range, and the maximum transmission range is lmax。
(3) The Sink node has strong information processing capacity, only receives data from the sensor node, does not generate data by itself, and does not consider the energy of the Sink.
(4) Due to the dynamic nature of WBANs, a one-hop based star network topology does not guarantee reliable transmission of data. Thus, a multi-hop network topology is employed herein, and the number of hops is at most three hops.
Because the sensor nodes are deployed at different positions of a human body and the sensed physiological data have different importance degrees, the physiological information (transmission data) can be divided into periodic data and emergency data, the two data are divided into priority, and the priority of the emergency data is higher than that of the periodic data.
Periodic data: refers to periodically transmitted data generated by the sensor nodes. Data can be periodically transmitted to the Sink node, the traffic is large, and the real-time requirement on data transmission is not high.
Emergency data: refers to abnormal data that exceeds normal health values and is non-periodic data generated by the sensor nodes. Such data traffic is small, but since the data value is at an abnormal level, if the data cannot be effectively transmitted, the data is likely to harm the life and health of human bodies, and thus, the requirements on real-time and reliable transmission of such data are high.
1.2 energy loss model
In WBAN, each sensor node generates energy consumption in the data sensing, processing, and transmission processes, but the energy consumption in the data transmission process is the greatest proportion, so that the energy consumption of the nodes in the data transmission process is mainly analyzed, and the energy consumption is calculated by using a first-order radio model, and the formula is as follows:
Etx(n,d)＝Etx-elec(n)+Eamp(n,d)
Etx(n,d)＝Etx-elec×n+Eamp×n×d2 (1)
Erx(n)＝Erx-elec(n)
Erx(n)＝Erx-elec×n (2)
where n is the size of the data packet and d is the distance between the transmitting end and the receiving end. Etx(n, d) energy consumption by internal circuits Etx-elecEnergy consumption E of the sum amplifier circuitampComposition, which represents the energy consumed to transmit data.
Erx(n) represents the energy consumed for receiving data, and the receiving end does not need to amplify the signal, so that only the energy consumption E of the internal circuit is consideredrx-elec。
2. Method for realizing route
2.1 selection of parameters
Parameters such as residual energy, link quality and hop count are mainly considered herein. Since the sensors in a WBAN have limited power, the remaining power of the nodes needs to be considered when selecting forwarding nodes in order to reduce power consumption in the network. In addition, due to the dynamic characteristics of WBAN, data loss and delay are easily caused during data transmission, and link quality and hop count need to be considered for the selected forwarding node to ensure timeliness and reliability of data transmission.
The energy obtained by subtracting the consumed energy from the initial energy of the sensor node is represented, and the calculation formula of the residual energy is as follows.
Eres＝Einitial-∑[Etx(n,d)+Erx(n)] (3)
EresRepresenting the remaining energy of the sensor node, EinitialRepresenting the initial energy of the sensor node, Etx(n, d) represents energy consumption of the sensor node to transmit data, ErxAnd (n) represents energy consumption of the sensor node for receiving data.
The RE normalization process is calculated as follows.
RE represents the normalized residual energy of the sensor node, EinitialIs the initial energy of the sensor node, EthIs the energy threshold.
Normalized link quality LQ: the link quality depends on the signal strength (RSSI), RSSIi,jThe calculation formula of (c) is as follows.
Wherein, PrxExpressed as received power, PtxDenoted as transmit power. The link quality parameter is normalized, and LQ is the normalized link quality between node i (transmission node) and node j (forwarding node) as shown in formula (5), wherein RSSIminIndicating the minimum signal strength of the data packet transmission.
Hop count H: refers to the hop value from the node to the Sink. In the network initialization phase, hop count is updated by exchanging HELLO packets between nodes.
2.2 calculation of Path benefit
The energy-aware routing (EARP) of the wireless body area network selects the candidate forwarding node with the maximum path benefit value as the optimal forwarding node by calculating the path benefit value of the candidate forwarding node. And establishing a fuzzy control model by utilizing the residual energy and the link quality of the candidate forwarding nodes, and calculating the path benefit value through the fuzzification, fuzzy reasoning and defuzzification processes in the model. The fuzzy control process is shown in figure 3. The residual energy RE and the link quality LQ are input variables, fuzzification is carried out on the input variables to obtain fuzzy input quantities RE and LQ, the fuzzy input quantities are subjected to a fuzzy reasoning process to obtain fuzzy output quantities PB, and defuzzification is carried out on the PB to obtain path benefits PB.
2.2.1 blurring
Both the input variable and the output variable belong to control variables for performing fuzzy control, however, neither the input variable nor the output variable can be directly used to perform fuzzy control, and therefore, the control variables must be subjected to fuzzification processing.
Firstly, the input variable is fuzzified, and the fuzzified language adopts { L, M, H } (low, middle and high) to represent the fuzzified result. The more fuzzy language permutations are made on the input variables, the more fuzzy rules are made, resulting in increased computation time, and therefore RE is quantized to { L, M, H }3 levels, and LQ is also quantized to { L, M, H }. Since the residual energy and the link quality have been normalized, the domain of residual energy RE is [0,1], and the domain of link quality is LQ [0,1 ]. Each fuzzy language has a corresponding membership function, in this document, the residual energy and the link quality adopt triangular membership functions, the membership functions represent the membership degree of fuzzy variables to a fuzzy set, and the closer the numerical value is to 1, the higher the membership degree of the fuzzy variables is. And establishing a membership function expression of the residual energy according to the fuzzy domain, wherein the membership function expression is shown in formulas (7) to (9), and the membership function expression of the link quality is shown in formulas (10) to (12).
Wherein, muL(RE) represents a membership function for RE belonging to L, μM(RE) represents a membership function of RE to M, μH(RE) represents a membership function for RE belonging to H, μL(LQ) denotes the membership function, μ, of LQ to LM(LQ) represents the membership function, μ, of LQ belonging to MH(LQ) represents LQMembership functions belonging to H. The expression principle of the membership functions means that the membership functions taken by the variables should be reasonably distributed in the domain of discourse, namely, the function curves of the membership functions cover the whole domain of discourse and are symmetrically balanced.
Because the membership functions of the residual energy and the link quality are the same as the domain of discourse, a membership function curve of the input variable is drawn according to the membership function expression, as shown in fig. 4.
The output variables also need to be fuzzified, and a method similar to the input variables is adopted, and the specific process is as follows.
The argument of the output variable is PB ═ 0,1, PB quantized { VL, L, M, H, VH } (very low, medium, high, very high), using a triangular membership function. The fuzzy language arrangement of the output variables is 5 levels, which not only can uniformly cover the whole discourse domain, but also can improve the resolution of the model and make the output smoother. Therefore, the expression of the membership function of the output variable is shown in equations (13) to (17), and the curve of the membership function of the output variable is shown in fig. 5.
μVL(PB) represents a membership function of PB belonging to VL, μL(PB) represents a membership function of PB belonging to L, μM(PB) represents a membership function of PB belonging to M, μH(PB) represents a membership function of PB as H, μVH(PB) represents a membership function for which PB belongs to VH.
2.2.2 fuzzy inference
After the input and output variables are fuzzified, the fuzzy reasoning process is carried out, a group of fuzzy rules are preset according to the relation between fuzzy input and output quantities by adopting a Mamdani type fuzzy reasoning method, and the reasoning calculation from input to output is realized. The fuzzy rule is usually formulated by a set of fuzzy conditional statements of If-Then structure, for example:
If(RE＝H)&(LQ＝L)Then Output＝H
If(RE＝M)&(LQ＝L)Then Output＝M
fuzzy rules affect the magnitude of the path benefit value. In this context, depending on the different emphasis on the required residual energy and link quality by the periodic data and the urgent data, a corresponding fuzzy rule is formulated to perform inference, for example, the periodic data emphasizes the residual energy of the node, and the urgent data emphasizes the link quality of the node. As shown in table 1, the fuzzy rule of the periodic data is defined, and table 2 is defined for the fuzzy rule of the urgent data.
TABLE 1 fuzzy rules for periodic data
TABLE 2 fuzzy rules for urgent data
2.2.3 Defuzzification
After the fuzzy reasoning process is finished, fuzzy output quantity obtained by reasoning according to periodic data or an emergency data rule can be obtained, no matter which fuzzy output quantity needs defuzzification, path benefit can be obtained, a gravity center method is adopted as a defuzzification method, the gravity center method is calculated by taking the gravity center of an area surrounded by a membership function curve, and the method can output a reasoning control value more smoothly.
Therefore, the fuzzy output quantity obtained by fuzzy inference is defuzzified by using a gravity center method, and the path benefit value of each candidate forwarding node is finally obtained.
2.3 routing procedures
The routing protocol proposed herein is divided into three phases: the initialization phase, the selection phase and the data transmission phase of the forwarding node, and the routing process are shown in fig. 6.
2.3.1 initialization phase
Each sensor node broadcasts information of a Hello Packet (HP) to its surrounding nodes periodically, and any node establishes or updates Neighbor Table (NT) information after receiving the HP from other neighbor nodes. The information of the Hello packet includes the ID of the node, the data type DT, the remaining energy RE, the link quality LQ, and the hop count H.
In order to ensure stable connection of links and avoid misleading of outdated information, each node must update current state information and neighbor node state information in time. A neighbor table is maintained in the sensor node and is established or updated by relying on Hello packets exchanged among the collection nodes. The algorithm for establishing and updating the neighbor table is shown in algorithm 1.
2.3.2 selection phase of optimal Forwarding node
If the transmission range of the node i does not support the direct transmission of the data to the Sink node, a proper forwarding node needs to be selected from the neighbor nodes to transmit the data. And determining the range of the candidate forwarding nodes according to the information in the neighbor table, and selecting the optimal forwarding node from the candidate forwarding nodes. The candidate forwarding node is composed of neighbor nodes with the shortest hop count to Sink in the neighbor node set.
The principle of selecting the optimal forwarding node is to establish a fuzzy control model consisting of residual energy and link quality in the candidate nodes, calculate corresponding PB of the model when the model transmits emergency data or periodic data, compare the calculated PB in the candidate forwarding nodes, and select the candidate forwarding node with the maximum PB as the optimal forwarding node to forward the data.
The selection process of the forwarding node is shown in algorithm 2.
2.3.3 data transfer phase
Once the best forwarding node is selected, transmission of data begins. The source node sends data to the selected optimal forwarding node and then repeats the steps until the data packet is transmitted to the Sink.
The transmission paths of the periodic data and the emergency data are not completely the same, and if the nodes bearing the periodic data are the same as the forwarding nodes selected by the nodes bearing the emergency data, the emergency data are forwarded preferentially based on a priority principle in the transmission process. If the residual energy of the sensor is lower than the threshold level, only the data which needs to be transmitted by the sensor is transmitted.
3 simulation results and analysis
3.1 simulation Environment and parameters
It is contemplated herein to deploy 10 biosensors and 1 sink node on a human body, as shown in fig. 2. The EARP uses MATLAB2017b to simulate and evaluate its performance in comparison with the existing routing protocols EERDT and M-TSIMPLE. Both existing protocols and the EARP proposed herein are based on the way in which forwarding nodes transmit data. The EARP performs performance evaluation according to the proposed model calculation method and parameter setting, and refers to the simulation parameters of Nordic nRF2401, which is a common low-power consumption single-chip transceiver for human body sensor networks, and the specific parameter values are shown in table 3. And analyzing the performance of the EARP by using performance indexes such as network life, residual energy, throughput, data packet delivery rate and the like.
TABLE 3 simulation parameters
3.2 simulation analysis
Network lifetime is expressed as the time from network startup to the death of the last node, and network stability is the time from network startup to the death of the first node. FIG. 7 shows a comparison of EARP with EERDT and M-TSIMPLE in terms of network lifetime. EARP is compared with EERDT and M-TSIMPLE respectively, and found that EARP appears as the first dead node in the 6300 round, while EERDT and M-TSIMPLE are 5731 round and 4200 round respectively; the last node death of EARP occurred in round 7670, while EERDT and M-TSIMPLE occurred in round 7450 and 7400, respectively.
For a complex WBAN, too many parameters are considered, and it is often difficult to correctly describe the dynamic characteristics of the network, and the incorporation of the fuzzy control model in the EARP can better adapt to the characteristics of the WBAN. In addition, the EARP uses the fuzzy control model to perform the calculation of the path benefits on a plurality of parameters, and the calculation complexity is higher than that of the other two protocols, which increases the calculation time and the corresponding energy consumption. However, the performance of the EARP is better than that of the existing two protocols in terms of the network lifetime, mainly because the EARP refers to the link quality when selecting the best forwarding node, and the reliability of the selected path is higher than that of the EERDT and the M-TSIMPLE, which avoids the energy consumed by retransmission after data loss. The network topology of the EERDT is stable, only residual energy is considered when a path is established, and a single-hop mode is adopted to transmit emergency data so as to balance the network energy utilization rate. Therefore, its network lifetime is higher compared to M-TSIMPLE. However, the mobility of the reference human body of M-TSIMPLE designs a route, and data transmission fails due to unstable network topology and no consideration of link quality, so that a data retransmission process occurs, and retransmission consumes a large amount of energy of nodes.
Using the energy efficiency of the residual energy analysis network, fig. 8 depicts the energy consumption of EARP versus EERDT and M-TSIMPLE as the average residual energy of the entire WBAN gradually decreases as the number of rounds increases. As shown in FIG. 8, EARP consumed too much energy at round 3310, while EERDT and M-TSIMPLE remained too much energy at rounds 2600 and 1800, respectively.
Analysis shows that the energy consumption of the EARP is less than that of EERDT and M-TSIMPLE, and the reason is that the EARP utilizes a fuzzy control model to synthesize various parameters to quickly establish a routing path, and plans corresponding transmission paths according to data types respectively to balance the energy consumption of the network. In addition, by dividing the data priority in the data transmission process, redundant data packets can be effectively avoided, and the energy efficiency is improved. EERDT selects forwarding nodes to establish a routing path in a cluster-based mode, and the initial energy of a cluster head is the same as that of other sensor nodes, which causes the cluster head to rapidly consume the energy of the cluster head due to being selected as the forwarding nodes for multiple times. In addition, the EERDT respectively transmits emergency data and normal data by adopting a mode of single hop and multi-hop, while the M-TSIMPLE does not divide the data type for data transmission, and the network topology of the M-TSIMPLE is unstable and is easy to cause data retransmission, which can cause excessive consumption of node energy, so that the energy efficiency of the EERDT is lower than that of the EERDT.
The throughput represents the total number of data packets successfully received by the Sink node, and the throughput varies depending on the number of surviving sensors in the network, as shown in fig. 9, which is an analysis of throughput for EARP, EERDT and M-TSIMPLE. As the number of iteration rounds increases, the longer the network settling period, the greater the packet delivery throughout the network. Compared with the existing two protocols, the EARP has obvious advantages in terms of throughput, adopts a fuzzy control model to realize rapid selection of forwarding nodes, preferentially establishes a routing path, and can improve the throughput of the whole WBAN. Furthermore, EARP has the longest network lifetime compared to EERDT and M-TSIMPLE, and the longer the sensor lifetime, the more packets can be sent and received. The throughput of the EERDT is relatively higher than that of the M-TSIMPLE, because the EERDT transmits data in a mode of combining single hop and multi-hop, so that the stability performance of a link is better, and the network stability period is longer than that of the M-TSIMPLE.
The data packet delivery rate is a key parameter for measuring the reliability of the routing protocol, and the data packet delivery rate represents the ratio of the number of data packets sent by the source node to the number of data packets received by the Sink node. With the increase of the number of iterations, the number of dead nodes will also increase, thereby affecting the delivery rate of the data packets, as shown in fig. 10, which is an analysis of the EARP, the EERDT and the M-TSIMPLE in terms of the delivery rate of the data packets. The EARP has a higher data packet delivery rate than the EERDT and the M-TSIMPLE because the EERDT and the M-TSIMPLE take the link quality parameter into account when establishing an effective routing path, but neither the EERDT nor the M-TSIMPLE take the influence of the link quality parameter on the reliability of the WBAN into account, and the EARP transmits according to the data priority during data transmission, and data is transmitted in order to avoid data packet loss when the available nodes are reduced. EERDT has better performance in terms of packet delivery rate than M-TSIMPLE because the number of available nodes is reduced when the number of dead nodes is increased, EERDT does not analyze the dynamic characteristics of the network, and data transmission is more stable than that of M-TSIMPLE. And the M-TSIMPLE considers the dynamic characteristics of the network, the communication link change between the sensor nodes causes the link state to be unstable, and the data packet is easy to lose in the transmission process.
4 conclusion
In order to reduce the energy consumption of WBANs and to guarantee the real-time reliability of their data transmission, an Energy Aware Routing Protocol (EARP) based on fuzzy control is proposed herein. The protocol establishes a fuzzy control model consisting of residual energy of a sensor and link quality, and obtains an optimal forwarding node for forwarding data through fuzzification, fuzzy reasoning and defuzzification processes. Simulation analysis shows that compared with the performances of the existing EERDT and M-TSIMPLE protocols, the EARP protocol provided by the method is good in network life, residual energy, throughput, packet delivery rate and other performances.
In the future, a complex mobile model can be introduced to explore the influence of human body mobile diversity on the performance of a routing protocol and further optimize the overall network performance of the WBAN.
Claims (5)
1. The method for realizing the energy perception routing of the wireless body area network based on the fuzzy control is characterized in that: the method comprises the following steps:
step 1, establishing a wireless body area network model
The wireless body area network consists of N sensor nodes and a Sink node, and is based on a network topology structure with the hop number of at most three hops, wherein the Sink node is positioned in the middle of the network topology structure;
step 2, designing normalized residual energy RE and normalized link quality LQ of sensor nodeNormalized residual energy RE is derived from residual energy E of the sensor noderesEnergy threshold E of the sensor nodethAnd initial energy E of the sensor nodeinitialCalculating to obtain; normalizing link quality LQ minimum signal strength RSSI transmitted by data packetsminAnd signal strength RSSIi,jCalculating to obtain;
step 3, transmitting the data packet of the sensor node i to the Sink node
3.1, judging whether the sensor node i can be directly transmitted to the Sink node or not, if so, directly transmitting data to the Sink node, and if not, executing the step 3.2;
3.2, selecting the candidate forwarding node with the maximum path benefit value as an optimal forwarding node through path benefit calculation, transmitting the data of the sensor node i to the Sink node by using the optimal forwarding node, and repeating the step 3.2 until all data packets are transmitted to the Sink node;
the specific method for calculating the path benefit comprises the following steps:
step 3.2.1, taking a neighbor node of the sensor node i with the shortest hop count to Sink as a candidate forwarding node j;
step 3.3.2, normalizing residual energy RE of candidate forwarding node jjAnd normalized link quality LQjAnd as an input variable, fuzzifying the input variable to obtain a fuzzy input quantity, obtaining a fuzzy output quantity after the fuzzy input quantity passes through a fuzzy reasoning process, and defuzzifying the fuzzy output quantity to obtain the path benefit.
2. The method for implementing the fuzzy control-based wireless body area network energy-aware routing as claimed in claim 1, wherein the method for fuzzifying the normalized residual energy RE and the normalized link quality LQ to obtain the fuzzy input quantity comprises quantizing the normalized residual energy RE and the normalized link quality LQ into { L, M, H }3 levels, { L, M, H } representing three levels of low, medium and high, RE ═ 0,1], LQ ═ 0,1], and establishing the membership function expression of the residual energy, wherein the membership functions corresponding to the normalized residual energy RE and the normalized link quality LQ are triangle membership functions.
3. The method for implementing fuzzy control based wireless body area network energy-aware routing of claim 2, wherein the fuzzy inference process is: a group of fuzzy rules are preset according to the relation between fuzzy input and fuzzy output by adopting a Mamdani type fuzzy reasoning method, and the reasoning calculation from input to output is realized; dividing the sensor data into periodic data and urgent data, satisfying periodic data fuzzy rules and urgent data fuzzy rules, quantizing fuzzy output quantity into { VL, L, M, H, VH }, { VL, L, M, H, VH } and expressing as five levels of very low, medium, high and very high;
the fuzzy rule of the periodic data is
The fuzzy rule of the urgent data is
4. The method of claim 1, wherein the method for implementing fuzzy control based wireless body area network energy aware routing comprises: the calculation method of the normalized residual energy RE comprises the following steps
Wherein E isresIs the residual energy, E, of the sensor nodethEnergy threshold for sensor node, EinitialIs the initial energy of the sensor node.
5. The method of claim 1, wherein the method for implementing fuzzy control based wireless body area network energy aware routing comprises: the method for calculating the normalized link quality LQ comprises the following steps
Wherein the RSSIminFor minimum signal strength, RSSI, of packet transmissioni,jIs the signal strength.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115568039A (en) * | 2022-09-30 | 2023-01-03 | 青岛科技大学 | Data acquisition method considering data emergency degree in underwater wireless sensor network |
| CN115578879A (en) * | 2022-09-14 | 2023-01-06 | 江苏天一航空工业股份有限公司 | Vehicle optimal path navigation control method and system based on Internet of things |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060167784A1 (en) * | 2004-09-10 | 2006-07-27 | Hoffberg Steven M | Game theoretic prioritization scheme for mobile ad hoc networks permitting hierarchal deference |
| CN102158927A (en) * | 2011-05-20 | 2011-08-17 | 北京邮电大学 | Efficient inhomogeneous clustering routing method of wireless sensor network |
| US20120016974A1 (en) * | 2010-07-14 | 2012-01-19 | Domanicom Corporation | Business process utilizing systems, devices and methods engendering cooperation among service providers to maximize end user satisfaction |
| CN104853365A (en) * | 2015-05-11 | 2015-08-19 | 浙江理工大学 | Wireless sensing networks topology construction method based on lossy link state prediction |
| CN106921998A (en) * | 2017-01-06 | 2017-07-04 | 河南农业大学 | A kind of energy mix is powered the access control system and method for mobile communications network |
| CN107666439A (en) * | 2016-07-29 | 2018-02-06 | 中国人民解放军陆军军官学院 | A kind of wireless senser method for routing and system |
| CN108770029A (en) * | 2018-05-02 | 2018-11-06 | 天津大学 | Cluster-Based Routing Protocols for Wireless Sensor based on cluster and fuzzy system |
| CN110062432A (en) * | 2019-04-26 | 2019-07-26 | 长春师范大学 | A kind of Wireless sensor network clustering routing algorithm based on least energy consumption |
-
2020
- 2020-09-04 CN CN202010922794.2A patent/CN112235846B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060167784A1 (en) * | 2004-09-10 | 2006-07-27 | Hoffberg Steven M | Game theoretic prioritization scheme for mobile ad hoc networks permitting hierarchal deference |
| US20120016974A1 (en) * | 2010-07-14 | 2012-01-19 | Domanicom Corporation | Business process utilizing systems, devices and methods engendering cooperation among service providers to maximize end user satisfaction |
| CN102158927A (en) * | 2011-05-20 | 2011-08-17 | 北京邮电大学 | Efficient inhomogeneous clustering routing method of wireless sensor network |
| CN104853365A (en) * | 2015-05-11 | 2015-08-19 | 浙江理工大学 | Wireless sensing networks topology construction method based on lossy link state prediction |
| CN107666439A (en) * | 2016-07-29 | 2018-02-06 | 中国人民解放军陆军军官学院 | A kind of wireless senser method for routing and system |
| CN106921998A (en) * | 2017-01-06 | 2017-07-04 | 河南农业大学 | A kind of energy mix is powered the access control system and method for mobile communications network |
| CN108770029A (en) * | 2018-05-02 | 2018-11-06 | 天津大学 | Cluster-Based Routing Protocols for Wireless Sensor based on cluster and fuzzy system |
| CN110062432A (en) * | 2019-04-26 | 2019-07-26 | 长春师范大学 | A kind of Wireless sensor network clustering routing algorithm based on least energy consumption |
Non-Patent Citations (4)
| Title |
|---|
| S SUGANYA: "Optimization and lifetime anticipation for WSN using fuzz logic", 《IEEE》 * |
| 刘超: "无线传感器网络模糊路由算法", 《中国硕士论文全文数据库》 * |
| 张强宇等: "一种基于模糊控制的无线传感器网络拓扑控制算法", 《计算机工程与科学》 * |
| 金鑫: "一种可靠传输的WSNs能耗均衡路由协议", 《传感器与微系统》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115578879A (en) * | 2022-09-14 | 2023-01-06 | 江苏天一航空工业股份有限公司 | Vehicle optimal path navigation control method and system based on Internet of things |
| CN115578879B (en) * | 2022-09-14 | 2023-12-08 | 江苏天一航空工业股份有限公司 | Vehicle optimal path navigation control method and system based on Internet of things |
| CN115568039A (en) * | 2022-09-30 | 2023-01-03 | 青岛科技大学 | Data acquisition method considering data emergency degree in underwater wireless sensor network |
| CN115568039B (en) * | 2022-09-30 | 2023-08-04 | 青岛科技大学 | Data acquisition method considering data emergency degree in underwater wireless sensor network |
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