Wireless sensor network robust routing protocol method and device based on game theory and particle swarm optimization algorithm

Abstract

The invention discloses a method and a device for a wireless sensor network robust routing protocol based on a game theory and a particle swarm optimization algorithm, and proposes that cluster heads are elected by adopting the game theory and the particle swarm optimization algorithm, and wireless sensor network routing which is low in energy consumption, long in service life and high in robustness is realized in combination with a fault sensor reutilization strategy. The method comprises the following steps: A, deducing a theoretical optimal cluster head number according to a network model and a communication energy consumption model; b, selecting candidate cluster heads from the sensors by adopting a non-cooperative game method; c, determining a final cluster head by adopting a particle swarm optimization algorithm; d, the sensors are clustered nearby, and the network enters a stable data transmission stage; and E, deducing a fault sensor reutilization strategy, and when a sensor with a soft permanent fault exists, taking the fault sensor as a relay node to realize wireless sensor network routing with low energy consumption, long service life and high robustness.

Description

Technical Field

[0001] The present invention belongs to the technical field of routing protocols, and particularly relates to a method and device for a robust routing protocol for wireless sensor networks based on game theory and particle swarm optimization algorithm. Background Art

[0002] Structural health monitoring (SHM) can deeply analyze the structural performance and is one of the key technologies to ensure the safety of civil engineering structures. Compared with traditional wired monitoring networks, wireless sensor networks (WSNs) have been widely used in the SHM field due to their low cost and flexible installation methods, and provide effective monitoring data for structural state evaluation. However, due to the influence of energy consumption and harsh environmental conditions such as high temperature and corrosion, sensor nodes may run out of energy or malfunction, thus affecting network stability and lifespan. Therefore, how to extend the network lifecycle has become one of the key technologies in WSN research.

[0003] Data transmission and reception are the main sources of energy consumption of sensor nodes, and network routing protocols mainly focus on the paths of data transmission and reception. Therefore, designing an efficient routing protocol is crucial for reducing node energy consumption. Cluster routing protocols have become the focus of WSN research due to their good scalability and low energy consumption characteristics. In existing cluster routing protocols, sensor nodes are divided into multiple clusters, and each cluster is coordinated by a cluster head to monitor the tasks of cluster members. After receiving and fusing the monitoring data of cluster members, it forwards the data to the base station or sink node. Although these cluster routing protocols have improved the network lifespan to a certain extent, they still do not fully consider factors such as the remaining energy of sensors and the impact of sensor failures on the network, resulting in uneven node energy consumption distribution and high overall network energy consumption. Therefore, it is necessary to develop a robust routing protocol that can comprehensively consider the influence of factors such as remaining energy, coverage rate, and node degree, and has a low-cost fault tolerance ability to extend the network lifespan of WSNs. Summary of the Invention

[0004] The present invention aims to overcome the above deficiencies of the prior art and provides a method and device for a robust routing protocol for wireless sensor networks (FTRP-GPSO) based on game theory and particle swarm optimization algorithm to reduce the energy consumption of WSNs, extend the network lifespan, and enhance network robustness.

[0005] The first aspect of the present invention includes a method for a robust routing protocol for wireless sensor networks based on game theory and particle swarm optimization algorithm, comprising the following steps:

[0006] A. Deduce the theoretical optimal number of cluster heads according to the network model and communication energy consumption model;

[0007] B. Elect candidate cluster heads from sensors using non-cooperative game methods;

[0008] C. Determine the final cluster heads using the particle swarm optimization algorithm;

[0009] D. Sensors form clusters in the vicinity, and the network enters the stable data transmission stage;

[0010] E. Derive the reuse strategy for faulty sensors (RFN). When there are sensors with soft permanent faults, regard the faulty sensors as relay nodes to achieve a low - energy - consumption, long - lifespan, and highly robust routing for the wireless sensor network.

[0011] Furthermore, step A specifically includes:

[0012] A1. Obtain relevant parameters of the monitoring network. Assume the number of sensors in the monitoring network is N, and the size of the monitoring area is m×n;

[0013] A2. Calculate the energy consumption of cluster members \(E_{cm}\), the energy consumption of cluster heads \(E_{ch}\), the energy consumption of the cluster group \(E_{cg}\), and the energy consumption of the network \(E_{n}\) according to the communication energy consumption model. The calculation formulas are as follows: CM \(E_{cm}\) CH \(E_{ch}\) Cluster \(E_{cg}\) network \(E_{n}\)

[0014]

[0015] In the formula: l is the length of the data packet (number of bits); k is the number of cluster heads; \(d_{i}\) toCH is the distance from the cluster member node to the cluster head; \(d_{0}\) toSN is the distance from the cluster head to the base station; \(\epsilon_{fs}\) elec is the energy consumption for sending or receiving each bit of data; \(\epsilon_{mp}\) fs is the energy consumption coefficient in the free - space model; \(\epsilon_{da}\) mp is the energy consumption coefficient in the multipath fading model; \(\epsilon_{f}\) DA is the energy consumption coefficient for data fusion;

[0016] A3. With the goal of minimizing the network energy consumption, derive the theoretical optimal number of cluster heads k according to the above formula. The calculation formula is:

[0017]

[0018] A4. Substitute the relevant parameters of the monitoring network into formula (2) to solve for the theoretical optimal number of cluster heads k.

[0019] Furthermore, step B specifically includes:

[0020] B1. Model the candidate cluster head selection problem as a clustering game model CG = {N, S, U}, where N represents the set of sensor nodes participating in the game, S = {H, NH} represents the strategy space of sensor nodes. The strategy H means the sensor becomes a cluster head, and the strategy NH means the sensor becomes a cluster member;

[0021] B2. When all sensors select the strategy NH, they directly send data to the base station, resulting in excessive energy consumption, so the utility is 0; when sensor i selects the strategy H to become a cluster head, its utility is defined as The utility of other cluster member nodes is defined as

[0022]

[0023] B3. Define the probability that sensor i selects the strategy H as p, and calculate the Nash equilibrium probability p of the mixed strategy; combine the remaining energy, node degree, and node importance to give the probability p that sensor i becomes a candidate cluster head i :

[0024]

[0025] In the formula: is the number of neighbor nodes; is the remaining energy; I i is the node importance; d toSN is the distance from the cluster head to the base station; α, β, and γ are control parameters whose sum is 1; ω is the amplification factor;

[0026] B4. Each sensor plays a game with its adjacent nodes and calculates the probability p that it becomes a cluster head i , and generates a random number rand i ∈[0,1];

[0027] B5. If rand i ≤p i , sensor i becomes a candidate cluster head and joins the candidate cluster head set C, otherwise sensor i becomes a cluster member.

[0028] Furthermore, step C specifically includes:

[0029] C1. According to the coverage rate C f , the remaining energy E f and the importance I f Define the objective function F of the particle swarm optimization algorithm as:

[0030]

[0031] In the formula: η, λ, and δ are control parameters whose sum is 1;

[0032] C2. Use the particle swarm optimization algorithm to solve the objective function F, and elect the final cluster head close to the theoretical optimal number k of cluster heads from the candidate cluster head set C.

[0033] Furthermore, step D specifically includes:

[0034] D1. According to the cluster head election results, the sensors that do not become cluster heads join the nearer cluster heads and become cluster members;

[0035] D2. The cluster head collects and collates the monitoring data collected by the cluster members and transmits it to the base station;

[0036] D3. After a period of time, the current round ends and a new cluster head is elected in the next round.

[0037] Furthermore, step E specifically includes:

[0038] E1. Calculate the communication energy consumption E from the cluster head to the relay node according to the relationship between the distance d and the threshold distance d0 CtoR , the communication energy consumption E from the relay node to the base station RtoS , and calculate the communication energy consumption E from the cluster head to the base station CtoS :

[0039]

[0040] Where: d CtoR is the distance from the cluster head to the relay node; d RtoS is the distance from the relay node to the base station; d CtoS is the distance from the cluster head to the base station;

[0041] E2. E CtoR + E RtoS - E CtoS represents the energy saved by the relay node when transmitting data. If this value is less than 0, it indicates that the relay node effectively reduces the network energy consumption. According to the relationship between d CtoR , d RtoS and d CtoS , there are the following eight working conditions:

[0042]

[0043] E3. When the sensor has soft permanent faults such as offset, drift, gain, and hysteresis faults that do not affect the sensor communication function, the faulty sensor that satisfies formula (7) can be used as a relay node to collect the monitoring data sent by the cluster head and forward it to the base station.

[0044] The second aspect of the present invention relates to an apparatus for a robust routing protocol of a wireless sensor network based on game theory and particle swarm optimization algorithm, including a memory and one or more processors. The memory stores executable code, and when the one or more processors execute the executable code, it is used for the method of the robust routing protocol of the wireless sensor network based on game theory and particle swarm optimization algorithm of the present invention.

[0045] The third embodiment of the present invention relates to a computer-readable storage medium, on which a program is stored. When the program is executed by a processor, the method of the robust routing protocol for wireless sensor networks based on game theory and particle swarm optimization algorithm of the present invention is implemented.

[0046] The advantages of the present invention are as follows:

[0047] (1) Based on the network model and energy consumption model, the present invention derives the calculation formula for the theoretically optimal number of cluster heads, and proposes a candidate cluster head selection method based on game theory, which combines the mixed strategy Nash equilibrium with factors such as the remaining energy of nodes, node degree, and node importance, making the elected candidate cluster heads more reasonable;

[0048] (2) The present invention proposes an optimal cluster head selection method based on particle swarm optimization, and constructs an objective function considering indicators such as the coverage rate, remaining energy rate, and importance of the cluster head set, making the final distribution of cluster heads uniform and the number close to the theoretical optimum, effectively balancing the network energy consumption;

[0049] (3) The present invention proposes a reuse strategy for faulty sensors, and uses eligible soft permanent faulty sensors as relay nodes, which can effectively utilize the remaining energy of faulty sensors, maintain the stability of the network topology, and extend the network life; Brief Description of the Drawings

[0050] Figure 1 is the flow chart of the present invention;

[0051] Figure 2 is the network structure diagram of the embodiment of the present invention in the normal data transmission stage;

[0052] Figure 3 is the change diagram of the number of cluster heads of the FTRP-GPSO protocol of the present invention;

[0053] Figure 4 is the change diagram of the number of surviving nodes of the FTRP-GPSO protocol of the present invention and the comparison protocol;

[0054] Figure 5 is the change diagram of the remaining energy of the FTRP-GPSO protocol of the present invention and the comparison protocol;

[0055] Figure 6 is the network structure diagram of the embodiment of the present invention when a sensor has a soft permanent fault;

[0056] Figure 7 is the change diagram of the number of cluster heads of the RFN strategy of the present invention;

[0057] Figure 8 is the change diagram of the number of surviving nodes of the RFN strategy of the present invention and the comparison strategy;

[0058] Figure 9 It is the graph of the remaining energy change of the RFN strategy and the comparison strategy of the present invention;

[0059] Figure 10 It is a schematic diagram of the device of the present invention. Specific Embodiments

[0060] The present invention will be further described in detail below in conjunction with the accompanying drawings and a simulation example.

[0061] Embodiment 1

[0062] The method for a robust routing protocol for a wireless sensor network based on game theory and particle swarm optimization algorithm disclosed in this embodiment is as follows, Figure 1 as shown, and specifically includes the following steps:

[0063] A. Derive the theoretically optimal number of cluster heads according to the network model and the communication energy consumption model, specifically including:

[0064] A1. Set the WSN monitoring area to 400m×150m, randomly distribute 71 measurement points, and the simulation parameters are shown in Table 1;

[0065] Table 1. Network simulation parameters

[0066]

[0067]

[0068] A2. Calculate the energy consumption E of cluster members, the energy consumption E of cluster heads, the energy consumption E of the cluster group, and the energy consumption E of the network according to the communication energy consumption model; CM CH Cluster network Cluster network network ;

[0069] A3. Substitute the relevant parameters of the monitoring network, and with the goal of minimizing the network energy consumption, derive that the theoretically optimal number of cluster heads k is 10.

[0070] B. Use the non - cooperative game method to elect candidate cluster heads from sensors, specifically including:

[0071] B1. Model the candidate cluster head selection problem as a clustering game model CG = {N, S, U}, and substitute the network parameters into the utility function U(s i );

[0072] B2. Define the probability that sensor i selects strategy H as p, calculate the Nash equilibrium probability p of the mixed strategy; combine the remaining energy, node degree, and node importance, and give the probability p that sensor i becomes a candidate cluster head i :

[0073] B3. Each sensor plays a game with its adjacent nodes and calculates the probability p of itself becoming a cluster head i , and generates a random number rand i ∈[0,1];

[0074] B4. If rand i ≤p i , sensor i becomes a candidate cluster head and joins the candidate cluster head set C, otherwise sensor i becomes a cluster member.

[0075] C. The particle swarm optimization algorithm is used to determine the final cluster head, specifically including:

[0076] C1. Substitute the network parameters, and define the objective function F of the particle swarm optimization algorithm according to the coverage rate C f , the remaining energy E f and the importance degree I f ;

[0077] C2. Use the particle swarm optimization algorithm to solve the objective function F, and elect the final cluster head close to the theoretical optimal number k of cluster heads from the candidate cluster head set C.

[0078] D. The sensors form clusters nearby, and the network enters the stable data transmission stage, specifically including:

[0079] D1. According to the cluster head election result, the sensors that do not become cluster heads join the closer cluster head and become cluster members;

[0080] D2. The cluster head collects and organizes the monitoring data collected by the cluster members and transmits it to the base station, as Figure 2 shown;

[0081] D3. After a period of time, the current round ends, and new cluster heads are elected in the next round;

[0082] D4. In the working condition where the sensors are fault-free, compared with other protocols, the changes in the number of cluster heads of the proposed FTRP-GPSO protocol are as Figure 3 shown, the changes in the number of surviving nodes are as Figure 4 shown, and the changes in the remaining energy are as Figure 5 shown.

[0083] E. Derive the RFN strategy. When there are sensors with soft permanent faults, regard the faulty sensors as relay nodes to achieve a low-energy consumption, long-life, and high-robust wireless sensor network routing, specifically including:

[0084] E1. When there are sensors with soft permanent faults, calculate d CtoR , d RtoS and d CtoS , and determine whether the RFN strategy is satisfied;

[0085] E2. The faulty nodes that meet the RFN strategy act as relay nodes, collect the monitoring data sent by the cluster head, and forward it to the base station, such as Figure 6 As shown;

[0086] E3. After a period of time, when the current round ends, a new cluster head is elected in the next round.

[0087] E4. In the case of soft permanent failure of the sensor, the number of cluster heads of the FTRP-GPSO protocol using the RFN strategy changes as compared with other strategies. Figure 7 As shown, the number of surviving nodes changes as Figure 8 As shown, the remaining energy changes as Figure 9 shown.

[0088] Example 2

[0089] like Figure 10 This embodiment relates to a device for a robust routing protocol for wireless sensor networks based on game theory and particle swarm optimization algorithm, including a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to implement the method for a robust routing protocol for wireless sensor networks based on game theory and particle swarm optimization algorithm of embodiment 1.

[0090] Example 3

[0091] This embodiment relates to a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the method of the wireless sensor network robust routing protocol based on game theory and particle swarm optimization algorithm of embodiment 1 is implemented.

[0092] The contents described in the implementation cases of this specification are merely an enumeration of the implementation forms of the inventive concept. The protection scope of the present invention should not be deemed to be limited to the specific forms described in the implementation cases. The protection scope of the present invention also extends to the equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A method for a robust routing protocol for wireless sensor networks based on game theory and particle swarm optimization algorithm, comprising the following steps: A. Derive the theoretical optimal number of cluster heads based on the network model and communication energy consumption model; B. Use non-cooperative game method to select candidate cluster heads from sensors; C. Use particle swarm optimization algorithm to determine the final cluster head; D. Sensors are clustered nearby, and the network enters the stable data transmission stage; E. Derive a strategy for reusing faulty sensors. When there are sensors with soft permanent faults, the faulty sensors are used as relay nodes to achieve low-energy, long-life, and highly robust wireless sensor network routing.

2. The method of wireless sensor network robust routing protocol based on game theory and particle swarm optimization algorithm as claimed in claim 1, characterized in that: Step A specifically includes: A1. Obtain the relevant parameters of the monitoring network, assuming that the number of sensors in the monitoring network is N and the size of the monitoring area is m×n; A2. Calculate the energy consumption E of cluster members based on the communication energy consumption model CM , cluster head energy consumption E CH , Cluster Energy Consumption E Cluster , Network energy consumption E network , the calculation formula is: Where: l is the length of the data packet (number of bits); k is the number of cluster heads; d toCH is the distance from the cluster member node to the cluster head; d toSN is the distance from the cluster head to the base station; ε elec is the energy consumption per bit of data sent or received; fs is the energy consumption coefficient under the free space model; ε mp is the energy consumption coefficient under the multipath fading model; ε DA is the energy consumption coefficient of data fusion; A3. With the goal of minimizing network energy consumption, the theoretical optimal number of cluster heads k is derived according to the above formula, and the calculation formula is: A4. Substitute the relevant parameters of the monitoring network into formula (2) to solve the theoretical optimal number of cluster heads k.

3. The method of wireless sensor network robust routing protocol based on game theory and particle swarm optimization algorithm as claimed in claim 1, characterized in that: Step B specifically includes: B1. Model the candidate cluster head selection problem as a clustering game model CG = {N, S, U}, where N represents the set of sensor nodes participating in the game, S = {H, NH} represents the strategy space of sensor nodes, strategy H represents the sensor becoming a cluster head, and strategy NH represents the sensor becoming a cluster member; B2. When all sensors choose strategy NH, they will send data directly to the base station, resulting in excessive energy consumption, so the utility is 0; when sensor i chooses strategy H to become the cluster head, its utility is defined as The utility of other cluster member nodes is defined as B3. Define the probability of sensor i selecting strategy H as p, calculate the Nash equilibrium probability p of the mixed strategy; combine the residual energy, node degree and node importance to give the probability p of sensor i becoming a candidate cluster head i : Where: is the number of neighbor nodes; is the remaining energy; i is the importance of the node; d toSN is the distance from the cluster head to the base station; α, β and γ are control parameters whose sum is 1; ω is the amplification factor; B4. Each sensor negotiates with its neighboring nodes and calculates the probability p of itself becoming a cluster head i , and generate a random number rand i ∈[0,1]; B5. If rand i ≤p i , sensor i becomes a candidate cluster head and joins the candidate cluster head set C, otherwise sensor i becomes a cluster member.

4. The method of wireless sensor network robust routing protocol based on game theory and particle swarm optimization algorithm as claimed in claim 1, characterized in that: Step C specifically includes: C1. According to coverage C f 、Residual energy E f and importance I f The objective function F of the particle swarm optimization algorithm is defined as: Where: η, λ and δ are control parameters whose sum is 1; C2. The particle swarm optimization algorithm is used to solve the objective function F and select the final cluster head close to the theoretical optimal number of cluster heads k from the candidate cluster head set C.

5. The method of wireless sensor network robust routing protocol based on game theory and particle swarm optimization algorithm as claimed in claim 1, characterized in that: Step D specifically includes: D1. According to the cluster head election results, the sensors that have not become cluster heads join the closer cluster heads and become members of the cluster; D2. The cluster head collects and organizes the monitoring data collected by the cluster members and transmits it to the base station; D3. After a period of time, when the current round ends, a new cluster head is elected in the next round.

6. The method of wireless sensor network robust routing protocol based on game theory and particle swarm optimization algorithm as claimed in claim 1, characterized in that: Step E specifically includes: E1. According to the relationship between the distance d and the threshold distance d0, calculate the communication energy consumption E from the cluster head to the relay node CtoR , communication energy consumption from relay node to base station E RtoS , calculate the communication energy consumption E from cluster head to base station CtoS : Where: d CtoR is the distance from the cluster head to the relay node; d RtoS is the distance from the relay node to the base station; d CtoS is the distance from the cluster head to the base station; E2.E CtoR +E RtoS -E CtoS Indicates the energy saved by the relay node when transmitting data. If the value is less than 0, it means that the relay node effectively reduces the network energy consumption. CtoR d RtoS and d CtoS There are eight working conditions: E3. When a sensor has a soft permanent fault such as offset, drift, gain and stuck fault that does not affect the sensor communication function, the faulty sensor that satisfies formula (7) can act as a relay node to collect the monitoring data sent by the cluster head and forward it to the base station.

7. A device for a robust routing protocol for wireless sensor networks based on game theory and particle swarm optimization algorithm, characterized in that: The invention comprises a memory and one or more processors, wherein the memory stores executable codes, and when the one or more processors execute the executable codes, the method for a robust routing protocol for a wireless sensor network based on game theory and particle swarm optimization algorithm as described in any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium, characterized in that: A program is stored thereon, and when the program is executed by a processor, the method of a robust routing protocol for a wireless sensor network based on game theory and a particle swarm optimization algorithm as described in any one of claims 1 to 6 is implemented.

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