WSN (Wireless Sensor Network) energy efficient clustering coverage method and device

Abstract

The invention discloses a WSN (Wireless Sensor Network) energy efficient clustering coverage method and device, relates to the field of intelligent sensors, and solves the technical problems of node energy consumption imbalance, low network coverage efficiency and short overall service life caused by an unreasonable clustering structure in a wireless sensor network. Comprising the following steps: S1, initializing a network, generating monkey group individuals by adopting binary coding, fixing the number of cluster heads through constraint conditions, and defining a fitness function; s2, iteratively optimizing the monkey swarm algorithm, and finally obtaining an optimized binary vector; s3, determining a cluster head node according to the optimized binary vector to meet the constraint condition; s4, clustering operation is executed, non-cluster-head nodes are added into the nearest cluster head to form a communication cluster, nodes in the cluster transmit data to the cluster head, and the cluster head aggregates the data and then sends the data to the base station; and S5, introducing a fuzzy operator to perform energy updating. The method has a good application prospect in the fields of industrial automatic monitoring and the like which require a high-energy-efficiency wireless sensor network.

Description

Technical Field

[0001] The present invention relates to the field of intelligent sensors, and particularly to an energy-efficient clustering coverage method and device for a WSN (Wireless Sensor Network). Background Art

[0002] Industrial wireless sensor networks usually adopt a clustering structure, where sensing nodes are divided into multiple node clusters within the monitoring range, and each cluster has a cluster head node. In the uplink transmission stage, the sensing nodes randomly distributed within the monitoring range complete the sensing of the monitoring target and converge the sensing results to the cluster head node of the cluster where they are located. The cluster head node collects the relevant information transmitted by the sensing nodes within the cluster and uploads the data to the gateway node directly or through multi-hop. The gateway node aggregates the data transmitted by each cluster head node and hands it over to the user for further analysis and processing. In the downlink transmission stage, the user issues monitoring tasks downlink through the gateway node and uniformly allocates the monitoring target and various resources within the network; the gateway node distributes the monitoring tasks to the sensing nodes within the cluster through the cluster head node to complete the downlink distribution process of the monitoring tasks. In order to reduce the network energy consumption, and restricted by the node communication distance at the same time, a high-density sensor network must formulate an efficient clustering scheme for reasonable clustering. The energy consumption of industrial wireless sensor networks mainly consists of three parts: communication energy consumption, sensing energy consumption, and microprocessor energy consumption. Research shows that communication energy consumption such as sending and receiving accounts for more than half of the energy consumption of sensor networks. At the same time, the sensing energy consumption and microprocessor energy consumption are relatively fixed and difficult to reduce through optimization.

[0003] In industrial wireless sensor networks, cluster heads (CHs) and ordinary nodes play different roles. Cluster heads are responsible for managing ordinary nodes within the cluster, including data collection, aggregation, storage, and forwarding. They are also responsible for communicating with other cluster heads or base stations (BSs) to transmit the aggregated data. At the same time, ordinary nodes are responsible for environmental sensing, data collection, and transmitting the collected data to the cluster head. In most cases, ordinary nodes do not communicate directly with each other but through the cluster head to reduce energy consumption and communication overhead. Therefore, the cluster heads close to the base station need to undertake a heavier data forwarding task, resulting in rapid energy consumption of the cluster heads.

[0004] At the same time, there are prominent problems of excessive energy consumption in the coverage and clustering processes of IWSN. On the one hand, the energy of sensor nodes is mainly provided by limited batteries. When performing the coverage task, operations such as frequent sensing and data transmission will quickly consume energy. For example, the node needs to regularly collect environmental data and send it out, and this series of actions will continuously consume energy. On the other hand, the clustering process will also exacerbate energy consumption. The election of cluster head nodes requires multi-faceted evaluation of nodes, which involves a large amount of information exchange and calculation and will consume energy; when fusing and forwarding data, the cluster head node needs to process and transmit data, and its energy consumption is even greater, thus greatly shortening the network lifetime.

[0005] When existing algorithms handle the clustering problem in IWSN, they face the dilemma of falling into local optima. The search mechanisms of these algorithms have defects. They often stop searching after finding a locally better solution and it is difficult to discover a better clustering scheme. This will cause the imbalance of network resource allocation. Some nodes will exhaust their energy prematurely due to overloading, thereby reducing the overall performance and stability of the network, unable to meet the requirements for the efficient and reliable operation of IWSN in practical applications, and affecting the normal operation and service quality of the network. Summary of the Invention

[0006] To solve the technical problems of unbalanced node energy consumption, low network coverage efficiency and short overall lifespan caused by unreasonable clustering structure in wireless sensor networks, the present invention provides an energy-efficient clustering coverage method for WSN, including:

[0007] S1. Network initialization, generating individual monkeys in the monkey population using binary coding, fixing the number of cluster heads through constraint conditions, and defining a fitness function;

[0008] S2. Iteratively optimize the monkey algorithm, including:

[0009] S2.1. Calculate the fitness of each individual;

[0010] S2.2. Perform an adaptive crawling operation and calculate the crawling step size;

[0011] S2.3. Perform an observation operation and conduct a local search with random bit flipping for the current solution;

[0012] S2.4. Perform a jumping operation, perform a crossover operation between the current individual and the global optimal solution, and finally obtain an optimized binary vector;

[0013] S3: Determine the cluster head nodes according to the optimized binary vector, satisfying the constraint conditions;

[0014] S4: Perform a clustering operation. Non-cluster head nodes join the nearest cluster head to form a communication cluster. Nodes within the cluster transmit data to the cluster head, and the cluster head aggregates the data and sends it to the base station;

[0015] S5: Introduce a fuzzy operator for energy update.

[0016] Furthermore, the fitness is obtained by:

[0017] Fit = cost s (k, d) + cost r (k)

[0018] where Fit is the fitness value of each monkey individual, cost s (k, d) is the transmission energy consumption function, costr (k) is the consumption energy consumption function.

[0019] Furthermore, in S2.1, the transmission energy consumption function is obtained by:

[0020]

[0021] where E elec is the circuit energy consumption coefficient, ε fs is the short - distance transmission energy consumption coefficient, ε amp is the long - distance transmission energy consumption coefficient, d0 is the distance threshold, k is the size of the data packet sent or received by the sensor node, and d is the data transmission distance;

[0022] The consumption energy consumption function is obtained by:

[0023] cost r (k) = E elec k

[0024] and is obtained.

[0025] Furthermore, in S2.2, the crawling step size is obtained by:

[0026]

[0027] where n a is the crawling step size, N C is the number of iterations in the crawling process, k is the current crawling number of the monkey in this iteration, iter is the current generation of evolution, M T is the maximum generation of evolution of the monkey swarm algorithm population, a is the initial crawling step size base number, and b is the adjustment parameter.

[0028] Furthermore, in S5, the fuzzy operator is obtained by:

[0029]

[0030] where Z(t) is the fitness value under the current fuzzy generation, t is the current number of iterations, Fit max (t) is the maximum fitness value in the population under the current generation, Fit avg (t) is the average fitness value in the population under the current generation, Fit min (t) is the minimum fitness value of the average fitness value in the population under the current generation.

[0031] There is also provided a WSN energy - efficient clustering coverage device, including:

[0032] A topology initialization module, used for network initialization, generating individual monkey swarm populations by binary coding, fixing the number of cluster heads through constraint conditions, and defining a fitness function;

[0033] An intelligent optimization calculation module for iteratively optimizing the monkey colony algorithm, including:

[0034] Calculating the fitness of each individual;

[0035] Performing an adaptive crawling operation and calculating the crawling step size;

[0036] Performing an observation operation to perform local search with random bit flipping on the current solution;

[0037] Performing a jumping operation to perform a crossover operation between the current individual and the global optimal solution, and finally obtaining an optimized binary vector;

[0038] A cluster head election module for determining a cluster head node according to the optimized binary vector and satisfying the constraint conditions;

[0039] A clustering routing management module for performing clustering operations, where non-cluster head nodes join the nearest cluster head to form a communication cluster, nodes within the cluster transmit data to the cluster head, and the cluster head aggregates the data and sends it to the base station;

[0040] An energy fuzzy evaluation module for introducing a fuzzy operator for energy update.

[0041] The beneficial effects of the present invention are as follows: By improving the monkey colony algorithm to optimize the clustering structure of the industrial wireless sensor network, the problem of unbalanced node energy consumption is significantly reduced, and the energy consumption of long-distance data transmission is reduced on the premise of ensuring the network coverage quality;

[0042] Its adaptive step size adjustment and fuzzy energy update mechanism improve the convergence speed of the algorithm, effectively extend the network survival period, and are especially suitable for the dynamic coverage requirements of large-scale industrial scenarios. Description of the Drawings

[0043] Figure 1 It is a flowchart of an energy-efficient clustering coverage method for a WSN

[0044] Figure 2 It is a block diagram of an energy-efficient clustering coverage device for a WSN;

[0045] Figure 3 It is a flowchart of the monkey colony algorithm;

[0046] Figure 4 It is the cluster structure of a low-power adaptive clustering hierarchical protocol (FMA-LEACH) based on an improved fuzzy monkey colony algorithm;

[0047] Figure 5 It is a comparison chart of the simulation results of the present invention and other methods. Detailed Embodiments

[0048] To make the technical solutions and advantages in the embodiments of the present invention clearer and more understandable, the following further details the exemplary embodiments of the present invention with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than an exhaustive list of all embodiments. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0049] Embodiment 1, in combination with Figure 1 This embodiment is described to provide a method for energy-efficient clustering coverage in a WSN, including:

[0050] S1. Network initialization: Generate the individual of the monkey population using binary coding, fix the number of cluster heads through constraint conditions, and define the fitness function;

[0051] S2. Iteratively optimize the monkey algorithm, including:

[0052] S2.1. Calculate the fitness of each individual;

[0053] S2.2. Perform the adaptive crawling operation and calculate the crawling step size;

[0054] S2.3. Perform the observation operation and conduct local search with random bit flipping on the current solution;

[0055] S2.4. Perform the jumping operation, perform a crossover operation between the current individual and the global optimal solution, and finally obtain the optimized binary vector;

[0056] S3: Determine the cluster head nodes according to the optimized binary vector to meet the constraint conditions;

[0057] S4: Perform the clustering operation. Non-cluster head nodes join the nearest cluster head to form a communication cluster. Nodes within the cluster transmit data to the cluster head, and after the cluster head aggregates the data, it is sent to the base station;

[0058] S5: Introduce a fuzzy operator for energy update.

[0059] Specifically, in S1, the generation of the individual of the monkey population is achieved through:

[0060]

[0061] which is realized that the rows represent each monkey individual in the monkey population 1 and also represent a cluster head selection scheme.

[0062] Each monkey individual is a binary vector with a length equal to the number of nodes in the network. Each element represents whether the corresponding node is selected as a cluster head.

[0063] For example: X i = [0, 1, 0, 1, 0] indicates that node 2 and node 4 are selected as cluster heads.

[0064] Number the L sensor nodes in the area from 1 to N. Use binary coding to represent individuals, and represent the individual coding as a vector. A vector element of "1" indicates that the sensor node at that position is a cluster head node, and "0" indicates a normal sensing node. e n,l Indicates whether the l-th sensor in the n-th individual in the population is a cluster head node.

[0065]

[0066] Mark whether the l-th sensor in the n-th individual in the population is a cluster head node, and constrain the number of cluster head nodes in the IWSN to a fixed value M.

[0067] The specific process of the monkey colony algorithm is as Figure 3 shown.

[0068] The described fitness is obtained through:

[0069] Fit = cost s (k, d) + cost r (k)

[0070] where Fit is the fitness value of each monkey individual, cost s (k, d) is the transmission energy consumption function, and cost r (k) is the consumption energy consumption function.

[0071] In S2.1, the described transmission energy consumption function is obtained through:

[0072]

[0073] where E elec is the circuit energy consumption coefficient, ε fs is the short-distance transmission energy consumption coefficient, ε amp is the long-distance transmission energy consumption coefficient, d0 is the distance threshold, k is the size of the data packet sent or received by the sensor node, and d is the data transmission distance;

[0074] The described consumption energy consumption function is obtained through:

[0075] cost r (k) = E elec k

[0076] to obtain.

[0077] In S2.2, the described crawling step size is obtained through:

[0078]

[0079] where n ais the crawling step size, N C is the number of iterations of the crawling process, k is the current crawling times of the monkey in this iteration, iter is the current generation number of evolution, M T is the maximum generation number of evolution of the population of the monkey colony algorithm, a is the initial crawling step size base number, and b is the adjustment parameter.

[0080] Specifically, it can be seen from the formula that is the adaptive adjustment factor of the crawling step size, and its value range is [0, 1]. As the crawling times k increases, the crawling step size decreases non-linearly and will gradually shrink to a smaller value At the same time, considering the increase in the generation number of evolution of the entire monkey colony, most monkeys gradually approach the local optimal value. By adding terms, the crawling step size base number can be further adjusted according to the generation number of evolution during the evolution process to maintain fine search in a small range.

[0081] In S5, the fuzzy operator is obtained by:

[0082]

[0083] where Z(t) is the fitness value under the current fuzzy generation, t is the current iteration number, Fit max (t) is the maximum fitness value in the population under the current generation, Fit avg (t) is the average fitness value in the population under the current generation, Fit min (t) is the minimum fitness value of the average fitness value in the population under the current generation.

[0084] Specifically, through this fuzzification process, each monkey individual is fuzzified, providing a more reliable data basis for subsequent individuals, making the search of monkeys in the search space more uniform, considering more uncertain factors, improving the accuracy of fitness evaluation, and avoiding falling into the local optimum due to local information misleading.

[0085] Example 2, in combination with Figure 2 illustrates this example. A WSN energy-efficient clustering coverage device includes:

[0086] A topology initialization module for network initialization, generating individual monkey colony populations using binary coding, fixing the number of cluster heads through constraint conditions, and defining a fitness function;

[0087] An intelligent optimization calculation module for iteratively optimizing the monkey colony algorithm, including:

[0088] Calculating the fitness of each individual;

[0089] Performing an adaptive crawling operation and calculating the crawling step size;

[0090] Perform an observation operation and conduct a local search by randomly flipping bits of the current solution;

[0091] Perform a jump operation, perform a crossover operation between the current individual and the global optimal solution, and finally obtain an optimized binary vector;

[0092] A cluster head election module for determining cluster head nodes according to the optimized binary vector to satisfy the constraint conditions;

[0093] A clustering routing management module for performing clustering operations. Non-cluster head nodes join the nearest cluster head to form a communication cluster. Nodes within the cluster transmit data to the cluster head, and after the cluster head aggregates the data, it sends it to the base station;

[0094] An energy fuzzy evaluation module for introducing a fuzzy operator for energy update.

[0095] Example 3, combined with Figure 5 This example is described. Among them, FMA-LEACH represents the method of the present invention. The performance differences between LEACH, FGF, and the method of the present invention are simulated using MATLAB R2022a software. The experimental parameter settings are as follows: The total number of nodes is 100, and these nodes are randomly deployed in a square area of 200m×200m. The coordinates of the base station (BS) are (100m, 100m), and the initial energy of all nodes is 0.5J. The cluster head ratio is 0.15. The specific parameters of the experimental simulation are shown in Table 1 below.

[0096] Table 1

[0097]

[0098] In Figure 4 it can be seen the topological relationship among node deployment, cluster head election, and base station coverage in the clustered network, indirectly verifying the effectiveness of the algorithm improvement for dynamic adaptation of the network topology.

[0099] In Figure 5 it can be seen that in the first 80 rounds, the curves of the remaining energy of the three algorithms. The slope of LEACH is the highest, indicating that the remaining energy is consumed rapidly, while the slope of the method of the present invention, FMA-LEACH, is the lowest, indicating that the rate of remaining energy consumption is slower. As the proportion of cluster head nodes gradually increases, the remaining energy of FMA-LEACH is the largest, and its remaining energy is much greater than that of the LEACH algorithm. When the two comparison algorithms run to 60 rounds, their energy is almost all consumed. After 60 rounds, the energy decline rate of the method of the present invention slows down, and the remaining energy is always higher than that of the three comparison algorithms.

[0100] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0101] The above-described embodiments only express several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent should be subject to the appended claims.

Claims

1. An energy-efficient clustering coverage method for WSN, characterized in that, Including: S1. Network initialization: Generate individuals of the monkey colony population using binary encoding, fix the number of cluster heads through constraint conditions, and define a fitness function. S2. Iteratively optimize the monkey colony algorithm, including: S2.

1. Calculate the fitness of each individual. S2.

2. Perform an adaptive crawling operation and calculate the crawling step size. S2.

3. Perform an observation operation and conduct a local search with random bit flipping for the current solution. S2.

4. Perform a jumping operation, perform a crossover operation between the current individual and the global optimal solution, and finally obtain an optimized binary vector. S3: Determine the cluster head nodes according to the optimized binary vector to meet the constraint conditions. S4: Perform a clustering operation. Non-cluster head nodes join the nearest cluster head to form a communication cluster. Nodes within the cluster transmit data to the cluster head, and the cluster head aggregates the data and sends it to the base station. S5: Introduce a fuzzy operator for energy update.

2. The energy-efficient clustering coverage method for WSN according to claim 1, wherein The fitness is obtained through: Fit=cost s (k,d)+cost r (k) obtained, where Fit is the fitness value of each monkey individual, cost s (k, d) is the transmission energy consumption function, cost r (k) is the consumption energy consumption function.

3. An energy-efficient clustering coverage method for WSN according to claim 2, characterized in that In S2.1, the transmission energy consumption function is obtained through: obtained, where E elec is the circuit energy consumption coefficient, ε fs is the short-distance transmission energy consumption coefficient, ε amp is the long-distance transmission energy consumption coefficient, d0 is the distance threshold, k is the size of the data packet sent or received by the sensor node, and d is the data transmission distance; The consumed energy consumption function is obtained through: cost r (k) = E elec k Obtained.

4. A WSN energy-efficient clustering coverage method according to claim 3, characterized in that In S2.2, the crawling step size is obtained through: Obtained, where n a is the crawling step size, N C is the number of iterations of the crawling process, k is the current crawling times of the monkey in this iteration, iter is the current generation of evolution, M T is the maximum number of generations of evolution of the population of the monkey swarm algorithm, a is the initial crawling step size base, and b is the adjustment parameter.

5. The energy-efficient clustering coverage method for WSN according to claim 4, characterized in that In S5, the fuzzy operator is obtained through: obtained, where Z(t) is the fitness value under the current fuzzy algebra, t is the current iteration number, and Fit max (t) is the maximum fitness value in the population under the current algebra, Fit avg (t) is the average fitness value in the population under the current algebra, Fit min (t) is the minimum fitness value of the average fitness value in the population under the current algebra.

6. An energy-efficient clustering coverage device for WSN, characterized in that, Including: A topology initialization module for network initialization, which generates individuals of the monkey colony population using binary encoding, fixes the number of cluster heads through constraint conditions, and defines a fitness function. An intelligent optimization calculation module for iteratively optimizing the monkey colony algorithm, including: Calculating the fitness of each individual. Performing an adaptive crawling operation and calculating the crawling step size. Performing an observation operation and conducting a local search with random bit flipping for the current solution. Performing a jumping operation, performing a crossover operation between the current individual and the global optimal solution, and finally obtaining an optimized binary vector. A cluster head election module for determining cluster head nodes according to the optimized binary vector to meet the constraint conditions. A clustering and routing management module for performing a clustering operation. Non-cluster head nodes join the nearest cluster head to form a communication cluster. Nodes within the cluster transmit data to the cluster head, and the cluster head aggregates the data and sends it to the base station. An energy fuzzy evaluation module for introducing a fuzzy operator for energy update.

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