Heterogeneous wireless sensor network node positioning method based on improved marine predator algorithm

Abstract

The invention discloses a heterogeneous wireless sensor network node positioning method based on an improved marine predator algorithm, which comprises a method process of improving the node positioning precision of a heterogeneous wireless sensor network in an offshore wind power plant based on the improved marine predator algorithm. The method comprises the following steps: firstly, constructing a nonlinear heterogeneous wireless sensor network positioning optimization model; secondly, for the improved marine predator algorithm, a Gaussian chaotic mapping model is introduced to initialize the population, and the population quality is improved. And thirdly, a golden sine strategy is introduced to update the position of the marine predator, and the convergence precision and convergence speed of the algorithm are improved. And finally, a Gaussian disturbance strategy is introduced, a disturbance solution is constructed at an optimal solution, the capability of jumping out of local optimum of the algorithm is enhanced, and the node positioning precision of the heterogeneous wireless sensor network in the offshore wind power plant is improved.

Description

Technical Field

[0001] The invention relates to a heterogeneous wireless sensor network node positioning method based on an improved ocean predator algorithm, and belongs to the technical field of offshore wind power. Background Art

[0002] With the development of offshore wind farm construction, it is necessary to monitor the sea conditions of the ocean where the wind farm is located. Due to the influence of ocean currents, the position of the monitoring device is not fixed, and it is necessary to grasp the location information of the sea area where the wind farm is located in real time. In the existing technology, each monitoring device is equipped with a GPS positioning device, but this solution cannot grasp the location information of the monitoring device in real time when the sea conditions are complex.

[0003] When using wireless sensor networks to monitor complex scenes such as the ocean, there are a large number of heterogeneous sensors in the network. These sensors have different perception radius and communication radius. Therefore, how to improve the positioning accuracy of marine heterogeneous wireless sensor network nodes is a key issue that needs to be solved when applying heterogeneous sensor networks to the positioning of sea areas where wind farms are located. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a heterogeneous wireless sensor network node positioning method based on an improved marine predator algorithm.

[0005] The present invention is achieved through the following technical solutions.

[0006] The present invention provides a heterogeneous wireless sensor network node positioning method based on an improved marine predator algorithm, which comprises the following steps.

[0007] Step 1: Build a wireless sensor network positioning model, input the area of ​​the monitored sea area, input sensor-related parameters, input algorithm-related parameters, complete the wireless sensor network node deployment and obtain the network communication topology.

[0008] Step 2: Introduce Gaussion chaotic mapping to initialize the population and calculate the fitness function.

[0009] Step 3: Assign iter=1.

[0010] Step 4: In the early stage of optimization, the elite predator leads the rest of the marine predators to perform Brownian motion to search the entire space and update the positions of the marine predators.

[0011] Step 5: In the middle of the optimization, half of the predators with small fitness values ​​update their positions through the Levy flight strategy, and half of the predators with large fitness values ​​update their positions through the Brownian motion strategy.

[0012] Step 6: In the later stage of optimization iteration, the golden sine strategy is introduced to update the position of the marine predator.

[0013] Step 7: Introduce the Gaussian perturbation strategy to perturb the optimal solution and generate a new solution.

[0014] Step 8: Calculate the fitness value, compare the fitness and select the current optimal solution.

[0015] Step 11: Determine whether the iter value is less than the maximum number of iterations. If so, iter = iter + 1 and return to step 4. If so, output the optimal position of the node.

[0016] The step 2 is specifically to introduce the Guassion chaotic map to initialize the algorithm population and improve the diversity of the algorithm population. The specific operations are as follows:

[0017] Generate a stallion position using SPM chaos mapping:

[0018]

[0019] Where 1 / z k mod(1)=1 / z k -[1 / z k ], [z] represents the largest integer less than z, and the shift of the consecutive decimal representation of the number. The chaotic interval of the Gaussion map is also (0,1).

[0020] The step six is ​​specifically: introducing the golden sine strategy to update the position of the marine predator to improve the convergence accuracy and convergence speed of the algorithm. The specific expression is:

[0021]

[0022] In the formula, R1 and R2 are random numbers in [0,2π] and [0,π] respectively. represents the value of the jth dimension of the optimal individual in the tth iteration. The initial values ​​of x1 and x2 are a·(1-τ)+b·c and a·τ+b·(1-τ), respectively. The initial values ​​of a and b are -π and π, respectively. τ is the golden section number. x1, x2, a, and b are updated dynamically.

[0023] The step 7 specifically introduces a Gaussian perturbation strategy to perform perturbations at the current optimal individual position to enhance the algorithm's ability to escape from the local optimum. The specific expression is:

[0024]

[0025]

[0026] Where: is the position of the optimal individual after applying Gaussian perturbation in the tth iteration, is a random individual in the population.

[0027] The beneficial effects of the present invention are as follows: first, a nonlinear heterogeneous wireless sensor network positioning optimization model is constructed. Secondly, for the improved marine predator algorithm, a Gaussion chaos mapping model is introduced to initialize the population and improve the population quality. Thirdly, a golden sine strategy is introduced to update the position of the marine predator, thereby improving the convergence accuracy and convergence speed of the algorithm. Finally, a Gaussian perturbation strategy is introduced to construct a perturbation solution at the optimal solution, thereby enhancing the algorithm's ability to jump out of the local optimum and improving the node positioning accuracy of the heterogeneous wireless sensor network in the offshore wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the structure of the present invention;

[0029] Figure 2 is the initial sensor deployment diagram of the present invention;

[0030] Figure 3 is the initial network topology diagram of the present invention;

[0031] Figure 4 is the positioning error graph of the original marine predator algorithm of the present invention;

[0032] Figure 5 is a node positioning error graph of the original marine predator algorithm of the present invention;

[0033] Figure 6 is a positioning error graph of the improved marine predator algorithm of the present invention;

[0034] Figure 7 is a node positioning error graph of the improved marine predator algorithm of the present invention;

[0035] Figure 8 It is a diagram showing the iterative process changes of the algorithm of the present invention and the original algorithm; DETAILED DESCRIPTION

[0036] The technical solution of the present invention is further described below, but the scope of protection claimed is not limited to the description.

[0037] The present application discloses a heterogeneous wireless sensor network node positioning method based on an improved marine predator algorithm, comprising the following steps.

[0038] See also Figures 1 to 8 It should be noted that Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 7 , Figure 8 In the figure, it is assumed that the monitoring area of ​​the sea area where the wind farm is located is a square area of ​​100m×100m, the communication radius of the heterogeneous wireless sensor network node is 15m-30m, and the number of nodes is 200. The red "*" is the beacon node, whose position is known and the number is 20% of the total number of nodes; the blue "o" is the node to be located, and its specific coordinates are unknown. Figure 3 The blue line in the middle represents the communication relationship between nodes. Two nodes connected by a blue line can communicate with each other. Figure 4 and Figure 6 The short blue line represents the connection between the actual position and the estimated position of the node. The shorter the line, the higher the accuracy of the node positioning.

[0039] Step 1: Input the area of ​​the monitored sea area, input sensor related parameters, and input algorithm related parameters.

[0040] Step 2: Obtain the neighbor relationship between nodes through the beacon node broadcast information and calculate the distance between the target node and the beacon node.

[0041] Step 3: Introduce Gaussion chaotic mapping to initialize the algorithm population, calculate the fitness function value, and select elite individuals.

[0042] Step 4: Input iter=1.

[0043] Step 5: When iter<Max_iter / 3, the marine predator updates its position using the Brownian motion strategy.

[0044] Step 6: When Max_iter / 3≤iter<2*Max_iter / 3, half of the marine predators with low fitness update their positions through the Levy flight strategy, and the other half update their positions through the Brownian motion strategy.

[0045] Step 7: When iter ≥ 2*Max_iter / 3, introduce the golden sine strategy to update the position of the marine predator.

[0046] Step 8: Introduce the Gaussian perturbation strategy to perform perturbations at the optimal solution to increase the ability of the algorithm to escape from the local optimum.

[0047] Step 9: Calculate the fitness function to obtain the current node coordinates; compare the fitness values ​​to obtain the current optimal node coordinates. Determine whether termination condition 1 (iter < maximum number of iterations) is met. If so, iter = iter + 1 and return to (4). If not, output the optimal node coordinates.

[0048] The present invention is applied in a sea area with an area of ​​X×Y, where n heterogeneous sensor nodes are randomly deployed in the sea area, where the coordinates of the beacon node are known and the coordinates of the node to be located are unknown, which can be expressed as (x i ,y i ), i∈{1,2,...,n}, such as Figure 2 shown.

[0049] These sensor nodes have different communication radius R i , i∈{1,2,...,n}, the distance between two nodes is less than the communication radius, then they can communicate with each other. The network communication structure after deployment is as follows: Figure 3 As shown. The beacon node broadcasts information outward with itself as the center, including the beacon node's own number, coordinates and communication hop count (initial value is 0). After the other nodes receive the information, the communication hop count increases by 1. The beacon node number is used to confirm whether the data packet has been received. If it has been received and the communication hop count is greater than the recorded value, the data packet is discarded, otherwise the data packet is recorded and forwarded until the minimum communication hop count from all nodes to the anchor node is obtained. Then calculate the Euclidean distance between beacon nodes i and j:

[0050]

[0051] Where, d ij Represents the Euclidean distance between beacon nodes i and j, and then calculates the average hop distance of the beacon node:

[0052]

[0053] Where, Hopsize i Represents the average hop distance of beacon node i, Hopnum ij represents the minimum number of communication hops between beacon nodes i and j. Then calculate the distance between the beacon node and the node to be located:

[0054] d ki =Hopnum ki ×Hopsize i

[0055] Where, d ki Represents the estimated distance between the node k to be located and the beacon node i, Hopnum ki It represents the minimum number of communication hops between the node to be located k and the beacon node i.

[0056] The node positioning problem is a nonlinear optimization problem, and its positioning optimization model is:

[0057]

[0058] The above improved marine predator algorithm introduces Gaussion chaos map initialization to improve the quality of the initialization population, introduces the golden sine strategy to improve the convergence speed and convergence accuracy of the algorithm, and introduces the Gaussian perturbation strategy to perform perturbations at the optimal solution to improve the algorithm's ability to jump out of the local optimum. The specific steps are as follows:

[0059] First, use Gaussion chaotic mapping to generate the position of an initial population:

[0060] X=Gaussion(1,dim)×(ub-lb)+lb

[0061] In the formula, X is the initial position of the population, dim is the dimension of the algorithm, ub and lb are the upper and lower limits of the algorithm's search space, and the Gaussion chaotic mapping expression is as follows:

[0062]

[0063] Where 1 / z k mod(1)=1 / z k -[1 / z k ], [z] represents the largest integer less than z, and the shift of the consecutive decimal representation of the number. The chaotic interval of the Gaussion map is also (0,1).

[0064] Second, update the position of the marine predator. Indicates the current number of iterations, and Max_Iter indicates the maximum number of iterations;

[0065]

[0066] RB is a vector of random numbers based on a non-normal distribution, representing Brownian motion. Symbol represents the step-by-step multiplication, and the product of RB and the prey simulates the movement of the prey. P is a constant, P = 0.5, and R is a uniform random number vector in [0,1].

[0067] when For the first half of the group,

[0068]

[0069] RL is a random number vector based on Levy distribution, representing Levy motion. The product of RL and Prey simulates the animal motion in Levy mode, and adding the step length to the position of the prey simulates the motion of the prey. For the second half of the population:

[0070]

[0071] In the formula CF is considered to be an adaptive parameter that controls the predator's moving step size. The product of RB and Elite simulates the movement of the predator in Brownian motion, and the prey updates its position according to the movement of the predator in Brownian motion. When the golden sine strategy is introduced, the marine predator updates its position according to this strategy.

[0072]

[0073] In the formula, R1 and R2 are random numbers in [0,2π] and [0,π] respectively. represents the value of the jth dimension of the optimal individual in the tth iteration. The initial values ​​of x1 and x2 are a·(1-τ)+b·c and a·τ+b·(1-τ), respectively. The initial values ​​of a and b are -π and π, respectively. τ is the golden section number. x1, x2, a, and b are updated dynamically.

[0074] Then, the Gaussian perturbation strategy is introduced to perturb the optimal solution of the algorithm to generate a new solution.

[0075]

[0076]

[0077] In the formula, is the position of the optimal individual after applying Gaussian perturbation in the tth iteration, is a random individual in the population.

[0078] By calculating the fitness function and comparing the current fitness value, the optimal position of the node is output, and the node positioning error is as follows: Figure 6-7 shown.

[0079] The present invention mainly measures the performance of the algorithm by normalizing the average positioning error and the Euclidean distance between the estimated node position and the true position. The normalized average positioning error is as follows:

[0080]

[0081] In the formula, and are the estimated position and actual position of the unknown node u, N is the number of unknown nodes, R u is the communication radius of the uth unknown node.

Claims

1. A node positioning method for heterogeneous wireless sensor networks based on an improved marine predator algorithm, characterized in that: include: A method process for improving the node positioning accuracy of heterogeneous wireless sensor networks in offshore wind farms based on an improved marine predator algorithm.

2. The heterogeneous wireless sensor network node positioning method based on the improved marine predator algorithm according to claim 1 is characterized in that: The method process comprises the following steps: Step 1: Build a wireless sensor network positioning model, input the area of ​​the monitored sea area, input sensor-related parameters, input algorithm-related parameters, complete the wireless sensor network node deployment and obtain the network communication topology structure; Step 2: Introduce Gaussion chaotic mapping to initialize the population and calculate the fitness function; Step 3: Assign iter=1; Step 4: In the early stage of optimization, the elite predator leads the rest of the marine predators to perform Brownian motion to search the entire space and update the positions of the marine predators; Step 5: In the middle of the optimization, half of the predators with small fitness values ​​update their positions through the Levy flight strategy, and half of the predators with large fitness values ​​update their positions through the Brownian motion strategy; Step 6: In the later stage of optimization iteration, the golden sine strategy is introduced to update the position of the marine predator; Step 7: Introduce the Gaussian perturbation strategy to perturb the optimal solution and generate a new solution; Step 8: Calculate the fitness value, compare the fitness and select the current optimal solution; Step 11: Determine whether the iter value is less than the maximum number of iterations. If so, iter = iter + 1 and return to step 4. If so, output the optimal position of the node.

3. The heterogeneous wireless sensor network node positioning method based on the improved marine predator algorithm according to claim 2 is characterized in that: The step 2 is specifically to introduce the Guassion chaotic map to initialize the algorithm population and improve the diversity of the algorithm population. The specific operations are as follows: Generate a stallion position using SPM chaos mapping: Where 1 / z k mod(1)=1 / z k -[1 / z k ], [z] represents the largest integer less than z, and the shift of the consecutive decimal representation of the number. The chaotic interval of the Gaussion map is also (0,1).

4. The heterogeneous wireless sensor network node positioning method based on the improved marine predator algorithm according to claim 2, characterized in that: The step six is ​​specifically: introducing the golden sine strategy to update the position of the marine predator to improve the convergence accuracy and convergence speed of the algorithm. The specific expression is: In the formula, R1 and R2 are random numbers in [0,2π] and [0,π] respectively. represents the value of the jth dimension of the optimal individual in the tth iteration. The initial values ​​of x1 and x2 are a·(1-τ)+b·c and a·τ+b·(1-τ), respectively. The initial values ​​of a and b are -π and π, respectively. τ is the golden section number. x1, x2, a, and b are updated dynamically.

5. The heterogeneous wireless sensor network node positioning method based on the improved marine predator algorithm according to claim 2, characterized in that: The step 7 specifically introduces a Gaussian perturbation strategy to perform perturbations at the current optimal individual position to enhance the algorithm's ability to escape from the local optimum. The specific expression is: Where: is the position of the optimal individual after applying Gaussian perturbation in the tth iteration, is a random individual of the population.

Images