CN-122002324-A - Wireless network coverage system and method based on self-adaptive surrounding attack conquering algorithm

CN122002324ACN 122002324 ACN122002324 ACN 122002324ACN-122002324-A

Abstract

The invention relates to a wireless network coverage system and method based on a self-adaptive attack and rescue algorithm, and belongs to the technical field of wireless network coverage. The method solves the problems of limited coverage rate improvement, insufficient stability, insufficient global search in the early stage, insufficient fine adjustment in the later stage and the like in the application of the network deterministic deployment method in a complex coverage scene in the prior art. The system comprises a parameter initialization module, an overlay modeling module, an optimization calculation module, an overlay evaluation module and a result output module. The system can automatically deploy and optimize the wireless sensor nodes under different node scale conditions, realize high coverage rate deployment when the number of the nodes is sufficient, and still can keep stronger searching capability when the number of the nodes is limited, thereby realizing higher network coverage rate, reducing coverage holes and redundant coverage, reducing network energy consumption and improving the deployment efficiency and operation stability of the wireless sensor network.

Inventors

MA ZHIXING
GUO ZHIPENG
ZHANG JIANPENG
YANG ZHIHENG
ZHANG ZHONGBI
FANG TONG
LIU YULONG
SUN WANLI

Assignees

吉林财经大学

Dates

Publication Date: 20260508
Application Date: 20260410

Claims (10)

1. The wireless network coverage system based on the self-adaptive attack and surrounding conquer algorithm is characterized by comprising a parameter initialization module, a coverage modeling module, an optimization calculation module, a coverage evaluation module and a structure output module: the parameter initialization module receives the monitoring area range parameter, the wireless sensor node quantity parameter, the node perception radius parameter and the node feasible deployment area parameter; the coverage modeling module performs space discretization on the monitoring area range to obtain discrete parameters of the demand points; The optimization calculation module generates an initial node deployment scheme population according to the monitoring area range parameter, the wireless sensor node number parameter, the node perception radius parameter, the node feasible deployment area parameter and the demand point discrete parameter through a three-time chaotic mapping mode, then adopts opposite learning optimization, reserves a better network coverage rate, obtains a candidate node deployment scheme population, adopts the attack calculation and the conquer calculation to carry out iterative optimization, selects an optimal solution mode or a differential mode to update according to random probability in each iteration, controls the update amplitude of the candidate node deployment scheme through a self-adaptive step length, carries out boundary processing on the newly generated candidate node deployment scheme, and inputs the evaluation of the coverage evaluation module; The coverage evaluation module is based on an objective function Evaluating the input newly generated candidate node deployment scheme, if the evaluation result is better than the current candidate node deployment scheme, updating the current candidate node deployment scheme, otherwise, keeping the current candidate node deployment scheme, and after each generation of iteration is finished, comparing objective function values of all newly determined current candidate node deployment schemes and updating the current global optimal deployment scheme Deployment scheme for nodes The termination condition is one of the maximum iteration times, the continuous optimal network coverage rate lifting amplitude of a plurality of generations being smaller than a preset threshold value, and the network coverage rate reaching the preset coverage rate threshold value; And the result output module outputs the current global optimal node deployment scheme.
2. The wireless network coverage system based on the adaptive attack and tear conquer algorithm according to claim 1, wherein the monitoring area is a two-dimensional matrix area, and the range parameters of the monitoring area are: In which, in the process, In order to monitor a two-dimensional matrix of areas, In order to monitor the length of the area, Is the width of the monitoring area; the node feasible deployment area parameters are as follows: In which, in the process, Represent the first The abscissa of the individual nodes is used, Represent the first The ordinate of the individual nodes, , , Is the number of wireless sensor nodes.
3. The wireless network coverage system based on the adaptive attack and tear conquer algorithm according to claim 1, wherein the monitoring area is divided into m x n grid cells by adopting a uniform grid division mode, and the central point of each grid cell is taken as a demand point.
4. The wireless network coverage system based on the adaptive attack and tear conquering algorithm according to claim 1, wherein the method for generating the initial node deployment scheme population by three chaotic mapping modes is as follows: first, the initial value of the chaotic variable is set as And generating a chaotic sequence in an iteration mode according to the following three chaotic mapping formulas: ; in the formula, Represent the first The chaotic variable obtained by the iteration is obtained, Represent the first The chaotic variable obtained by the iteration is obtained, Taking the chaos control parameter ; Then, mapping the generated chaotic sequence into a node deployment space to obtain initial node deployment scheme coordinates, wherein the mapping formula is as follows: ; in the formula, Represent the first The initial node deployment scheme is at the first The components in the dimensions of the device, Represent the first The dimension nodes deploy the lower bound of the space, Represent the first The dimension nodes deploy an upper bound of space.
5. The wireless network coverage system based on the adaptive attack and tear conquering algorithm according to claim 1, wherein the method for obtaining the candidate node deployment scheme population by adopting opposite learning optimization and reserving a better network coverage rate is as follows: Firstly, constructing opposite solutions of each initial node deployment scheme, wherein the calculation formula is as follows: ; in the formula, Represent the first The deployment scheme of the initial node is configured, Represent the first The opposite node deployment schemes corresponding to the initial node deployment schemes, A lower bound vector representing the node deployment space, An upper bound vector representing a node deployment space; each initial node is then deployed with a scheme And its opposite node deployment scheme And inputting a coverage evaluation module, calculating the network coverage, reserving a node deployment scheme with better network coverage, and transmitting the node deployment scheme back to an optimization calculation module to obtain a candidate node deployment scheme population.
6. The wireless network coverage system based on the adaptive attack and conquer algorithm according to claim 1, wherein a candidate node deployment scheme population is obtained, iterative optimization is performed by adopting attack calculation and conquer calculation, in each iteration, an optimal solution mode or a differential mode is selected to update according to random probability, the update amplitude of the candidate node deployment scheme is controlled through an adaptive step length, and after boundary processing is performed on the newly generated candidate node deployment scheme, the process of inputting the coverage evaluation module to evaluate is as follows: (1) Iterative optimization is carried out by adopting the attack calculation, wherein the attack calculation formula is as follows: ; in the formula, Represent the first The deployment scheme of the candidate nodes is on the first Dimension (V) and (I) The value of the iteration is taken; Represent the first The deployment scheme of the candidate nodes is on the first Dimension (V) and (I) The value of the iteration is taken; Representing the current globally optimal node deployment scenario at the first Dimension (V) and (I) The value of the iteration is taken; representing randomly selected first from a candidate node deployment scenario population The deployment scheme of the candidate nodes is on the first Dimension (V) and (I) The value of the iteration is taken; representing a random angle parameter; an index representing a current candidate node deployment scenario; representing randomly selected candidate node deployment scenario indexes, an ; Representing a dimension of a node deployment scenario; Representing the current iteration number; (2) Iterative optimization is carried out by adopting a conquering calculation, wherein the formula of the conquering calculation is as follows: ; in the formula, Representing a random angle parameter; (3) Calculating the self-adaptive step length; First, a time decay factor is calculated : ; In the formula, Representing the initial step size factor of the device, The number of iterations of the maximum is indicated, Representing the current iteration number; then, calculate the first Individual strength factor for individual candidate node deployment schemes : ; In the formula, Represent the first The objective function values of the individual candidate node deployment scenarios, Representing a very small constant for avoiding zero denominator; based on the time attenuation factor And individual intensity factors Calculate the first Adaptive step length corresponding to each candidate node deployment scheme : ; In the formula, Representing a sensitivity parameter; (4) In each iteration, one of the following two updating modes is selected according to the random probability: In the first mode, updating is performed towards the current global optimal node deployment scheme, and the calculation formula is as follows: ; in the formula, Representing a newly generated candidate node deployment scenario, Indicating the current first The deployment scenario of the individual candidate nodes, Represents the current globally optimal node deployment scenario, Representing the random vector of the code, Representing the multiplication of the corresponding elements; In the second mode, a differential update mode is adopted, and the calculation formula is as follows: ; in the formula, And Representing two randomly selected candidate node deployment schemes that are different from each other; (5) And carrying out boundary processing on the newly generated candidate node deployment scheme, cutting off the newly generated candidate node deployment scheme to the range of the boundary corresponding to the node deployment space, and inputting the newly generated candidate node deployment scheme subjected to the boundary processing into the coverage evaluation module for evaluation.
7. The adaptive-tapping-conquering-algorithm-based wireless network coverage system according to claim 6, wherein in each generation of iterations, the optimization computation module probabilities the newly generated candidate node deployment scenario Disturbance of Levy flight is carried out, and boundary processing is carried out, wherein the probability is that The value range of (2) is 0.1-0.3; The calculation formula is as follows: ; in the formula, A new candidate node deployment scenario representing a flight disturbance update, And Representing an upper bound vector and a lower bound vector of the node deployment space respectively, Representing the multiplication of the corresponding elements; ; in the formula, Representing the random step size of the levy distribution, The value range is 1.0-2.0 for the distribution parameters; the disturbance coefficient is represented, and the value range is 0.01-0.5.
8. The adaptive-tapping-conquering-algorithm-based wireless network coverage system according to claim 1, wherein the wireless network coverage system is characterized in that The calculation method of (1) is as follows: (1) Set the first The coordinates of each node are First, the The coordinates of each demand point are as follows The distance between the two If it meets Then it is indicated that the demand point is located at the first position The sensing range of each node can be judged to be covered by the node, and the node is marked as covered, otherwise, the node is marked as uncovered; (2) Let the total number of covered demand points in all demand points be Network coverage corresponding to node deployment scheme X N is the total number of demand points.
9. The wireless network coverage system based on the adaptive attack and tear conquer algorithm according to claim 1, wherein the preset coverage rate threshold is 0.80-0.99; The preset threshold value is that the continuous 10 times of optimal network coverage rate lifting amplitude is smaller than 10 -4 .
10. A wireless network coverage method of a wireless network coverage system based on an adaptive attack and go conquering algorithm according to any of claims 1-9, characterized by the steps of: (1) The parameter initialization module receives the monitoring area range parameter, the wireless sensor node quantity parameter, the node perception radius parameter and the node feasible deployment area parameter; (2) The coverage modeling module disperses the monitoring area range into a plurality of demand points; (3) The optimization calculation module generates an initial node deployment scheme population according to the monitoring area range parameter, the wireless sensor node number parameter, the node perception radius parameter, the node feasible deployment area parameter and the demand point discrete parameter through a three-time chaotic mapping mode, then adopts opposite learning optimization to keep a better network coverage rate, obtains a candidate node deployment scheme population, adopts the attack calculation and the conquer calculation to carry out iterative optimization, selects an optimal solution mode or a differential mode to update according to random probability in each iteration, controls the update amplitude of the candidate node deployment scheme through a self-adaptive step length, carries out boundary processing on the newly generated candidate node deployment scheme, and inputs the coverage evaluation module to evaluate; The coverage evaluation module is based on an objective function Evaluating the input newly generated candidate node deployment scheme, if the evaluation result is better than the current candidate node deployment scheme, updating the current candidate node deployment scheme, otherwise, keeping the current candidate node deployment scheme, and after each generation of iteration is finished, comparing objective function values of all newly determined current candidate node deployment schemes and updating the current global optimal deployment scheme Deployment scheme for nodes The termination condition is one of the maximum iteration times, the continuous optimal network coverage rate lifting amplitude of a plurality of generations being smaller than a preset threshold value, and the network coverage rate reaching the preset coverage rate threshold value; (4) And the result output module outputs the current global optimal node deployment scheme.

Description

Wireless network coverage system and method based on self-adaptive surrounding attack conquering algorithm Technical Field The invention belongs to the technical field of wireless network coverage, and particularly relates to a wireless network coverage system and method based on a self-adaptive attack and surrounding conquer algorithm. Background The wireless sensor network is widely applied to scenes such as environment monitoring, security inspection, disaster early warning, industrial field sensing and the like. The coverage problem is one of basic problems in deployment and operation of a wireless sensor network, and is generally required to achieve effective coverage as high as possible under the constraint conditions of given monitoring areas, node number, perceived radius and the like, and give consideration to indexes such as energy consumption, redundant overlapping, network service life and the like. The coverage optimization of the wireless sensor network belongs to the multi-constraint, nonlinear and non-convex optimization problem. When the number of nodes is increased, the space dimension of node deployment is rapidly expanded, the solving complexity is obviously improved, and under the condition that the number of nodes is limited or the deployment cost is limited, coverage holes are more difficult to repair, the coverage rate is improved, bottlenecks are easy to appear, and the network monitoring quality and reliability are further affected. In the prior art, the method for optimizing the network coverage mainly comprises two major types, namely a deterministic deployment method and an intelligent optimization method. The deterministic deployment method is simple in structure, such as regular grid deployment, geometric division deployment and the like, but is insufficient in adaptability to complex scenes such as irregular boundaries, obstacle areas, node failure, limited feasible areas and the like, and coverage holes or redundant coverage are easy to generate. The intelligent optimization method such as genetic algorithm, particle swarm algorithm, bacterial foraging algorithm, bat algorithm and other swarm intelligent and meta-heuristic algorithm has the advantages of no need of gradient information, wider application range and the like, but the following defects still exist in complex coverage scenes: (1) Insufficient initial solution quality and diversity can easily cause more initial coverage holes and slower convergence speed; (2) The method is easy to sink into local optimum or search stagnation, so that the final coverage rate is limited; (3) Dynamic balance is difficult to explore and develop, and the early global searching is insufficient or the later fine adjustment is insufficient; (4) The method is sensitive to parameter setting, and has insufficient stability under different node scales and regional conditions; (5) The constraint processing is imperfect, and no feasible solution can be generated, so that the engineering deployment efficiency is affected. Therefore, it is necessary to provide a wireless sensor network coverage optimization method with good applicability under different node scale conditions, so that high coverage deployment can be realized when the number of nodes is sufficient, and unit node coverage contribution can be obviously improved when the number of nodes is limited, thereby improving the overall coverage effect and operation stability of the network. Disclosure of Invention The invention aims to solve the technical problems, and provides a wireless network coverage system and a wireless network coverage method based on a self-adaptive surrounding attack conquering algorithm, which can automatically deploy and optimize wireless sensor nodes under different node scale conditions, realize high coverage deployment when the number of the nodes is sufficient, and still keep stronger searching capability when the number of the nodes is limited, thereby realizing higher network coverage, reducing coverage holes and redundant coverage, reducing network energy consumption and improving the deployment efficiency and operation stability of a wireless sensor network. In a first aspect, the invention provides a wireless network coverage system based on a self-adaptive attack and rescue algorithm, which comprises a parameter initialization module, a coverage modeling module, an optimization calculation module, a coverage evaluation module and a structure output module; The optimization calculation module generates an initial node deployment scheme population according to the monitoring area range parameter, the wireless sensor node number parameter, the node perception radius parameter, the node feasible deployment area parameter and the demand point discrete parameter through a three-time chaotic mapping mode, then adopts opposite learning optimization, reserves a better network coverage rate, obtains a candidate node deployment scheme population, adopts the attack