Optimization Deployment Method for Nodes in 3D Wireless Sensor Networks

Abstract

The present invention discloses an optimized deployment method for wireless sensor network nodes based on a three-dimensional space. A three-dimensional space model to be deployed for wireless sensor network nodes is obtained, and the KD tree is used to partition the three-dimensional space to be deployed; movable wireless sensor network nodes are randomly scattered into the three-dimensional space to be deployed to obtain the initial positions of each wireless sensor network node; the ant lion optimization algorithm is adopted to plan the position deployment of the wireless sensor network nodes, and a differential mutation perturbation strategy is introduced to enhance the global search ability of the algorithm; the RRT algorithm is used for path search, and at the same time, a cost function is introduced for optimization to avoid node movement collisions. The present invention solves the problem of the deployment of wireless sensor network nodes in a three-dimensional space by constructing a three-dimensional environment model, optimizing the particle swarm algorithm, and adopting path planning, etc., improves the search efficiency and convergence accuracy of the algorithm, reduces the collisions and interferences between nodes and obstacles and between nodes, and can effectively improve the stability, energy efficiency, and communication quality of the wireless sensor network.

Description

Technical Field:

[0001] The present invention belongs to the technical field of wireless sensor networks, and particularly relates to an optimized deployment method for nodes of a three-dimensional space wireless sensor network. Background Art:

[0002] A wireless sensor network is formed by a large number of wireless sensor nodes through a self-organizing manner, and can perform real-time monitoring, collaborative sensing on a working area, and collect and process information on a monitored object and the working area. Node deployment is a prerequisite for a wireless sensor network to achieve a predetermined function and is the basis for ensuring the normal operation of the network. An excellent node deployment scheme is a prerequisite for ensuring the normal operation of a wireless sensor network in a working area and having a long life cycle.

[0003] Traditional wireless sensor network deployment methods mostly focus on node arrangement on a two-dimensional plane, ignoring the influence of complex environments and obstacles in three-dimensional space. However, in actual application scenarios, obstacles in three-dimensional space (such as buildings, trees, walls, etc.) will significantly affect the propagation of wireless signals, resulting in a decline in network communication quality and even the loss of connectivity between nodes. Therefore, how to find an optimal node deployment position in three-dimensional space and reasonably avoid obstacles to ensure the coverage, connectivity, and stability of the network has become an important research topic in current wireless sensor network deployment.

[0004] Patent No. CN113242562B discloses an invention of a WSNs coverage enhancement method and system. In its solution, the maximum coverage rate of the wireless sensor network is used as a fitness function, and the positions of the ant population and the antlion population are updated multiple times through an improved antlion optimization algorithm to obtain the position of the elite antlion, that is, to calculate the optimal deployment strategy of the corresponding sensor nodes, avoiding coverage holes and a large amount of redundancy of nodes. However, the global search ability of this method is not strong, the convergence speed is not fast enough, and it is limited to two-dimensional space.

[0005] Therefore, designing a deployment scheme for wireless sensor network nodes with a fast convergence speed, strong search ability, and high environmental fitness is a problem that needs to be solved.

[0006] The information disclosed in this background art section is only intended to increase the understanding of the overall background of the present invention and should not be regarded as an admission or any form of suggestion that this information constitutes prior art already known to those of ordinary skill in the art. Summary of the Invention:

[0007] The object of the present invention is to provide an optimized deployment method for nodes of a three-dimensional space wireless sensor network, which uses an improved ant lion optimization algorithm to find the optimal deployment plan, and at the same time uses an improved RRT algorithm to perform path planning on movable nodes according to the target points of the deployment plan, so as to overcome the defects in the above-mentioned prior art.

[0008] To achieve the above object, the present invention provides an optimized deployment method for nodes of a three-dimensional space wireless sensor network, and its steps are as follows:

[0009] S01. Obtain the model of the three-dimensional space to be deployed by laser scanning. The model includes the positions, sizes of obstacles and the physical characteristics of the environment;

[0010] S02. Use a KD tree to divide the obtained three-dimensional space to be deployed;

[0011] S03. Randomly sprinkle movable wireless sensor network nodes into the three-dimensional space to be deployed, and obtain the initial positions of each wireless sensor network node;

[0012] S04. Use an ant lion optimization algorithm optimized by a differential mutation perturbation strategy to plan the position deployment of wireless sensor network nodes;

[0013] S05. Use an RRT algorithm optimized by introducing a cost function to construct an obstacle avoidance path planning from the initial node to the target point.

[0014] Preferably, in the technical solution, in step S02, the wireless sensor network nodes are surrounded by cubes with slightly larger volumes. The cubes are regarded as wireless sensor network nodes, and the volume sizes of the cubes are set as the cutting control parameters, where the side length of the cube is equal to the diameter size of the wireless sensor network node; use a KD tree to divide the three-dimensional space to be deployed into cube spaces of the same size according to the control parameters; check whether each cube space completely or partially contains an object. If there is no object part in a cube space, it can be removed.

[0015] Preferably, in the technical solution, in step S03, a three-dimensional space coordinate system is established, and the position of each cube in the three-dimensional space to be deployed is regarded as a coordinate point, and the initial positions of each wireless sensor network node are recorded according to the coordinate points.

[0016] Preferably, in the technical solution, in step S04, the process of position deployment of wireless sensor network nodes is as follows:

[0017] 4.1. Set the parameters of the ant lion optimization algorithm. The parameters of the ant lion optimization algorithm include the number of ant populations, the number of ant lion populations, the dimension and range of the search space, and the maximum number of iterations;

[0018] 4.2. Calculate the fitness values of each individual in the ant population and the antlion population, sort them in descending order, and assign the optimal individual to the elite antlion;

[0019] 4.3. Update the ant population using the differential mutation perturbation strategy, and generate new ant individuals using differential mutation;

[0020] 4.4. Randomly initialize the positions of ants and antlions, and simulate the random walk behavior of ants; perform normalization processing on the ant positions to ensure that the random walk of ants is always within the feasible region; the antlions build traps, and the random walk behavior of the ants located in the traps is affected:

[0021] 4.5. As the number of iterations increases, the activity range of ants gradually decreases, and then the elite antlion is selected;

[0022] 4.6. During the iteration process, the ants perform random walks around the elite antlion and the antlions selected through the roulette wheel mechanism to determine their positions;

[0023] 4.7. Compare the fitness values of the antlions selected through the roulette wheel mechanism and the elite antlion. If the antlion selected through the roulette wheel mechanism has a better fitness value than the elite antlion, the antlion preys on the ant and updates the position of the antlion population, and a new elite antlion is selected;

[0024] 4.8. Determine whether the number of iterations has reached the pre-set parameter; if not, increment the current iteration number t by 1, jump to step 4.3, and continue to execute; if so, end the algorithm, output the position of the elite antlion, which is the target position of node deployment.

[0025] Preferably, in the technical solution, in step 4.3, the differential mutation perturbation strategy formula is:

[0026] X(t) * = F[X AL - X(t)] - F[X A - X(t)] (1),

[0027] where t represents the current iteration number, F represents a random number between [0, 1], X AL is the current elite antlion individual, X A represents the antlion individual selected through the roulette wheel mechanism, X(t) is the current ant individual, and X(t) * represents the ant individual obtained by mutation update.

[0028] Preferably, in the technical solution, in step 4.4, if the random walk behavior of the ant is represented as X(t), then:

[0029] X(t) = [0, cumsum(2r(t1)-1), cumsum(2r(t2)-1),..., cumsum(2r(t n )-1)](2),

[0030] where cumsum() is the cumulative step length of the ant's wandering, n represents the number of ants; r(t) is a random function that randomly selects 0 or 1 according to probability;

[0031] The normalization process of the ant's position is expressed as:

[0032]

[0033] where a i and b i represent the lower and upper bounds of the random wandering range of the i-th variable of the ant respectively, and represent the lower and upper bounds of the random wandering range of the i-th variable of the ant at the t-th iteration respectively;

[0034] The random wandering behavior of the ant in the trap is expressed as:

[0035]

[0036] where c t and d t represent the lower and upper bounds of all variables of the ant at the t-th iteration respectively, is the position of the antlion at the t-th iteration.

[0037] Preferably, in the technical solution, in step 4.5, the range of the ant's activity and decreases with the increase of the number of iterations, which is expressed as:

[0038]

[0039] where w is a constant and iters represents the maximum number of iterations.

[0040] Preferably, in the technical solution, in step 4.6, the position determined by the ant is:

[0041]

[0042] where represents the new position of the random wandering of the antlion selected by the roulette mechanism at the t-th iteration, represents the new position of the random wandering of the elite antlion at the t-th iteration, represents the position generated after weighted fusion of the i-th ant at the t-th iteration.

[0043] Preferably, in the technical solution, in step 4.7, the update of the elite antlion is expressed as:

[0044]

[0045] wherein, represents the fitness value of the elite antlion, represents the fitness value of the antlion selected by the roulette wheel mechanism.

[0046] Preferably, in the technical solution, in step S05, the obstacle avoidance path planning process is as follows:

[0047] 5.1 Randomly collect node X in the three-dimensional space to be deployed rand , and put the node X rand nearest to X near into the set T;

[0048] 5.2 Let X near grow along the direction pointing to X rand , and obtain a new node X new by specifying the step size;

[0049] 5.3 Introduce a cost function to impose constraints on the generation of random points;

[0050] 5.4 Perform a collision detection on the connection line between X near and X new . If a collision occurs, perform the next round of loop. If no collision occurs, put the new node X new into the node set T, and set X near as the parent node of X new ;

[0051] 5.5 If X new is equal to the target end point X goal , or the distance from X new to X goal is less than the set threshold, the path is successfully found, that is, exit the loop.

[0052] Preferably, in the technical solution, in step 5.3, the introduced cost function is:

[0053] f(n) = g(n) + h(n) (10),

[0054] where f(n) is the evaluation function from the initial point via node n to the target node, g(n) is the actual cost from the initial node to node n in the state space, and h(n) is the estimated cost of the best path from node n to the target node.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The KD tree is used to divide the three-dimensional space to be deployed into cube spaces of the same size as the nodes of the wireless sensor network, clarifying the coordinate positions of the nodes and obstacles in the three-dimensional space, which helps to quickly find the target position and perform path planning and deployment; the ant lion optimization algorithm using the differential mutation perturbation strategy enriches the diversity of the population and improves the global search ability of the algorithm; the RRT algorithm optimized by introducing a cost function is used to perform path planning for the movable nodes, effectively avoiding collisions between the nodes and obstacles; the optimized deployment and path planning integration of the nodes in a three-dimensional complex environment are realized. Brief Description of the Drawings:

[0057] Figure 1 It is a flowchart of the optimization deployment method for the nodes of the three-dimensional space wireless sensor network according to the present invention;

[0058] Figure 2 It is a flowchart of the ant lion optimization algorithm improved by the differential mutation strategy according to the present invention. Specific Embodiments:

[0059] The specific embodiments of the present invention will be described in detail below, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.

[0060] Unless otherwise explicitly stated, in the whole specification and claims, the term "comprising" or its variations such as "including" or "having" etc. will be understood to include the stated elements or components, without excluding other elements or other components.

[0061] As Figure 1 shown, for the optimization deployment method of the nodes of the three-dimensional space wireless sensor network, the steps are as follows:

[0062] S01. Obtain the model of the three-dimensional space to be deployed by laser scanning, and the model includes the positions, sizes of the obstacles and the physical characteristics of the environment;

[0063] S02. Divide the obtained three-dimensional space to be deployed using the KD tree; surround the nodes of the wireless sensor network with a cube slightly larger in volume, and this cube is regarded as the node of the wireless sensor network, and the volume size of this cube is set as the segmentation reference parameter, where the side length of the cube is equal to the diameter size of the node of the wireless sensor network; divide the three-dimensional space to be deployed into cube spaces of the same size according to the reference parameter using the KD tree; check whether each cube space completely or partially contains an object, and if there is no object part in a cube space, it can be removed;

[0064] Each cuboid space is regarded as a coordinate point, which helps to quickly find the final deployment position iteratively updated by the ant lion optimization algorithm and set its coordinate point as the final target point of the RRT algorithm path planning;

[0065] S03. Randomly sprinkle the nodes of the mobile wireless sensor network into the three-dimensional space to be deployed, and obtain the initial positions of each wireless sensor network node; establish a three-dimensional space coordinate system, regard the position of each cube in the three-dimensional space to be deployed as a coordinate point, and record the initial positions of each wireless sensor network node according to the coordinate points;

[0066] S04. Use the ant lion optimization algorithm optimized by the differential mutation perturbation strategy to plan the position deployment of the wireless sensor network nodes;

[0067] As Figure 2 shown, the process of position deployment of the wireless sensor network nodes is as follows:

[0068] 4.1. Set the parameters of the ant lion optimization algorithm, including the number of ant populations, the number of ant lion populations, the dimension and range of the search space, and the maximum number of iterations;

[0069] 4.2. Calculate the fitness values of each individual in the ant population and the ant lion population, sort them in descending order, and assign the optimal individual to the elite ant lion;

[0070] 4.3. Use the differential mutation perturbation strategy to update the ant population and generate new ant individuals by differential mutation;

[0071] The formula of the differential mutation perturbation strategy is:

[0072] X(t) * = F[X AL - X(t)] - F[X A - X(t)] (1),

[0073] where t represents the current number of iterations, F represents a random number between [0, 1], X AL is the current elite ant lion individual, X A represents the ant lion individual selected by the roulette wheel mechanism, X(t) is the current ant individual, and X(t) * represents the ant individual obtained by mutation update;

[0074] 4.4. Randomly initialize the positions of ants and ant lions, simulate the random walk behavior of ants X(t), then:

[0075] X(t) = [0, cumsum(2r(t1) - 1), cumsum(2r(t2) - 1),..., cumsum(2r(t n)-1)](2),

[0076] where cumsum() is the cumulative step length of the ants' wandering, n represents the number of ants; r(t) is a random function that randomly selects 0 or 1 according to probability;

[0077] Normalize the ant positions to ensure that the ants' random wandering is always within the feasible region.

[0078]

[0079] where a i and b i represent the lower and upper bounds of the random wandering range of the i-th variable of the ant respectively. and represent the lower and upper bounds of the random wandering range of the i-th variable of the ant at the t-th iteration respectively;

[0080] The antlion constructs a trap, and the random wandering behavior of the ants located in the trap is expressed as:

[0081]

[0082] where c t and d t represent the lower and upper bounds of all variables of the ant at the t-th iteration respectively, is the position of the antlion at the t-th iteration;

[0083] 4.5. The range of ant activities and decrease with the increase of the number of iterations, and then the elite antlions are selected:

[0084]

[0085] where w is a constant and iters represents the maximum number of iterations;

[0086] 4.6. During the iteration process, the ants wander randomly around the elite antlions and the antlions selected through the roulette wheel mechanism to determine their positions:

[0087]

[0088] where represents the new position of the random wandering of the antlion selected by the roulette wheel mechanism at the t-th iteration, represents the new position of the random wandering of the elite antlion at the t-th iteration, represents the position generated by the weighted fusion of the i-th ant at the t-th iteration;

[0089] 4.7. Compare the fitness values of the ant lions selected through the roulette wheel mechanism and the elite ant lions. If the fitness value of the ant lion selected through the roulette wheel mechanism is better than that of the elite ant lion, the ant lion preys on the ant and updates the position of the ant lion population, and selects a new elite ant lion;

[0090]

[0091] Among them, represents the fitness value of the elite ant lion, represents the fitness value of the ant lion selected by the roulette wheel mechanism;

[0092] 4.8. Determine whether the number of iterations reaches the pre-set parameter; if not, increment the current iteration number t by 1, jump to step 4.3, and continue to execute; if so, end the algorithm and output the position of the elite ant lion, that is, the target position of node deployment

[0093] S05. Use the RRT algorithm optimized by introducing the cost function to construct the obstacle avoidance path planning from the initial node to the target point; the process of obstacle avoidance path planning is as follows:

[0094] 5.1. Randomly collect a node X rand in the three-dimensional space to be deployed, and put the node X rand nearest to X near into the set T;

[0095] 5.2. Let X near grow along the direction pointing to X rand , and obtain a new node X new by specifying the step size;

[0096] 5.3. Introduce the cost function to impose constraints on the generation of random points;

[0097] f(n) = g(n) + h(n) (10),

[0098] where f(n) is the evaluation function from the initial point through node n to the target node, g(n) is the actual cost from the initial node to node n in the state space, and h(n) is the estimated cost of the best path from node n to the target node;

[0099] Take f(n) as one of the decision conditions for generating random points, perform cost detection on the newly generated random points, and select the point with f(n) less than the value of its parent node plus a step size distance as the expansion point; here, g(n) is the product of the step size and the number of random points generated on the current path; for the calculation of the estimated cost h(n), in order to ensure the randomness of node generation in the RRT algorithm, the Euclidean distance that can grow in any direction is selected;

[0100] 5.4. For X near and Xnew Perform collision detection on the connection line. If a collision occurs, perform the next round of loop. If no collision occurs, add the new node X new to the node set T and set X near as the parent node of X new ;

[0101] 5.5. If X new is equal to the target end point X goal , or the distance from X new to X goal is less than the set threshold, the path is successfully found, and the loop is exited.

[0102] The foregoing description of the specific exemplary embodiments of the present invention is for purposes of illustration and exemplification. These descriptions are not intended to limit the invention to the precise forms disclosed, and it is apparent that many changes and variations are possible in light of the above teaching. The purpose of selecting and describing the exemplary embodiments is to explain the specific principles of the invention and its practical applications, so that those skilled in the art can implement and utilize the various different exemplary embodiments of the invention, as well as various different selections and changes. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. An optimization deployment method for nodes in a three-dimensional space wireless sensor network, the steps of which are as follows: S01. Use laser scanning to obtain a model of the three-dimensional space to be deployed, where the model includes the positions, sizes of obstacles, and physical characteristics of the environment; S02. Use a KD-tree to partition the obtained three-dimensional space to be deployed; S03. Randomly scatter movable wireless sensor network nodes into the three-dimensional space to be deployed, and obtain the initial positions of each wireless sensor network node; S04. Use the ant lion optimization algorithm optimized by the differential mutation perturbation strategy to plan the position deployment of wireless sensor network nodes; the process of position deployment of wireless sensor network nodes is as follows: 4.

1. Set the parameters of the ant lion optimization algorithm, where the parameters of the ant lion optimization algorithm include the number of ant populations, the number of ant lion populations, the dimension and range of the search space, and the maximum number of iterations; 4.

2. Calculate the fitness values of each individual in the ant population and the ant lion population, sort them in descending order, and assign the optimal individual to the elite ant lion; 4.

3. Use the differential mutation perturbation strategy to update the ant population, and generate new ant individuals by differential mutation; the formula of the differential mutation perturbation strategy is: X(t) * = F[X AL - X(t)] - F[X A - X(t)] (1) where \(t\) represents the current iteration number, \(F\) represents a random number between \([0, 1]\), and \(X\) AL is the current elite antlion individual, \(X\) A represents the antlion individual selected by the roulette wheel mechanism, \(X(t)\) is the current ant individual, and \(X(t)\) * represents the ant individual obtained by mutation update; S05. Use the RRT algorithm optimized by introducing a cost function to construct an obstacle avoidance path planning from the initial node to the target point; the process of obstacle avoidance path planning is as follows: 5.

1. Randomly collect node X in the three-dimensional space to be deployed rand , and put the node X that is closest to X rand into set T; near ​ 5.

2. Let X near grow along the direction pointing to X rand and obtain a new node X new by specifying the step size; 5.

3. Introduce a cost function to impose a constraint condition on the generation of random points; the introduced cost function is: f(n) = g(n) + h(n) (10), where f(n) is the evaluation function from the initial point through node n to the target node, g(n) is the actual cost from the initial node to node n in the state space, and h(n) is the estimated cost of the best path from node n to the target node; 5.

4. For X near The connection line with X new is subjected to collision detection. If a collision occurs, the next round of loop is carried out. If no collision occurs, the new node X new is put into the node set T, and X near is set as the parent node of X new . 5.

5. If X new is equal to the target end node X goal , or the distance from X new to X goal is less than the set threshold, then the path is successfully found, and the loop is exited.

2. The optimized deployment method for nodes of a three-dimensional space wireless sensor network according to claim 1, wherein: In step S02, the wireless sensor network nodes are surrounded by cubes with slightly larger volumes. This cube is regarded as the wireless sensor network node, and the volume size of this cube is set as the segmentation reference parameter. Among them, the side length of the cube is equal to the diameter size of the wireless sensor network node; use the KD-tree to divide the three-dimensional space to be deployed into cube spaces of the same size according to the reference parameter; check whether each cube space completely or partially contains an object. If there is no object part in a cube space, it can be eliminated.

3. The optimized deployment method for nodes of a three-dimensional space wireless sensor network according to claim 1, characterized in that: In step S03, establish a three-dimensional space coordinate system. The position of each cube in the three-dimensional space to be deployed is regarded as a coordinate point, and the initial position of each wireless sensor network node is recorded according to the coordinate point.

4. The optimization deployment method of the three-dimensional space wireless sensor network node according to claim 1, characterized in that: In step S04, the process of position deployment of wireless sensor network nodes further includes: 4.

4. Randomly initialize the positions of ants and ant lions, and simulate the random walking behavior of ants; perform normalization processing on the ant positions to ensure that the random walking of ants is always within the feasible domain; the ant lions build traps, and the random walking behavior of the ants located in the traps is affected: 4.

5. As the number of iterations increases, the activity range of ants gradually decreases, and then the elite ant lions are selected; 4.

6. During the iteration process, the ants randomly walk around the elite ant lions and the ant lions selected through the roulette wheel mechanism to determine their positions; 4.

7. Compare the fitness values of the antlions selected through the roulette wheel mechanism and the elite antlions. If the fitness value of the antlion selected through the roulette wheel mechanism is better than that of the elite antlion, the antlion preys on the ants and updates the position of the antlion population, and a new elite antlion is selected; 4.

8. Determine whether the number of iterations reaches the preset parameter; if not, increment the current iteration number t by 1, jump to step 4.3, and continue to execute; if so, end the algorithm and output the position of the elite antlion, that is, the target position of node deployment.

5. The optimization deployment method of a three-dimensional space wireless sensor network node according to claim 4, wherein: In step 4.4, if the random walk behavior of the ant is represented as X(t), then: X(t) = [0, cumsum(2r(t1)-1), cumsum(2r(t2)-1),..., cumsum(2r(t n )-1)](2), where cumsum() is the cumulative step length of the ant's random walk, n represents the number of ants; r(t) is a random function that randomly selects 0 or 1 according to the probability; The normalization process of the ant position is expressed as: where a i and b i represent the lower and upper bounds of the random walk range of the \(i\)-th variable of the ant respectively, and represent the lower and upper bounds of the random walk range of the \(i\)-th variable of the ant at the \(t\)-th iteration respectively; The random walk behavior of the ant located in the trap is expressed as: where c t and d t represent the lower and upper bounds of all variables of the ant at the t-th iteration respectively, is the position of the antlion at the t-th iteration.

6. The optimization deployment method of the three-dimensional space wireless sensor network node according to claim 5, wherein: In step 4.5, the range of ant activities and decreases with the increase in the number of iterations, expressed as: where w is a constant and iters represents the maximum number of iterations.

7. The optimized deployment method for nodes of a three-dimensional space wireless sensor network according to claim 6, characterized in that: In step 4.6, the position determined by the ant is: Among them represents the new position of the random walk of the antlion selected by the roulette wheel mechanism at the t-th iteration, represents the new position of the random walk of the elite antlion at the t-th iteration, represents the position generated after weighted fusion of the i-th ant at the t-th iteration.

8. The optimization deployment method of the three-dimensional space wireless sensor network node according to claim 7, characterized in that: In step 4.7, the update of the elite antlion is expressed as: Among them, represents the fitness value of the elite antlion, represents the fitness value of the antlion selected by the roulette wheel mechanism.

Images