CN115866621A - Wireless sensor network coverage method based on whale algorithm - Google Patents
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Abstract
The invention belongs to the field of wireless sensor networks, and particularly relates to a wireless sensor network coverage method based on a whale algorithm, which comprises the following steps: dividing an area to be covered into L multiplied by W monitoring points; taking wireless sensor nodes in a wireless sensor network as an initial population of a whale algorithm; according to the method, the wireless sensor nodes can be accurately placed at the optimal positions of the areas to be covered, and the coverage efficiency of the wireless sensor network on the areas to be covered is improved.
Description
Technical Field
The invention belongs to the technical field of wireless sensor networks, and particularly relates to a wireless sensor network coverage method based on a whale algorithm.
Background
The Wireless Sensor Network (WSN) is composed of a plurality of low-power consumption sensor nodes with wireless communication capability, and is widely applied to the fields of environmental protection, industrial and agricultural control, city management, environmental detection, disaster prevention and reduction and the like, so that the WSN plays a vital role in the industrial field.
At present, most of large-scale wireless sensor networks are mainly deployed randomly, and the random deployment of sensor nodes can cause uneven distribution of nodes in the network, so that a large number of redundant nodes can be generated on one hand, and a coverage hole can exist in the network on the other hand. These problems not only weaken the monitoring ability of the wireless sensor network for the target monitoring area, but also reduce the service quality of the wireless sensor network.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides a wireless sensor network coverage method based on a whale algorithm, which deploys sensor nodes by adopting a reasonable and effective deployment method, ensures the network coverage rate to the maximum extent, improves the network service quality, and reduces the network cost, and comprises the following steps:
s1: dividing an area to be covered into L multiplied by W monitoring points;
s2: taking wireless sensor nodes in a wireless sensor network as an initial population of a whale algorithm; and calculating the optimal monitoring point position of the wireless sensor node in the area to be covered by utilizing a whale algorithm according to the communication radius, the sensing radius and the reliability radius of the wireless sensor, and setting the wireless sensor node at the optimal monitoring point position to cover the area to be covered.
The present invention has at least the following advantageous effects
Compared with intelligent algorithms such as a particle swarm algorithm and a gravity search algorithm, the whale optimization algorithm adopted by the method has the advantages of high solving precision, high convergence speed and reduced calculated amount in solving the coverage problem of a wireless sensor network.
The Tent chaotic mapping initialization strategy related by the invention can improve the algorithm search space and increase the population diversity, effectively solves the problem of uneven random initialization distribution of the population, dynamically adjusts the weight by utilizing the iteration times based on the self-adaptive inertia weight of cosine, enhances the search capability by utilizing larger inertia weight in the early stage of the algorithm iteration, maintains the population diversity, improves the development capability by utilizing smaller inertia weight in the later stage of the iteration, accelerates the algorithm convergence, effectively improves the optimization precision and efficiency, and is a Levin flight strategy.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a block diagram of the whale algorithm flow of the present invention;
FIG. 3 is a comparison graph of network coverage for four other algorithms in accordance with an embodiment of the present invention;
FIG. 4 is a diagram illustrating an effect of initial coverage of a network according to an embodiment of the present invention;
FIG. 5 is a diagram showing the effect of network coverage after 1000 iterations of the whale algorithm in the embodiment of the invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and embodiments may be combined with each other without conflict.
Referring to fig. 1, the invention provides a wireless sensor network coverage method based on a whale algorithm, which comprises the following steps:
s1: dividing an area to be covered into L multiplied by W monitoring points;
preferably, the dividing the area to be covered into L × W monitoring points includes:
dividing the area to be covered into a grid form of L multiplied by W, wherein L and W respectively represent the number of rows and columns of the grid, and each small grid is regarded as a monitoring point, then the area to be covered is converted into a monitoring point set p = { p } with the size of L multiplied by W 1 ,p 2 ,p 3 ,…,p L×w And the area of each grid is S.
Referring to fig. 2, S2: taking wireless sensor nodes in a wireless sensor network as an initial population of a whale algorithm; and calculating the optimal monitoring point position of the wireless sensor node in the area to be covered by utilizing a whale algorithm according to the communication radius, the sensing radius and the reliability radius of the wireless sensor, and setting the wireless sensor node at the optimal monitoring point position to cover the area to be covered.
Preferably, all wireless sensor nodes in the wireless sensor network are homogeneous nodes, that is, each wireless sensor node has the same communication radius r t And measuring the reliability radius r e Radius of perception r s And performance attributes such as communication protocol, where r t ＝2r s The number of the wireless sensor nodes is N, and the wireless sensor node set G = { G = { (G) } 1 ,G 2 ,G 3 ,…,G N And the sensing radius and the measurement reliability radius are attributes of the sensor and represent the farthest measurement distance and the reliable measurement distance of the sensor, and the communication radius is the farthest transmission distance of the data transmitted by the sensor node.
Preferably, the calculating the optimal position of the wireless sensor node in the area to be covered by using a whale algorithm according to the communication radius, the perception radius and the reliability radius of the wireless sensor comprises the following steps:
s21: initializing the position of a whale population in an area to be covered by using a Tent chaotic mapping algorithm;
x 1 ＝random(x,y)
wherein, delta is a chaotic parameter and belongs to delta E (0,1), and delta is 0.5 in the invention n The invention relates to a Tent chaotic mapping initialization strategy, which can improve the algorithm search space, increase the population diversity, effectively solve the problem of uneven random initialization distribution of the population, reduce the optimization time of the whale algorithm and improve the optimization speed.
S22: calculating the coverage probability of each monitoring point by the wireless sensor node by adopting a probability perception model, and obtaining the joint coverage probability of each monitoring point by all the sensor nodes according to the coverage probability of each monitoring point by the wireless sensor node;
preferably, the coverage probability of each monitoring point by the wireless sensor node comprises:
α＝d(G i ,P j )-(r s -r e )
p j ∈p，0<r e <r s
wherein G is i Denotes the ith wireless sensor node, P (G) i ,p j ) Representing the coverage probability of the ith wireless sensor node to the jth monitoring point, p representing the monitoring point set, d (G) i ,p j ) Representing the Euclidean distance r between the ith wireless sensor node and the jth monitoring point s Representing the perceived radius, r, of the wireless sensor node e And the radius of measurement reliability of the wireless sensor node is represented, and λ and β are sensor perception coefficients, λ =1, and β =1.5.
S23: calculating the coverage rate of the wireless sensor network to-be-covered area according to the joint coverage probability of all the sensor nodes to each monitoring point;
wherein f (P) represents the coverage rate of the wireless sensor network to the area to be covered, L W represents the number of monitoring points of the area to be covered, and P (G, P) j ) And (4) representing the joint coverage probability of all the sensor nodes to the jth monitoring point, and S representing the area of each monitoring point.
S24: calculating the coverage efficiency of the wireless sensor network to the area to be covered according to the coverage rate of the wireless sensor network to the area to be covered and the area of the area to be covered;
wherein L W S represents the area of the area to be covered, f (p) represents the coverage rate of the wireless sensor network to the area to be covered, N represents the number of wireless sensing nodes, r s The sensing radius of the wireless sensor node is represented, and eta represents the coverage efficiency of the wireless sensor network to the area to be covered.
S25: calculating the fitness value of the whale population according to the coverage rate and the coverage efficiency of the area to be covered by the wireless sensor network; if the fitness value of the current whale population is higher than the historical optimal fitness value of the whale population, performing boundary detection on the position of the current sensor node; when the positions of all the sensor nodes are in the area to be covered, the fitness value of the current whale population is used as the optimal fitness value of the whale population;
f it ＝h*f (p) +k*η
h+k＝1
wherein h and k are each f (p) And a weighting coefficient of eta, f (p) represents the coverage rate of the wireless sensor network to the coverage area, eta represents the coverage efficiency of the wireless sensor network to the coverage area, f it Representing the fitness value of the whale population, h =0.8, k =0.2 in the present invention.
S26: generating an indication parameter rho, and updating the position of the whale population according to a preset iteration number and the indication parameter rho;
when the parameter rho is smaller than a preset threshold value, carrying out whale individual position vector iteration updating by adopting contraction enclosure and random search of a whale algorithm;
ρ＝random(0，1)
A＝2ar-a
C＝2r
wherein r represents [0,1]A is a convergence factor, T is the current iteration number, T MAX A preset iteration number, rho is a probability factor, and rho is epsilon [0,1 ]]。
When the parameter rho is larger than or equal to a set threshold value, carrying out whale individual position vector iteration updating by adopting spiral rising of a whale algorithm:
ρ＝random(0，1)
wherein T is the current iteration number, T MAX For maximum number of iterations, ρ is the probability factor, and v ∈ [0,1]. w (t) is self-adaptive inertia weight based on cosine, and the weight is dynamically adjusted by using iteration times. In the early stage of algorithm iteration, the global search capability is enhanced by means of larger inertia weight, and the population diversity is maintained. In the later iteration stage, the local development capacity is improved by means of smaller inertia weight, the algorithm convergence is accelerated, and the optimization precision and efficiency are effectively improved.
S27: when the globally optimal solution of the whale population is not changed after the Q iterations are continuously carried out, disturbing the globally optimal solution of the whale population by using a Levis flight strategy, otherwise executing the step S28;
wherein, X b (T) is the globally optimal solution for the current whale population, X Levy (T) is the global optimal solution for the population of whales after the disturbance, λ represents the step size scaling factor for the Lewy flight, and λ = rand (sign [ rand-0.5)]) And rand is [0,1]A random number in between, and a random number,
for point multiplication, levy (beta) represents the flight step length, beta is a balance parameter which is usually 1.5, epsilon and v represent parameters conforming to normal distribution, gamma represents parameters complying with standard gamma distribution, and the Levy flight strategy can effectively enrich the diversity of the search population, enlarge the range of searching individual wandering, avoid the algorithm from being trapped in local minimum points, improve the global solving performance of the algorithm, and improve the coverage efficiency of a wireless network.
S28: and repeatedly executing the steps S21-S28 until the preset iteration times are reached, and taking the global optimal solution corresponding to the historical optimal fitness value of the whale population as the optimal position of the wireless sensor node in the area to be covered.
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, while the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.
The invention is further illustrated by specific experiments below.
Experiment simulation and result analysis: in order to verify the coverage performance of the wireless sensor network coverage method based on the improved whale algorithm (IWOA), the same simulation conditions are set, and the IWOA, the basic whale algorithm (WOA), the basic particle swarm algorithm (PSO), the King butterfly optimization algorithm (PSO-MBO) based on particle swarm optimization and the resampling particle swarm algorithm (RPSLOA) based on reinforcement learning are compared in the coverage rate of the wireless sensor network.
Fig. 3 is a network coverage comparison graph of the above algorithm, and it can be seen from the graph that the coverage of the coverage method provided by the present invention reaches 95.16% after 1000 iterations, and then RPSLOA, whose coverage is 93.51%, coverage of PSO-MBO is 91.79%, coverage of PSO is 84.05%, and coverage of WOA is 85.12%. It can be seen that the coverage rate of the coverage method provided by the invention is greatly improved compared with that of the original whale algorithm, and the coverage method also has obvious advantages compared with other novel coverage methods.
In order to verify the coverage effect of the method provided by the invention, namely the coverage method of the wireless sensor network based on the improved whale algorithm (IWOA), 30 isomorphic sensors with the sensing radius of 10m are considered to be deployed in the area of 100 x 100m 2 A square area of (a). By adopting the coverage method provided by the invention, the sensor node deployment control is carried out on the area, and the iteration times of the algorithm is 1000. Fig. 4 is a diagram of the initial positions of the sensor nodes obtained by random initialization, the coverage rate of the diagram is about 49.11%, and fig. 5 is a diagram of the final positions of the sensor nodes obtained after 1000 times of algorithm iteration, the coverage rate of the diagram is about 93.46%. Comparing fig. 4 and fig. 5, it can be seen that the method provided by the present invention can optimize the sensor network coverage rate and reduce the network coverage holes.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (7)
1. A wireless sensor network coverage method based on whale algorithm is characterized by comprising the following steps:
s1: dividing an area to be covered into L multiplied by W monitoring points;
s2: taking wireless sensor nodes in a wireless sensor network as an initial population of a whale algorithm; and calculating the optimal monitoring point position of the wireless sensor node in the area to be covered by utilizing a whale algorithm according to the communication radius, the sensing radius and the reliability radius of the wireless sensor, and setting the wireless sensor node at the optimal monitoring point position to cover the area to be covered.
2. The wireless sensor network coverage method based on whale algorithm as claimed in claim 1, wherein preferably, the dividing the area to be covered into L x W monitoring points comprises:
dividing the area to be covered into a grid form of L multiplied by W, wherein L and W respectively represent the number of rows and columns of the grid, and each small grid is regarded as a monitoring point, then the area to be covered is converted into a monitoring point set p = { p } with the size of L multiplied by W 1 ,p 2 ,p 3 ,…,p L×w And the area of each grid is S.
3. The method for covering the wireless sensor network based on the whale algorithm as claimed in claim 1, wherein all the wireless sensor nodes in the wireless sensor network are homogeneous nodes, that is, each wireless sensor node has the same communication radius r t Measuring the reliability radius r e And a sensing radius r s Wherein r is t ＝2r s The number of the wireless sensor nodes is N, and the wireless sensor node set G = { G = { (G) } 1 ,G 2 ,G 3 ,…,G N }。
4. The method for covering the wireless sensor network based on the whale algorithm as claimed in claim 1, wherein the calculating the optimal position of the wireless sensor node in the area to be covered by the whale algorithm according to the communication radius, the perception radius and the reliability radius of the wireless sensor comprises:
s21: initializing the position of a whale population in an area to be covered by using a Tent chaotic mapping algorithm;
s22: calculating the coverage probability of each monitoring point by the wireless sensor node by adopting a probability perception model, and obtaining the joint coverage probability of each monitoring point by all the sensor nodes according to the coverage probability of each monitoring point by the wireless sensor node;
s23: calculating the coverage rate of the wireless sensor network to-be-covered area according to the joint coverage probability of all the sensor nodes to each monitoring point;
s24: calculating the coverage efficiency of the wireless sensor network to the area to be covered according to the coverage rate of the wireless sensor network to the area to be covered and the area of the area to be covered;
s25: calculating the fitness value of the whale population according to the coverage rate and the coverage efficiency of the area to be covered by the wireless sensing network; if the fitness value of the current whale population is higher than the historical optimal fitness value of the whale population, performing boundary detection on the position of the current sensor node; when the positions of all the sensor nodes are in the area to be covered, the fitness value of the current whale population is used as the optimal fitness value of the whale population;
s26: generating an indication parameter rho, and updating the position of the whale population according to a preset iteration number and the indication parameter rho;
s27: when the globally optimal solution of the whale population is not changed after the Q iterations are continuously carried out, disturbing the globally optimal solution of the whale population by using a Levis flight strategy, otherwise executing the step S28;
s28: and repeatedly executing the steps S21-S28 until the preset iteration times are reached, and taking the global optimal solution corresponding to the historical optimal fitness value of the whale population as the optimal position of the wireless sensor node in the area to be covered.
5. The whale algorithm-based wireless sensor network coverage method according to claim 4, wherein the coverage probability of the wireless sensor nodes on the monitoring points comprises:
α＝d(G i ,P j )-(r s -r e )
p j ∈p，0<r e <r s
wherein G is i Denotes the ith wireless sensor node, P (G) i ,p j ) Representing the coverage probability of the ith wireless sensor node to the jth monitoring point, p representing the monitoring point set, d (G) i ,p j ) Representing the Euclidean distance r between the ith wireless sensor node and the jth monitoring point s Representing the perceived radius, r, of the wireless sensor node e And the radius of measurement reliability of the wireless sensor node is represented, and λ and β are sensor perception coefficients, λ =1, and β =1.5.
6. The whale algorithm-based wireless sensor network coverage method according to claim 4, wherein the updating of the whale population position according to the indication parameter p comprises:
when the parameter rho is smaller than a preset threshold value, carrying out whale individual position vector iteration updating by adopting contraction enclosure and random search of a whale algorithm;
and when the parameter rho is larger than or equal to a set threshold value, carrying out whale individual position vector iterative updating by adopting spiral rising of a whale algorithm.
7. The method for wireless sensor network coverage based on the whale algorithm, as claimed in claim 4, wherein when the global optimal solution of the whale population is not changed after Q iterations are continuously performed, perturbing the global optimal solution of the whale population by using a Levy flight strategy comprises:
wherein, X b (T) is the global optimal solution of the current whale population, X Levy (T) is a globally optimal solution for the whale population after the disturbance, λ represents the step size scaling factor of the Lewy flight, and λ = rand (sign [ rand-0.5)]) And rand is [0,1]A random number in between, and a random number,
for dot product, levy (β) represents the flight step size, β is the equilibrium parameter, ε and v represent parameters fitting a normal distribution, and Γ represents parameters obeying a standard gamma distribution. />
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