CN107295541B - Wireless sensor network coverage optimization method based on virtual force and firefly algorithm - Google Patents
Wireless sensor network coverage optimization method based on virtual force and firefly algorithm Download PDFInfo
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Abstract
The invention provides a wireless sensor network coverage optimization method based on virtual force and a firefly algorithm, which comprises the steps of firstly, establishing a corresponding mathematical model by taking the utilization rate of sensor nodes in a wireless sensor network and the effective coverage rate of the network as optimization targets; then, carrying out initial layout of the sensor nodes according to the resultant force of the virtual forces among the sensor nodes; and finally, carrying out self-adaptive dynamic deployment on the sensor nodes by a firefly algorithm. The invention prolongs the service life of the wireless sensor network and simultaneously effectively improves the network coverage efficiency.
Description
Technical Field
The invention belongs to the technical field of wireless sensor networks, and particularly relates to a wireless sensor network coverage optimization method based on virtual force and a firefly algorithm.
Background
With the rapid development of information technology, the important role of people in mastering instant and effective information is played. Therefore, means and techniques for collecting information and data are widely developed and applied, and many emerging information collection techniques and strategies are brought forward. Wireless sensor networks are one of the most popular data collection technologies today. The wireless sensor network has many advantages, such as low energy consumption, easy distribution in any environment, low cost, self-organized formation of wireless network, and the like, so that the wireless information sensing and collection become unprecedented simple and convenient. Therefore, the wireless sensor network has been widely used in real life, and has a very good application prospect in detecting the surrounding environment in terms of air temperature, pressure, positioning, and the like, and data collection in the wireless sensor network is also a hot issue in current research.
In the process of constructing the wireless sensor network, the wireless sensor nodes are reasonably deployed, so that the effective coverage area of the wireless sensor monitoring network can be increased. In large-scale sensor node deployment, coverage effect of sensor node deployment obtained by means of random scattering and the like has uncertainty. In order to realize the optimization of the wireless sensor node deployment, in recent years, an intelligent optimization algorithm is introduced into the optimization of the wireless sensor node deployment, and the optimization aims to improve the effective coverage of the whole wireless sensor network on a monitoring area and prolong the monitoring time of the wireless sensor network. Deployment optimization is usually performed with the goal of increasing the coverage area ratio or the covered grid ratio (grid ratio means that the monitored area is discretized into a grid form), so as to reduce blind areas and repeated coverage areas as much as possible. However, the technology has the defects of short service life and low coverage efficiency of the network due to the redundancy of the sensor nodes.
Disclosure of Invention
The invention aims to provide a wireless sensor network coverage optimization method based on virtual force and a firefly algorithm, which effectively improves the network coverage efficiency while prolonging the service life of a wireless sensor network.
In order to solve the technical problems, the invention provides a wireless sensor network coverage optimization method based on virtual force and a firefly algorithm, and the method comprises the following steps of firstly, establishing a corresponding mathematical model by taking the utilization rate of sensor nodes in a wireless sensor network and the effective coverage rate of the network as optimization targets; then, carrying out initial layout of the sensor nodes according to the resultant force of the virtual forces among the sensor nodes; and finally, carrying out self-adaptive dynamic deployment on the sensor nodes by a firefly algorithm.
Further, the mathematical model comprises a sensor node perception probability model, the regional coverage rate of the sensor node and a fitness function in a coverage optimization problem;
the perceptual probability model is expressed as follows;
wherein, P (c)iG) is the ithSensor node ciAs to the perceived probability of the target g,is a sensor node ciThe distance between the target g and the target g, and r is a sensor node ciRadius of coverage, reIs a sensor node ciA measure of perceived uncertainty error, and reThe value of r, alpha is usually takenλ and β are the perception ranges r-r, respectivelyeAnd r + reThe time perception quality attenuation coefficient, sigma, is various interferences and is a random number which obeys normal distribution;
the area coverage rate R (C) of the sensor node is shown as the following formula:
wherein n is the number of wireless sensor nodes arranged in a monitoring area, and i is less than or equal to n; c is a wireless sensor node set, and C ═ C1,c2,…,cn}，ci＝{xi,xjR, representing sensor node ciBy the coordinate { xi,xjCircle with the center of circle and radius of coverage area r; assuming that a monitoring area is digitally discretized into m multiplied by n pixel points, m and n represent the number of the pixel points, the area of each pixel point is delta multiplied by delta y, and the coverage rate p (x, y, c) of a sensor node set is usedi) Whether each pixel point is covered by a wireless sensor network node or not is represented;
the fitness function in the coverage optimization problem is as follows;
F(x)＝ω1×f1(x)+ω2×(1-f2(x))
wherein f is1(x) Indicating wireless sensor node utilization, f2(x) Representing wireless sensor network coverage, i.e. f2(x) R (c), ic is the total number of sensor nodes deployed in the wireless sensing network, ciI is the number of sensor nodes in working state, omega1+ω2＝1,ω1,ω2The weights of the corresponding functions respectively, and the value depends on the requirement on the comprehensive performance of the network.
Further, the method for performing initial layout of the sensor nodes according to the virtual force resultant force between the sensor nodes comprises the following steps:
3.1 calculating the virtual force F between two sensor nodesijThe calculation method is shown in the following formula,
wherein, aijFor one of the sensor nodes siTo another sensor node sjThe vector angle of (c); dijOne of the sensor nodes s is representediTo another sensor node sjDistance of dthAdjusting the distance threshold of the interaction force attribute between the sensor nodes; c is a critical value of the distance, c is set to 2 times dth；ωaAnd ωrRespectively representing a gravitational coefficient and a repulsive coefficient which are used as the basis for adjusting the density of the virtual force adjusting nodes;
3.2 calculating the resultant force F received by the sensor nodeiThe calculation method is shown in the following formula,
wherein, FoFor obstacle to sensor node siActing force of (F)rFor sensor nodes s in hot spot areaiThe acting force of (c);
3.3 updating the position of the sensor node, wherein the position updating method comprises the following steps:
wherein (x)old,yold) To the location before update, (x)new,ynew) For updated position, FixAnd FiyRespectively a resultant force F of virtual forcesiAnd a maximum movable distance of the sensing sensor node, Maxstep.
Further, the method for self-adaptive dynamic deployment of the sensor nodes by the firefly algorithm comprises the following steps:
4.1, each sensor node is regarded as a firefly to form a firefly group, and each sensor node in the throwing nodes is endowed with the same fluorescein concentration;
4.2 updating the fluorescein concentration of the sensor node;
4.3 the sensor node moves to the direction of an adjacent node with higher fluorescein concentration than the sensor node according to the movement probability;
4.4 updating the position of the sensor node after the movement;
4.5 updating the radius of the firefly adjacent decision domains.
Compared with the prior art, the method has the obvious advantages that an improved heterogeneous node probability perception model and a target optimization function are established, then the virtual force algorithm is adopted to guide the nodes to move under the action of the virtual force for initial deployment, in order to further improve the coverage rate and the deployment efficiency of the network, the firefly algorithm is adopted to realize optimization of node deployment, the coverage rate of the wireless sensor network nodes is improved, the redundancy of the sensor nodes is reduced, the network cost is effectively reduced, and the network survival time is prolonged.
Drawings
FIG. 1 is a simulation of a wireless sensor network after initial placement of sensor nodes with resultant virtual forces;
FIG. 2 is a diagram of a simulation of a wireless sensor network after adaptive dynamic deployment using a firefly algorithm based on FIG. 1;
FIG. 3 is a flow chart of the method of the present invention.
Detailed Description
It is easily understood that, according to the technical solution of the present invention, those skilled in the art can imagine various embodiments of the coverage optimization method of the wireless sensor network based on the virtual force and the firefly algorithm without changing the spirit of the present invention. Therefore, the following detailed description and the accompanying drawings are merely illustrative of the technical aspects of the present invention, and should not be construed as all of the present invention or as limitations or limitations on the technical aspects of the present invention.
The invention relates to a wireless sensor network coverage optimization method based on virtual force and a firefly algorithm, which comprises the steps of firstly, establishing a corresponding mathematical model by taking the utilization rate of sensor nodes in a wireless sensor network and the effective coverage rate of the network as optimization targets; then, performing initial layout of the sensor nodes according to the resultant force of the virtual forces among the sensor nodes, so that the sensor nodes can be uniformly covered in a wireless sensor network area; and finally, carrying out self-adaptive dynamic deployment on the sensor nodes by a firefly algorithm. The method specifically comprises the following steps:
step one, mathematical model establishment
1.1 determining sensor node perception probability model
In view of the fact that factors such as electromagnetic disturbance and background noise interference often occur in an actual wireless sensor network monitoring area, and meanwhile, the influence of residual energy of sensor nodes on the sensing capability of the sensor nodes is also considered, the factors are added on the basis of a traditional sensing probability model. Facility forAs the ith sensor node ciFor the perception probability of the target g, a perception probability model is shown as the following formula;
in the perceptual probability model, the probability of the perceptual probability model,is a sensor node ciDistance from target g, r represents sensor node ciRadius of coverage, reIs a sensor node ciPerceived uncertainty error measure and reThe value of r, alpha is usually takenλ and β are the perception ranges r-r, respectivelyeAnd r + reThe time-varying perceptual quality attenuation coefficient, σ, is various interferences and is a random number which obeys normal distribution.
1.2 calculating the area coverage of sensor nodes
Supposing that a monitoring area of the wireless sensor network is a two-dimensional plane, n wireless sensor nodes are arranged in the monitoring area, i is less than or equal to n, and the set of the wireless sensor nodes is C ═ C1,c2,…,cnIn which c isi＝{xi,xjR, representing sensor node ciBy the coordinate { xi,xjCircle with the center of circle and radius of coverage area r. Assuming that a monitoring area is digitally discretized into m multiplied by n pixel points, m and n represent the number of the pixel points, the area of each pixel point is delta multiplied by delta y, and the coverage rate p (x, y, c) of a sensor node set is usedi) To indicate whether each pixel point is covered by a wireless sensor network node, the coverage rate R (C) of the whole wireless sensor network monitoring area is the coverage area of a node setThe coverage ratio R (C) is expressed by the following formula in relation to the total area m × n of the monitored area:
1.3 determining fitness function in coverage optimization problem
In the wireless sensor network coverage optimization problem, two conditions are to be satisfied: the first condition is to maximize the coverage of the wireless sensor network; the second condition is to maximize the utilization of the sensor nodes, i.e., maximize the utilization of the sensor nodes. The fitness function may be described as follows;
F(x)＝ω1×f1(x)+ω2×(1-f2(x))
wherein f is1(x) Indicating wireless sensor node utilization, f2(x) Representing wireless sensor network coverage, i.e. f2(x) R (c), ic is the total number of sensor nodes deployed in the wireless sensing network, ciI is the number of sensor nodes in working state, omega1+ω2＝1,ω1,ω2The weights of the corresponding functions respectively, and the value depends on the requirement on the comprehensive performance of the network.
Step two, introducing a virtual force algorithm to initially arrange the sensor nodes
2.1 calculate the virtual force between the two sensor nodes. In the wireless sensor network, the node can be judged by judging the sensor nodeAndthe distance between the two sensor nodes, the threshold value and the communication radius to determine the attraction or repulsion between the sensor nodesAndvirtual force Fi betweenjCan be calculated according to the following equation:
wherein, aijAs sensor node siTo sjAngle of vector of (d)ijRepresenting a node siAnd sjDistance of dthFor adjusting the distance threshold of the interaction force attribute between the mobile nodes, c is a critical value of the distance and is set as 2 times dth，ωaAnd ωrRespectively representing a gravitational coefficient and a repulsive coefficient which are used as the basis for adjusting the density of the virtual force adjusting nodes.
2.2 calculating the resultant force applied to the sensor node. For a certain sensor node, the acting force of obstacles and hot spot areas on the sensor node is also received in addition to the virtual force acted on the sensor node by other sensor nodes, so that the nodeResultant force F of virtual forceiAs shown in the following formula
Wherein, FoSensor node for obstacleActing force of (F)rSensor node pair for hot spot areaThe force of (2).
And 2.3 updating the position of the sensor node. Sensor nodeWill be at the virtual force resultant FiUnder the action of (3), from the original position(xold,yold) Updated to a new position (x)new,ynew) The location updating process is as follows:
wherein the resultant force F of the virtual forcesiThe x-axis component and the y-axis component of (A) are respectively FixAnd FiyAnd Maxstep is the maximum movable distance of the sensing sensor node.
In this way, the initial deployment construction of the sensor nodes applying the virtual force algorithm is completed. Then, self-adaptive dynamic deployment is carried out by using a firefly algorithm, and the process of the firefly algorithm is divided into four stages: updating fluorescein, moving fireflies, updating fireflies positions and updating fireflies adjacent radiuses.
Step three, carrying out self-adaptive dynamic deployment on sensor nodes through a firefly algorithm
3.1, initially deploying sensor nodes according to the virtual force, and regarding each sensor node as a firefly, so as to form a firefly group;
3.2 giving the same fluorescein concentration to each sensor node in the throwing nodes at the beginning of the algorithm;
3.3 updating the fluorescein concentration of the sensor node in the following way:
li(t+1)＝(1-ρ)li(t)+γF(xi(t+1))
wherein li(t) denotes the fluorescein concentration of sensor node i at the tth iteration, increasing the fluorescein concentration attenuation coefficient ρ (0 < ρ < 1), F (x)i(t +1)) is the objective function value of node sensor node i at t iterations. The objective function is a function based on the coordinates of the sensor node i,
is the distance between sensor nodes i and j, j being the neighbor node within the sensing radius of sensor node i, i.e. sensor node i,is the radius of the neighboring decision domain of sensor node i at t iterations.
3.4 judging the adjacent node with the sensor node i having higher fluorescein concentration than the sensor node i, moving towards the direction of the adjacent node, wherein the moving probability can be obtained by the following formula:
wherein the content of the first and second substances,k is the neighboring node within the sensing radius of the sensor node i and having a fluorescein concentration lower than that of the sensor node i at the t-th iteration, dik(t) is the distance between sensor nodes i and k at the tth iteration.
And 3.5, updating the position of the sensor node i after movement.
Wherein x isi(t) is the position of the sensor node i in space, s is the iteration step length of position update, | xj(t)-xi(t) | | is the standard euclidean distance.
3.6 after the position of the sensor node is updated, updating the radius of the adjacent decision domain of the firefly, wherein the updating method comprises the following steps:
wherein β is a proportionality constant, rcIs the sensing radius of the sensor node, ntIs a parameter for controlling the number of neighboring fireflies in the neighborhood range, | Ni(t) | denotes dynamic decisionNumber of fireflies within the domain.
Claims (3)
1. A wireless sensor network coverage optimization method based on virtual force and a firefly algorithm is characterized in that firstly, the utilization rate of sensor nodes in the wireless sensor network and the effective coverage rate of the network are used as optimization targets, and a corresponding mathematical model is established; then, carrying out initial layout of the sensor nodes according to the resultant force of the virtual forces among the sensor nodes; finally, carrying out self-adaptive dynamic deployment on the sensor nodes by a firefly algorithm;
the mathematical model comprises a sensor node perception probability model, the regional coverage rate of the sensor node and a fitness function in a coverage optimization problem;
the perceptual probability model is expressed as follows;
wherein, P (c)iG) is the ith sensor node ciAs to the perceived probability of the target g,is a sensor node ciThe distance between the target g and the target g, and r is a sensor node ciRadius of coverage, reIs a sensor node ciA measure of perceived uncertainty error, and reThe value of r, alpha is usually takenλ and β are the perception ranges r-r, respectivelyeAnd r + reThe time perception quality attenuation coefficient, sigma, is various interferences and is a random number which obeys normal distribution;
the area coverage rate R (C) of the sensor node is shown as the following formula:
wherein n is the number of wireless sensor nodes arranged in a monitoring area, and i is less than or equal to n; c is a wireless sensor node set, and C ═ C1,c2,…,cn}，ci＝{xi,xjR, representing sensor node ciBy the coordinate { xi,xjCircle with the center of circle and radius of coverage area r; assuming that a monitoring area is digitally discretized into m multiplied by n pixel points, m and n represent the number of the pixel points, the area of each pixel point is delta multiplied by delta y, and the coverage rate p (x, y, c) of a sensor node set is usedi) Whether each pixel point is covered by a wireless sensor network node or not is represented;
the fitness function in the coverage optimization problem is as follows;
F(x)＝ω1×f1(x)+ω2×(1-f2(x))
wherein f is1(x) Indicating wireless sensor node utilization, f2(x) Representing wireless sensor network coverage, i.e. f2(x) R (c), ic is the total number of sensor nodes deployed in the wireless sensing network, ciI is the number of sensor nodes in working state, omega1+ω2＝1,ω1,ω2The weights of the corresponding functions respectively, and the value depends on the requirement on the comprehensive performance of the network.
2. The method for optimizing the coverage of the wireless sensor network according to claim 1, wherein the method for performing the initial layout of the sensor nodes according to the resultant virtual forces among the sensor nodes comprises the following steps:
2.1 calculating the virtual force F between two sensor nodesijThe calculation method is shown in the following formula,
wherein, aijFor one of the sensor nodes siTo another sensor node sjThe vector angle of (c); dijOne of the sensor nodes s is representediTo another sensor node sjDistance of dthAdjusting the distance threshold of the interaction force attribute between the sensor nodes; c is a critical value of the distance, c is set to 2 times dth；ωaAnd ωrRespectively representing a gravitational coefficient and a repulsive coefficient which are used as the basis for adjusting the density of the virtual force adjusting nodes;
2.2 calculating the resultant force F experienced by the sensor nodeiThe calculation method is shown in the following formula,
wherein, FoFor obstacle to sensor node siActing force of (F)rFor sensor nodes s in hot spot areaiThe acting force of (c);
2.3, updating the position of the sensor node, wherein the position updating method is as follows:
wherein (x)old,yold) To the location before update, (x)new,ynew) For updated position, FixAnd FiyRespectively a resultant force F of virtual forcesiAnd a maximum movable distance of the sensing sensor node, Maxstep.
3. The coverage optimization method of the wireless sensor network according to claim 2, wherein the method for performing adaptive dynamic deployment on the sensor nodes by the firefly algorithm comprises the following steps:
3.1, each sensor node is regarded as a firefly to form a firefly group, and each sensor node in the throwing nodes is endowed with the same fluorescein concentration;
3.2 updating the fluorescein concentration of the sensor node;
3.3 the sensor node moves to the direction of an adjacent node with higher fluorescein concentration than the sensor node according to the movement probability;
3.4 updating the position of the sensor node after the movement;
3.5 updating the radius of the adjacent decision domains of the firefly.
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