CN112954594A - Wireless sensor network node positioning algorithm based on artificial bee colony - Google Patents
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Abstract
The invention discloses a positioning algorithm based on artificial bee colony wireless sensor network nodes, wherein the nodes comprise anchor nodes with known positions and unknown nodes, the method firstly estimates the possible areas of each unknown node before positioning, and compared with the prior art, the method improves the calculation efficiency and accuracy by searching in the whole space; in addition, by using a simulated annealing-artificial bee colony mixed algorithm, on one hand, the algorithm is prevented from falling into a local optimal value through a simulated annealing mechanism, and on the other hand, the node coordinates are estimated in a target area through the artificial bee colony algorithm.
Description
Technical Field
The invention relates to the technical field of wireless sensor networks, in particular to a wireless sensor network node positioning algorithm based on artificial bee colonies.
Background
The Wireless Sensor Network (WSN) is composed of a large number of cheap and small sensor nodes, and has wide application prospects in the civil and military fields. However, whether in military reconnaissance, environmental detection, or medical patient tracking applications, the data should be appended with location information, otherwise the data would lose its meaning of collection. Therefore, how to improve the accuracy of the position information becomes a hot spot in recent years.
Nodes with Global Positioning System (GPS) hardware are referred to as anchor nodes or beacons, and other nodes are referred to as unknown nodes. The location information of the nodes seems to be obtainable by equipping each node with a GPS. However, this method is not feasible due to problems of scale, cost, etc. Moreover, this method cannot achieve the intended effect in an indoor environment. Therefore, in order to more accurately locate the node, various non-GPS-based positioning algorithms are proposed. These algorithms can be divided into two categories: distance-based algorithms and non-distance-based algorithms. Conventional distance-based methods include: time difference of arrival (TDOA), angle of arrival (AOA), time of arrival (TOA), and Received Signal Strength Indication (RSSI). Distance-based methods have higher requirements on their own hardware, but the accuracy of distance-based methods is generally higher than non-distance-based methods.
In many areas of engineering, actual engineering problems are often formulated as optimization problems, and metaheuristic algorithms exhibit powerful global mining capabilities when dealing with such problems. Meta-heuristic algorithms of natural elicitation can generally be divided into two categories: population intelligence (SI) and Evolutionary Algorithms (EA). Many SI algorithms, including the imperial butterfly optimization algorithm (MBO), the elephant grazing optimization algorithm (EHO), the Firefly Algorithm (FA), and the particle swarm optimization algorithm (PSO), have been successfully used for coordinate estimation.
Artificial bee colony Algorithm (ABC), one of SI algorithms for solving optimization problems, was proposed by d. The ABC algorithm simulates the foraging behavior of bees and forms a set of complete theoretical system to solve the numerical optimization problem in 2007. ABC bee colonies can be divided into three categories: hiring bees, observing bees, and reconnaissance bees. The task of employing bees is to collect food sources and share them with the observing bees, which are responsible for judging the quality of the food sources and improving the quality of the food sources. The scout bee's responsibility is to find new food sources when the existing food sources are exhausted.
These metaheuristic-based methods transform the positioning problem into an optimization problem, establishing an objective function. Then, the optimal estimated node position is solved by using a meta-heuristic algorithm. Compared with the traditional positioning method, the node positioning performance is better, but most algorithms still have the problem of low positioning precision due to the randomness of the metaheuristic algorithm, particularly in a noise environment. Therefore, under the influence of noise, a more effective positioning algorithm must be found, so that the positioning algorithm has higher positioning accuracy and efficiency.
Disclosure of Invention
The invention aims to provide a wireless sensor network node positioning algorithm based on an artificial bee colony so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a wireless sensor network node positioning algorithm based on artificial bee colony, wherein the nodes comprise anchor nodes with known positions and unknown nodes, and the positioning of the unknown nodes comprises the following steps:
s100, initializing a wireless sensor network: randomly generating an anchor node and an unknown node in the network, wherein the anchor node measures the distance between the anchor node and the unknown node
Sending the distance information to an unknown node;
s200, unknown nodes in the network measure the distance
Determining a rectangular area which may exist in the device;
s300, setting the boundary of the rectangular area as a search area of a simulated annealing-artificial bee colony hybrid algorithm;
s400, carrying out system modeling, and establishing a target function through the measurement distance between each anchor node and the unknown node and the search area;
s500, independently operating an artificial bee colony algorithm by each unknown node receiving the distance information, initializing parameters including population quantity, maximum cycle times and maximum mining times, and generating an initial solution as a current solution;
s600, searching a new solution by employing bees, calculating the fitness values of the current solution and the new solution, updating the current solution by a simulated annealing mechanism, wherein the probability of accepting the solution with lower fitness is P, and updating the temperature Tm after the solution with lower fitness is accepted;
s700, selecting a solution needing to be improved by observing a peak through a roulette method, reserving the solution with higher fitness through a greedy principle, and increasing the mining frequency of the solution by 1 if the improved solution is not reserved;
s800, judging whether a solution reaching the maximum mining times exists, if so, generating a new solution through scout bees, and if not, recording the optimal solution so far;
and S900, judging whether the maximum cycle number is reached, if not, returning to S600, if so, outputting the optimal estimated coordinates of the unknown nodes, broadcasting the optimal estimated coordinates of the unknown nodes to the wireless sensor network by the unknown nodes, and completing the positioning of the unknown nodes.
Preferably, the anchor node of S100 obtains the distance between the anchor node and the unknown node by a Received Signal Strength Indication (RSSI) method, and utilizes
Represents its distance to an unknown node, where diIs the actual distance of the anchor node from the unknown node, niIs zero mean variance of
Gaussian noise. Wherein P isnIs the noise percentage of the measured distance.
Preferably, in the rectangular region of S200, if the unknown node first determines that there are no less than 3 anchor nodes around the unknown node, then 3 anchor nodes closest to the unknown node are selected, and the coordinate of each anchor node is taken as the center of circle and the radius is taken as the radius
The circles are taken as the circumscribed rectangles of the circles, and the overlapping parts of the rectangles are regarded as the areas where the unknown nodes exist, whereinAnd then simulating an annealing-artificial bee colony mixing algorithm to estimate the coordinates of the nodes in the area.
Preferably, in S400, the mathematical model of the objective function is as follows:
wherein M is the number of anchor nodes within the communication radius of the unknown node, and M > 3, xun,yunIs the coordinate of the unknown node, xi,yiThe coordinates of the ith anchor node within the communication radius,
is the measured distance between the ith anchor node and the unknown node in the communication radius.
Preferably, in S600, the hybrid simulated annealing-artificial bee colony algorithm uses a fitness function F to evaluate the fitness value of each solution, and determines the goodness and badness of the solution, wherein a solution with high fitness is retained as the basis of the next iteration, and a solution with lower fitness is still acceptable with a probability P.
In the above formula Fi(xi) Is the value of the objective function.
Preferably, in S600, the method for updating the current solution by the simulated annealing mechanism is to keep the solution with higher fitness as the current solution, and update the solution with lower fitness as the current solution according to the probability of P, where P is P
Wherein f (v)i,j),f(xi,j) The function values, T, of the new and current solutions, respectivelymIs the current temperature.
Preferably, in S600, the temperature Tm is updated on the premise that T is received by the algorithm when a solution with lower fitness is receivedmHas an attenuation function of
Wherein λ is an annealing factor equal to 0.01.
Compared with the prior art, the invention has the beneficial effects that: the method aims at estimating the possible existing area of each unknown node before positioning, and improves the calculation efficiency and accuracy by searching in the whole space compared with the prior art; according to the method, the hybrid simulated annealing-artificial bee colony algorithm is used, on one hand, the algorithm is prevented from falling into a local optimal value through a simulated annealing mechanism, on the other hand, the node coordinates are estimated in a target area through the artificial bee colony algorithm, and the positioning accuracy of the algorithm under noise interference is improved.
Drawings
Fig. 1 is a schematic structural diagram of a wireless sensor network node distribution.
Fig. 2 is a schematic diagram of the difference between the actual distance and the measured distance under noise interference.
Fig. 3 is a schematic diagram of an unknown node determining its existing location.
FIG. 4 is a flow chart of the hybrid simulated annealing-artificial bee colony algorithm of the present invention.
FIG. 5 is a schematic diagram of the flow of the positioning algorithm based on the hybrid simulated annealing-artificial bee colony algorithm of the present invention
Fig. 6 is a graph comparing the positioning error of the algorithm of the present invention and the common artificial bee colony algorithm when Pn is 10.
FIG. 7 is a graph comparing the positioning error of the algorithm of the present invention and the common artificial bee colony algorithm at different Pn.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
FIG. 1 is a schematic diagram showing the distribution structure of wireless sensor network nodes in the invention
Fig. 2 shows a ranging effect diagram under a noise interference condition, and due to the impression of measurement errors, a distance measured by RSSI is deviated from an actual distance, so that a local optimal value is trapped in a global search node coordinate when a conventional algorithm performs trilateral positioning, and the positioning effect is inaccurate.
The invention aims to overcome the defects of a wireless sensor network node positioning technology with ranging noise, and provides a wireless sensor node positioning method based on an artificial bee colony algorithm. In the positioning stage, the coordinates of the nodes are calculated through the mixed simulated annealing artificial bee colony algorithm, the probability that the algorithm falls into local optimum is reduced, and the positioning accuracy is improved.
The invention provides the following technical scheme: a wireless sensor network node location algorithm, wherein nodes comprise an anchor node with known positions and a position node, and the location of the unknown node position comprises the following steps:
s100, initializing a wireless sensor network: randomly generating an anchor node and an unknown node in the network, wherein the anchor node measures the distance between the anchor node and the unknown node
Sending the distance information to an unknown node;
s200, unknown nodes in the network measure the distance
Determining a rectangular area which may exist in the device;
s300, setting the boundary of the rectangular area as a search area of a simulated annealing-artificial bee colony hybrid algorithm;
s400, carrying out system modeling, and establishing a target function through the measurement distance between each anchor node and the unknown node and the search area;
s500, independently operating an artificial bee colony algorithm by each unknown node receiving the distance information, initializing parameters including population quantity, maximum cycle times and maximum mining times, and generating an initial solution as a current solution;
s600, searching a new solution by employing bees, calculating the fitness values of the current solution and the new solution, updating the current solution by a simulated annealing mechanism, wherein the probability of accepting the solution with lower fitness is P, and updating the temperature Tm after the solution with lower fitness is accepted;
s700, selecting a solution needing to be improved by observing a peak through a roulette method, reserving the solution with higher fitness through a greedy principle, and increasing the mining frequency of the solution by 1 if the improved solution is not reserved;
s800, judging whether a solution reaching the maximum mining times exists, if so, generating a new solution through scout bees, and if not, recording the optimal solution so far;
and S900, judging whether the maximum cycle number is reached, if not, returning to S600, if so, outputting the optimal estimated coordinates of the unknown nodes, broadcasting the optimal estimated coordinates of the unknown nodes to the wireless sensor network by the unknown nodes, and completing the positioning of the unknown nodes.
In the invention, the anchor node of S100 obtains the distance between the anchor node and the unknown node by a received signal strength indication method and utilizes
Represents its distance to unknown node D, where DiIs the actual distance of the anchor node from the unknown node, niIs zero mean variance of
Gaussian noise. Wherein P isnIs the noise percentage of the measured distance.
In the invention, in the rectangular area of S200, if no less than 3 anchor nodes exist around the unknown node, the 3 closest anchor nodes are selected, the coordinate of each anchor node is taken as the center of a circle, and the radius is taken as the radius
Taking the circumscribed rectangles of the circles, and regarding the overlapped parts of the rectangles as the areas where the unknown nodes exist, and then estimating the coordinates of the nodes in the areas by a hybrid simulated annealing-artificial bee colony algorithm.
In the present invention, in S400, the mathematical model of the objective function is as follows:
wherein M is the number of anchor nodes within the communication radius of the unknown node, and M > 3, xun,yunIs the coordinate of the unknown node, xi,yiThe coordinates of the ith anchor node within the communication radius,
is the measured distance between the ith anchor node and the unknown node in the communication radius.
In the invention, S500 comprises the following steps:
step 510: initialization parameters including the population number (CZ), the maximum number of cycles (MCN), the maximum number of mines (limit), where the number of employed bees is equal to the number of observed peaks, is
The task of hiring the peak is to collect the honey source (solution of equation), the task of observing the peak is to evaluate and improve the honey quantity of the honey source (goodness of solution), and the algorithm reaches the optimal solution through iteration by continuously updating the quality of the solution.
Step 520: assume that in a search space with dimension D (D2, which is the coordinate estimate of the present invention), an initial solution is generated in the search area by equation 2
xi,j＝li,j+rand(0,1)*(ui,j-li,j) (2)
Wherein
And u isi,j,li,jThe upper and lower bounds of the search area are the boundaries of the rectangle in step 3, respectively, and rand (0,1) is a randomly generated number distributed in (0, 1). x is the number ofi,jRepresenting the value of the jth dimension in the ith solution.
In the invention, S600 comprises the following steps:
step 610: the hiring bee searches for new solutions by equation 3.
vi,j＝xi,j+θi,j*(xi,j-xk,j) (3)
Wherein
k∈[1,D]And i ≠ k, θi,jIs [ -1, 1 [ ]]Random number in between
Step 620: the hybrid simulated annealing-artificial bee colony algorithm evaluates the fitness value of each solution by using a fitness function F to determine the degree of goodness and badness of the solution, the solution with higher fitness is reserved as the basis of the next iteration, and the solution with lower fitness can still be accepted by a probability P.
In the above formula Fi(xi) Is the value of the objective function.
Step 630: the hybrid simulated annealing-artificial bee colony algorithm receives a differential solution with the probability of P, wherein P is
Wherein f (v)i,j),f(xi,j) The function values, T, of the new and current solutions, respectivelymIs the current temperature.
Step 640: after the algorithm accepts a solution with lower fitness, the value of P decays with decreasing Tm as a function of the decay of Tm
Wherein λ is equalAn annealing factor of 0.01.
Analysis of localization performance
Fig. 4 shows a schematic flow chart of the simulated annealing-artificial bee colony mixing algorithm of the invention. Fig. 5 shows a schematic flow chart of the positioning algorithm based on the hybrid simulated annealing-artificial bee colony algorithm of the invention. The positioning accuracy in the case of Pn 10 by the simulated annealing-artificial bee colony mixing algorithm is shown in fig. 6, and the positioning accuracy in the case of different Pn by the simulated annealing-artificial bee colony mixing algorithm is shown in fig. 7. The improved hybrid algorithm is obviously superior to the basic algorithm in solving the problem of positioning of the wireless sensor network with noise, and the positioning error of the improved hybrid algorithm is obviously smaller than that of the basic algorithm.
Simulation results show that the simulated annealing-artificial bee colony hybrid algorithm introduced by the invention has great advantages in solving the problem of positioning of a wireless sensor network with noise, and relatively accurate positioning of unknown nodes can be realized.
In conclusion, the method and the device estimate the possible areas of each unknown node before positioning, and compared with the prior art that the areas are searched in the whole space, the method and the device improve the calculation efficiency and accuracy; according to the invention, by using the hybrid simulated annealing-artificial bee colony algorithm, on one hand, the algorithm is prevented from falling into a local optimal value through a simulated annealing mechanism, and on the other hand, the node coordinates are estimated in a target area through the artificial bee colony algorithm, so that the problem of premature convergence of the algorithm is solved.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (7)
1. A wireless sensor network node positioning algorithm based on artificial bee colony, the nodes include anchor nodes with known positions and unknown nodes, and the method is characterized in that: the positioning of the unknown node position comprises the following steps:
s100, initializing a wireless sensor network: randomly generating an anchor node and an unknown node in the network, wherein the anchor node measures the distance between the anchor node and the unknown node
Sending the distance information to an unknown node;
s200, unknown nodes in the network measure the distance
Determining a rectangular area which may exist in the device;
s300, setting the boundary of the rectangular area as a search area of a simulated annealing-artificial bee colony hybrid algorithm;
s400, carrying out system modeling, and establishing a target function through the measurement distance between each anchor node and the unknown node and the search area;
s500, independently operating an artificial bee colony algorithm by each unknown node receiving the distance information, initializing parameters including population quantity, maximum cycle times and maximum mining times, and generating an initial solution as a current solution;
s600, searching a new solution by employing bees, calculating fitness values of the current solution and the new solution, updating the current solution by a simulated annealing mechanism, wherein the probability of accepting the solution with lower fitness is P, and updating the temperature Tm after the solution with lower fitness is accepted;
s700, selecting a solution needing to be improved by observing a peak through a roulette method, reserving the solution with higher fitness through a greedy principle, and increasing the mining frequency of the solution by 1 if the improved solution is not reserved;
s800, judging whether a solution reaching the maximum mining times exists, if so, generating a new solution through scout bees, and if not, recording the optimal solution so far;
and S900, judging whether the maximum cycle number is reached, if not, returning to S600, if so, outputting the optimal estimated coordinates of the unknown nodes, broadcasting the optimal estimated coordinates of the unknown nodes to the wireless sensor network by the unknown nodes, and completing the positioning of the unknown nodes.
2. The artificial bee colony based wireless sensor network node positioning algorithm according to claim 1, characterized in that: the anchor node of S100 obtains the distance between the anchor node and the unknown node by a Received Signal Strength Indication (RSSI) method and utilizes
Represents its distance to an unknown node, where diIs the actual distance of the anchor node from the unknown node, niIs zero mean variance of
Gaussian noise. Wherein P isnIs the noise percentage of the measured distance.
3. The artificial bee colony based wireless sensor network node positioning algorithm according to claim 1, characterized in that: in the rectangular area of S200, if there are no less than 3 anchor nodes around the unknown node, selecting the nearest 3 anchor nodes, taking the coordinate of each anchor node as the center of circle and the radius as
The circles are circumscribed rectangles of the circles, and the overlapping parts of the rectangles are regarded as areas where unknown nodes exist.
4. The artificial bee colony based wireless sensor network node positioning algorithm according to claim 1, characterized in that: in S400, the mathematical model of the objective function is as follows:
wherein M is the number of anchor nodes within the communication radius of the unknown node, and M > 3, xun,yunIs the coordinate of the unknown node, xi,yiThe coordinates of the ith anchor node within the communication radius,
is the measured distance between the ith anchor node and the unknown node in the communication radius.
5. The artificial bee colony based wireless sensor network node positioning algorithm according to claim 1, characterized in that: in S600, the hybrid simulated annealing-artificial bee colony algorithm evaluates the fitness value of each solution by using a fitness function F, which is defined as follows:
in the above formula Fi(xi) Is the value of the objective function.
6. The artificial bee colony based wireless sensor network node positioning algorithm according to claim 1, characterized in that: in S600, the method for updating the current solution by the simulated annealing mechanism is to keep the solution with higher fitness as the current solution, and update the solution with lower fitness as the current solution according to the probability of P, where P is
Wherein f (v)i,j),f(xi,j) The function values, T, of the new and current solutions, respectivelymIs the current temperature.
7. The artificial bee colony based wireless sensor network node positioning algorithm according to claim 1, characterized in that: what is needed isThe premise for updating the temperature Tm in S600 is that T is less suitable when a solution is accepted by the algorithmmHas an attenuation function of
Wherein λ is an annealing factor equal to 0.01.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113905389A (en) * | 2021-10-27 | 2022-01-07 | 湖北文理学院 | Wireless sensor network coverage method based on particle swarm optimization imperial butterfly algorithm |
| CN114095953A (en) * | 2021-09-28 | 2022-02-25 | 成都盛科信息技术有限公司 | Wireless sensor network link reliability optimization algorithm based on artificial bee colony algorithm |
| CN115297435A (en) * | 2022-07-27 | 2022-11-04 | 上海应用技术大学 | RSSI self-adaptive ranging model matching method |
| CN115442887A (en) * | 2022-09-01 | 2022-12-06 | 重庆邮电大学 | Indoor positioning method based on cellular network RSSI |
| CN115515144A (en) * | 2022-10-12 | 2022-12-23 | 同济大学 | Heterogeneous AIoT ad hoc network signal full coverage method and device |
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2021
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114095953A (en) * | 2021-09-28 | 2022-02-25 | 成都盛科信息技术有限公司 | Wireless sensor network link reliability optimization algorithm based on artificial bee colony algorithm |
| CN113905389A (en) * | 2021-10-27 | 2022-01-07 | 湖北文理学院 | Wireless sensor network coverage method based on particle swarm optimization imperial butterfly algorithm |
| CN113905389B (en) * | 2021-10-27 | 2023-10-31 | 广东石油化工学院 | Wireless sensor network coverage method based on particle swarm optimization monarch butterfly algorithm |
| CN115297435A (en) * | 2022-07-27 | 2022-11-04 | 上海应用技术大学 | RSSI self-adaptive ranging model matching method |
| CN115297435B (en) * | 2022-07-27 | 2024-04-16 | 上海应用技术大学 | RSSI self-adaptive ranging model matching method |
| CN115442887A (en) * | 2022-09-01 | 2022-12-06 | 重庆邮电大学 | Indoor positioning method based on cellular network RSSI |
| CN115442887B (en) * | 2022-09-01 | 2024-04-05 | 重庆邮电大学 | Indoor positioning method based on RSSI of cellular network |
| CN115515144A (en) * | 2022-10-12 | 2022-12-23 | 同济大学 | Heterogeneous AIoT ad hoc network signal full coverage method and device |
| CN115515144B (en) * | 2022-10-12 | 2024-06-04 | 同济大学 | Heterogeneous AIoT Ad hoc network signal full-coverage method and device |
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Application publication date: 20210611 |