CN108924755B - DV-HOP indoor positioning method based on immune particle swarm optimization - Google Patents
DV-HOP indoor positioning method based on immune particle swarm optimization Download PDFInfo
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Abstract
The invention provides a DV-HOP indoor positioning algorithm based on immune particle swarm optimization, which introduces the immune particle swarm algorithm into a DV-HOP indoor positioning method, improves the particle swarm method by utilizing the immune algorithm, seeks the conditions of maximum iteration precision and best fitness through the cross and variation operation of the particle swarm, improves the diversity maintenance capability of the particle swarm, expands the search space of the solution, improves the convergence speed of the algorithm, and obviously improves the precision and the global search capability. Therefore, compared with the traditional DV-HOP indoor positioning method based on the standard particle swarm algorithm, the positioning precision is remarkably improved. Compared with an indoor positioning method in a distance measurement mode, the method does not need external hardware support, and is low in cost, low in expenditure and capable of reducing human input. On the premise of ensuring higher positioning accuracy, the cost overhead is also considered, and the method is favorable for large-scale popularization and application.
Description
Technical Field
The invention relates to the technical field of indoor wireless sensor networks, in particular to a DV-HOP indoor positioning method based on immune particle swarm optimization.
Background
Wireless Sensor Networks (WSNs) are formed by a multi-hop self-organizing network system through wireless communication by a large number of cheap miniature sensor nodes deployed in a detection area. One of the most basic functions of the wireless sensor network is to know the position information of the occurrence of an event or to acquire the position of a node in real time. Compared with an outdoor environment, the indoor environment is more complex, and the positioning of the wireless sensor network node is more accurate than the positioning of a GPS.
Positioning technology is one of the main supporting technologies of wireless sensor networks, and is receiving wide attention from researchers. Existing wireless sensor network positioning is mainly divided into distance-based positioning and distance-independent positioning. The distance-based positioning algorithm utilizes the signal strength or angle information between nodes for positioning unknown nodes, and needs to be supported by external hardware equipment. The distance-independent positioning algorithm is simple to implement, has no additional requirements on network nodes, has the advantages of low cost, low cost and the like, and is typically represented by a DV-HOP algorithm.
The positioning process of the existing DV-HOP positioning algorithm is generally divided into three stages: in the first stage, all anchor nodes (anchor nodes refer to nodes with known positions) send a data packet to neighbor nodes in a flooding mode, and all the nodes can obtain the related information of the anchor nodes; in the second stage, after receiving the data packet sent by the anchor node, the node calculates and broadcasts a correction value; and in the third stage, the unknown node obtains the estimated distance between the unknown node and each anchor node according to the minimum hop count received in the first stage and the average per-hop distance correction value calculated in the second stage, and then the positioning coordinate of the unknown node is calculated according to algorithms such as trilateration, maximum likelihood estimation (MLA) and Particle Swarm Optimization (PSO).
The particle swarm optimization algorithm is a global optimization algorithm based on a swarm search strategy, each member in a swarm is called a particle and represents a potential feasible solution, and a food position represents a global optimal solution. The particles fly in the D-dimension search space, the speed of the particles is dynamically adjusted according to self experience and the experience of the whole population, and the speed and the position of each particle are updated through two extreme values, wherein one is the optimal solution pbest found by the particles so far, and the other is the optimal solution gbest found by the whole population so far. And calculating a fitness value once every time the position of the particle is updated, then determining new individual extremum and group extremum according to pbest and gbest of all the particles, and updating the position corresponding to the pbest and the position corresponding to the group gbest. However, the standard particle swarm algorithm has the defects of low convergence speed, low precision, easy precocity of the swarm and the like, so that the DV-HOP positioning method based on the standard particle swarm algorithm has the technical defects of low convergence speed, low efficiency, poor positioning precision, weak searching capability and the like.
Disclosure of Invention
In order to solve the technical problems of the indoor positioning method in the prior art, the invention provides a DV-HOP indoor positioning method based on immune particle swarm optimization.
The invention is realized by the following technical scheme:
a DV-HOP indoor positioning method based on immune particle swarm optimization is suitable for an indoor wireless sensor network and comprises the following steps:
step 1, a plurality of anchor nodes and unknown nodes are randomly deployed in a target area of an indoor wireless sensor network, and each anchor node is used for sending data grouping information to neighbor nodes of the anchor node in a flooding mode.
The data packet information includes: identification of anchor node i, anchor node coordinates (x)i，xj) Hop count hi(initialized to 0).
And 2, each unknown node calculates the average hop distance of each anchor node according to the received data grouping information sent by the corresponding anchor node.
And 3, calculating the corrected estimated distance from each unknown node to each anchor node by each unknown node according to the minimum hop count received in the step 1 and the average hop distance calculated in the step 2.
Step 4, initializing particle swarms and determining the speed and the position of each particle; the initialization parameters include: particle population size N, learning factor C1、C2Propagation algebra M, inertia weight omega, and search space dimension D.
Step 5, calculating the fitness value of the current particle according to a preset objective function, and determining the historical optimal value and the global optimal value of the particle swarm; checking the termination condition of iterative computation, and judging whether the preset maximum iteration times or the optimal fitness value condition is reached: if so, finishing the calculation and outputting a result; otherwise, continue to step 6.
Step 6, generation of immune memory cells: sequencing according to the sequence from high affinity to low affinity, and storing M antibodies with high affinity into a memory bank as immune cells.
Step 7, generating an immune vaccine: selecting two antibodies with the highest affinity from the immune memory cells to carry out cross operation, and storing the obtained public subset part into a vaccine bank to be used as a vaccine.
Step 8, updating the speed and the position of each particle according to a formula (12), a formula (13) and a dynamic inertia weight formula (14); and (3) obtaining n new particles after updating, and randomly drawing q immune cells from the memory bank to form a particle population with the scale of n + q.
vi(t+1)＝ωvi(t)+c1r1(pbesti-xi(t))+c2r2(gbesti-xi(t)) (12)
xi(t+1)＝xi(t)+vi(t+1) (13)
Where i is 1,2, …, N, ω is the inertial weight, ωmax、ωminMaximum and minimum inertial weight, t current iteration number, tmaxIs the maximum number of iterations, r1、r2Is [0,1 ]]Random numbers, pbest, uniformly distributed internallyiFor the historically optimal position of particle i, gbestiThe optimal position is the global history of the particle i.
Step 9, promotion or inhibition of antibodies: calculating the selection probability of each particle in the particle population obtained in step 8 according to formula (15):
wherein i is 1,2, …, N + M,xidenotes the ith particle, G (x)i) Expressing the value of the affinity function of the ith particle; p (x)i) Representing the selection probability of the ith particle.
And selecting n particles with the highest probability according to the selection probability to form a new antibody group.
Step 10, obtaining vaccination: and (4) selecting two antibodies with the highest affinity from the antibody group obtained in the step (9) to perform an intercrossing operation, and using the obtained public subset as a vaccination.
Step 11, immunoselection: calculating the fitness value of the vaccination, if the fitness value of the vaccination is smaller than the fitness value in the step 5, receiving the vaccination to perform vaccination operation, otherwise giving up the vaccination and keeping the original value; the rotation executes step 5.
Compared with the prior art, the invention has the beneficial effects that:
(1) the DV-HOP indoor positioning method based on immune particle swarm optimization introduces the immune particle swarm algorithm into the DV-HOP indoor positioning method, improves the particle swarm algorithm by utilizing the immune algorithm, seeks the conditions of maximum iteration precision and best fitness through the cross and variation operation of the particle swarm, improves the diversity maintenance capability of the particle swarm, expands the search space of the solution, improves the convergence speed of the algorithm, and obviously improves the precision and the global search capability. Therefore, compared with the traditional DV-HOP indoor positioning method based on the standard particle swarm algorithm, the positioning precision is remarkably improved.
(2) The DV-HOP indoor positioning method based on immune particle swarm optimization provided by the invention belongs to an indoor positioning method in a non-ranging mode, and compared with the indoor positioning method in a ranging mode, the DV-HOP indoor positioning method based on the immune particle swarm optimization does not need external hardware support, is low in cost and low in expenditure, and reduces the manpower input. On the premise of ensuring higher positioning accuracy, the cost overhead is also considered, and the method is favorable for large-scale popularization and application.
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FIG. 1 is a node plane layout diagram of DV-HOP indoor positioning method based on immune particle swarm optimization.
FIG. 2 is a general flow chart of DV-HOP indoor localization method based on immune particle swarm optimization.
FIG. 3 is a diagram of the analysis results of a simulation experiment of DV-HOP indoor localization method based on immune particle swarm optimization.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1:
the DV-Hop indoor positioning algorithm based on immune particle swarm optimization mainly introduces the immune evolution mechanism in the immune algorithm to be combined with the particle swarm algorithm. The invention takes an area of 100m × 100m as an experimental simulation environment of an indoor wireless sensor network, as shown in fig. 1. All the nodes in the simulated indoor wireless sensor network area are normally communicated with each other, the communication radius is 30m, the population scale is 30, and the maximum iteration number is gmaxMaximum speed v 50maxMaximum inertial weight ω 10max0.8, minimum inertial weight ωmin0.2, learning factor c1＝c21.4962, searching the space dimension D to 3, all the simulation experiments are carried out 200 times, and the average positioning error is used for judging the positioning accuracy of the algorithm:
wherein the content of the first and second substances,
the estimated average position of the unknown node, x is the actual position of the unknown node, Q is the number of the unknown nodes in the network, and R is the communication radius.
As shown in fig. 2, the present embodiment provides a DV-HOP indoor positioning method based on immune particle swarm optimization, which is suitable for an indoor wireless sensor network, and includes the following steps:
step 1, randomly deploying a plurality of anchor nodes and unknown nodes in a target area of an indoor wireless sensor network, and initializing and zeroing hop data information corresponding to each anchor node when deploying the anchor nodes. And transmitting data packet information to the neighbor nodes of each anchor node by using each anchor node in a flooding manner. In this embodiment, the example is illustrated with 20 anchor nodes and 5 unknown nodes arranged as in the experiment of fig. 1.
The data packet information includes: identification of anchor node i, anchor node coordinates (x)i，xj) Hop count hi(initialized to 0).
And 2, each unknown node calculates the average hop distance of each anchor node according to the received data grouping information sent by the corresponding anchor node.
Specifically, the average hop distance of each anchor node is calculated according to the formula (1):
wherein, CiAverage distance per hop, h, for anchor node iijRepresents the minimum number of hops between anchor node i and anchor node j (i ≠ j), (x)i，yi)、(xj,yj) Coordinates representing anchor node i and anchor node j.
Step 3, each unknown node calculates the estimated distance d from each unknown node to each anchor node in the own data packet after correction according to the minimum hop count received in the step 1 (the minimum hop count refers to the minimum value of the hop counts in the data packet of the same anchor node received by the unknown node) and the average hop distance calculated in the step 2i(distance d)iIs the measured distance of the unknown node to the anchor node).
The process of calculating the corrected estimated distance from each unknown node to each anchor node specifically includes:
step 301, calculating the actual distance d between anchor nodes i and j according to formula (3)rij；
Step 302, calculating the measured distance d between anchor nodes i and j according to formula (4)eij；
deij＝ci×hij(4)
Step 303, calculating the average error epsilon of the anchor node i per hop distance according to the formula (5)i；
Wherein, L is the number of anchor nodes in the network;
step 304, calculating the weighted value lambda of the average per-hop distance of the ith anchor node according to the formula (6)i；
Step 305, calculating a corrected value C of the distance per hop of the unknown node according to a formula (7) and a formula (8);
C＝λk×Ck+λs×Cs+λt×Ct(7)
λk+λs+λt＝1 (8)
wherein k, s, t are the three anchor nodes closest to the unknown node.
Step 306, calculating the distance value from the unknown node to the anchor node i in the data packet according to the formula (9);
di＝C×hi(9)
wherein C is the corrected value of the distance per hop of the unknown node, hiA minimum number of hops for anchor node i is received for the unknown node.
Step 4, initializing particle swarms and determining the speed and the position of each particle; the initialization parameters include: particle population size N, learning factor C1、C2Propagation algebra M, inertia weight omega, and search space dimension D.
Step 5, calculating the fitness value of the current particle according to a preset objective function, and determining the historical optimal value and the global optimal value of the particle swarm; checking the termination condition of iterative computation, and judging whether the preset maximum iteration times or the optimal fitness value condition is reached: if so, finishing the calculation and outputting a result; otherwise, continue to step 6.
The method for calculating the particle fitness value specifically comprises the following steps:
calculating the fitness value of each current particle according to the formula (10),
wherein L is the number of anchor nodes, (x)i，yi) For unknown node position coordinates, (x)j，yj) Anchor node position coordinates.
Step 6, generation of immune memory cells: sequencing according to the sequence from high affinity to low affinity, and storing M antibodies with high affinity into a memory bank as immune cells. The affinity represents the proximity between the antibody and the antigen, the particle is represented as the antibody, the solution to the objective function is represented as the antigen, the affinity (x) in the present inventioni) Is composed of
In formula (11), F (x)i) Is the fitness value of particle i.
Step 7, generating an immune vaccine: selecting two antibodies with the highest affinity from the immune memory cells to perform an interdigitation operation, and storing the obtained public subset part into a vaccine bank to be used as a vaccine.
Step 8, updating the speed and the position of each particle according to a formula (12), a formula (13) and a dynamic inertia weight formula (14); obtaining n new particles after updating, and randomly extracting q immune cells from the memory bank to form a particle population with the scale of n + q;
vi(t+1)＝ωvi(t)+c1r1(pbesti-xi(t))+c2r2(gbesti-xi(t)) (12)
xi(t+1)＝xi(t)+vi(t+1) (13)
where i is 1,2, …, N, ω is the inertial weight, ωmax、ωminMaximum and minimum inertial weight, t current iteration number, tmaxIs the maximum number of iterations, r1、r2Is [0,1 ]]Random numbers, pbest, uniformly distributed internallyiFor the historically optimal position of particle i, gbestiThe optimal position is the global history of the particle i.
Step 9, promotion or inhibition of antibodies: calculating the selection probability of each particle in the particle population obtained in step 8 according to formula (15):
in the formula (15), i is 1,2, …, N + M, xiDenotes the ith particle, G (x)i) Expressing the value of the affinity function of the ith particle; p (x)i) Representing the selection probability of the ith particle;
and selecting n particles with the highest probability according to the selection probability to form a new antibody group.
Step 10, obtaining vaccination: and (4) selecting two antibodies with the highest affinity from the antibody group obtained in the step (9) to perform an intercrossing operation, and using the obtained public subset as a vaccination.
Step 11, immunoselection: calculating the fitness value of the vaccination, if the fitness value of the vaccination is smaller than the fitness value in the step 5, receiving the vaccination to perform vaccination operation, otherwise giving up the vaccination and keeping the original value; the rotation executes step 5.
The positioning of the unknown node in the invention is to solve the coordinate (x, y) of the unknown node in the objective function, namely the fitness function.
The fitness function of the particle is:
wherein (x)i，yi) Is the coordinate position of anchor node i, diThe distance between the unknown node and the anchor node is obtained through measurement, and the minimum value point of the fitness function F (x, y) is the required positioning coordinate.
The following experimental demonstration is carried out on the method of the invention by specific examples, and the specific contents are as follows:
1. experimental Environment
In order to verify the positioning effect of the DV-HOP indoor positioning method based on immune particle swarm optimization, simulation experiments are carried out on the DV-HOP indoor positioning method based on immune particle swarm optimization (IA-PSO algorithm) based on the standard particle swarm optimization (PSO algorithm) in the MATLABR2011b environment.
The simulation experiment environment is a planar indoor area of 100m × 100m, 20 anchor nodes with known positions and 5 unknown nodes are randomly arranged in the area.
2. Evaluation index
In the present embodiment, the positioning error value is used as the experimental evaluation index, and the smaller the positioning error value is, the higher the positioning accuracy is.
3. Results of the experiment
In order to verify the effectiveness of the method provided by the invention, the positioning error values of the DV-HOP indoor positioning method based on the standard Particle Swarm Optimization (PSO) and the DV-HOP indoor positioning method based on the immune particle swarm optimization (IA-PSO) are compared, and the comparison result is shown in FIG. 3. As can be seen from FIG. 3, the positioning accuracy of the DV-HOP indoor positioning method based on immune particle swarm optimization (IA-PSO algorithm) is significantly higher than that of the DV-HOP indoor positioning method based on standard particle swarm optimization (PSO algorithm), which indicates that the DV-HOP indoor positioning method based on immune particle swarm optimization (IA-PSO algorithm) provided by the invention can effectively improve the positioning accuracy of the wireless sensor network.
It will be understood by those skilled in the art that the foregoing is merely a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included within the scope of the present invention.
Claims (1)
1. A DV-HOP indoor positioning method based on immune particle swarm optimization is suitable for an indoor wireless sensor network, and is characterized by comprising the following steps:
step 1, randomly deploying a plurality of anchor nodes and unknown nodes in a target area of an indoor wireless sensor network, and sending data grouping information to neighbor nodes of the anchor nodes by using each anchor node in a flooding manner;
the data packet information includes: identification of anchor node i, anchor node coordinates (x)i，xj) Hop count hi(initialized to 0);
when the anchor nodes are deployed, initializing and zeroing corresponding hop count data information between each anchor node;
step 2, each unknown node calculates the average hop distance of each anchor node according to the received data grouping information sent by the corresponding anchor node;
calculating the average hop distance of each anchor node according to formula (2):
wherein, CiAverage distance per hop, h, for anchor node iijRepresents the minimum number of hops between anchor node i and anchor node j (i ≠ j), (x)i，xj)、(yi,yj) Coordinates representing anchor node i and anchor node j;
step 3, calculating the corrected estimated distance from each unknown node to each anchor node by each unknown node according to the minimum hop count received in the step 1 and the average hop distance calculated in the step 2;
the specific operation is as follows:
step 301, calculating the actual distance d between anchor nodes i and j according to formula (3)rij；
Step 302, calculating the measured distance d between anchor nodes i and j according to formula (4)eij；
deij＝ci×hij(4)
Step 303, calculating the average error epsilon of the anchor node i per hop distance according to the formula (5)i；
Wherein M is the number of anchor nodes in the network;
step 304, calculating the weighted value lambda of the average per-hop distance of the ith anchor node according to the formula (6)i；
Step 305, calculating a corrected value C of the distance per hop of the unknown node according to a formula (7) and a formula (8);
C＝λk×Ck+λs×Cs+λt×Ct(7)
λk+λs+λt＝1 (8)
wherein k, s and t are three anchor nodes nearest to the unknown node;
step 306, calculating the distance value from the unknown node to the anchor node i in the data packet according to the formula (9);
di＝C×hi(9)
wherein C is the corrected value of the distance per hop of the unknown node, hiReceiving the minimum hop count of the anchor node i for the unknown node;
step 4Initializing a particle swarm and determining the speed and the position of each particle; the initialization parameters include: particle population size N, learning factor C1、C2Breeding algebra M, inertia weight omega and searching space dimension D;
step 5, calculating the fitness value of the current particle according to a preset objective function, and determining the historical optimal value and the global optimal value of the particle swarm; checking the termination condition of iterative computation, and judging whether the preset maximum iteration times or the optimal fitness value condition is reached: if so, finishing the calculation and outputting a result; otherwise, continuing to execute the step 6;
calculating the fitness value of each current particle according to the formula (10),
step 6, generation of immune memory cells: sequencing according to the sequence from high affinity to low affinity, and storing M antibodies with high affinity into a memory bank as immune cells;
affinity (x)i) Is shown in formula (11):
step 7, generating an immune vaccine: selecting two antibodies with the highest affinity from the immune memory cells to carry out cross operation, and storing the obtained public subset part into a vaccine library to be used as a vaccine;
step 8, updating the speed and the position of each particle according to a formula (12), a formula (13) and a dynamic inertia weight formula (14); obtaining n new particles after updating, and randomly extracting q immune cells from the memory bank to form a particle population with the scale of n + q;
vi(t+1)＝ωvi(t)+c1r1(pbesti-xi(t))+c2r2(gbesti-xi(t)) (12)
xi(t+1)＝xi(t)+vi(t+1) (13)
where i is 1,2, …, N, ω is the inertial weight, ωmax、ωminMaximum and minimum inertial weight, t current iteration number, tmaxIs the maximum number of iterations, r1、r2Is [0,1 ]]Random numbers, pbest, uniformly distributed internallyiFor the historically optimal position of particle i, gbestiThe global historical optimal position of the particle i is obtained;
step 9, promotion or inhibition of antibodies: calculating the selection probability of each particle in the particle population obtained in step 8 according to formula (15):
wherein, i is 1,2, …, N + M, xiDenotes the ith particle, G (x)i) Expressing the value of the affinity function of the ith particle; p (x)i) Representing the selection probability of the ith particle;
selecting n particles with the maximum probability to form a new antibody group according to the selection probability;
step 10, obtaining vaccination: selecting two antibodies with the highest affinity from the antibody group obtained in the step 9 to carry out mutual crossing operation, and taking the obtained public subset as a vaccination;
step 11, immunoselection: calculating the fitness value of the vaccination, if the fitness value of the vaccination is smaller than the fitness value in the step 5, receiving the vaccination to perform vaccination operation, otherwise giving up the vaccination and keeping the original value; the rotation executes step 5.
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