CN110839245A - Wireless sensor network node deployment method applied to indoor positioning - Google Patents

Wireless sensor network node deployment method applied to indoor positioning Download PDF

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CN110839245A
CN110839245A CN201911053290.5A CN201911053290A CN110839245A CN 110839245 A CN110839245 A CN 110839245A CN 201911053290 A CN201911053290 A CN 201911053290A CN 110839245 A CN110839245 A CN 110839245A
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wireless sensor
sensor network
grid
beacon
population
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CN110839245B (en
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周后盘
胡进
吴辉
夏鹏飞
胡菲群
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Hangzhou Electronic Science and Technology University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention discloses a wireless sensor network node deployment applied to indoor positioning, which divides a two-dimensional plane space into two-dimensional plane grids, takes the effective coverage rate of nodes, the minimum node number N and the maximum convex hull area formed by beacon nodes deployed in the two-dimensional plane space as an optimization target, prohibits the deployment of other beacon nodes on 8 grid points adjacent to the beacon nodes as constraint conditions, adopts NSGA2 algorithm to carry out iterative optimization, selects a solution which is consistent with the actual condition in the optimal solution set obtained when the termination condition of the iteration is reached as a wireless sensor network node deployment scheme of the indoor positioning, finally obtains the optimal solution set, selects the optimal solution according to the actual requirement, solves the problems that the positioning precision is poor in the indoor positioning and the cost of the node number of the wireless sensor network beacon nodes participating in the positioning is too high when the wireless sensor network node is deployed, under the condition of not increasing additional beacon nodes, the indoor positioning accuracy is improved.

Description

Wireless sensor network node deployment method applied to indoor positioning
Technical Field
The invention relates to the field of indoor positioning, in particular to a wireless sensor network node deployment method applied to the field of indoor positioning.
Background
With the rapid development of Location Based Services (LBS) such as navigation, the demand of people for accurate indoor positioning including market complexes, mines, underground parking lots, etc. is increasing. Currently, the research on indoor positioning in the industry is to improve the accuracy of indoor positioning by filtering sensor signals, fusing with other positioning signals, or compensating for ranging errors and improving a positioning algorithm, but the research on the optimization of a wireless sensor network topology structure providing positioning services is not much.
The Wireless Sensor Network (WSN) is composed of sensor nodes with certain sensing, calculating and communication capabilities, and is widely applied to the fields of national defense and military, industrial sites, emergency rescue and relief and indoor positioning and navigation. Currently, the research on the indoor positioning aspect of wireless sensor network topology optimization is still in a starting stage, a general wireless sensor network topology beacon node layout optimization method is not formed, the wireless sensor network topology of indoor positioning is mostly uniformly deployed, the positioning error is large, and if the positioning accuracy is improved, an additional beacon node needs to be added, so that the deployment cost is increased.
Current research has proven that determining the optimal deployment of wireless sensor network beacons in an area is an NPC problem that can be difficult to solve using conventional methods. Group intelligence algorithms, such as genetic algorithms, have shown good performance in solving problems such as large-scale computation, NPC, etc., so the optimization problem of the wireless sensor network is widely applied. The research adopts a non-dominated rapid sequencing multi-objective genetic algorithm (NSGA2) with an elite strategy to solve the wireless sensor network topology optimization problem to improve the indoor positioning precision without increasing additional deployment cost.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method is used for solving the problem of poor positioning accuracy in the traditional wireless sensor network layout scheme.
The invention discloses a wireless sensor network node deployment method applied to indoor positioning, which comprises the following steps:
step 1, dividing a two-dimensional plane space into two-dimensional plane grids with a horizontal axis of x and a vertical axis of y, assuming a rectangular grid with the grid specification of A & ltB & gt and a square with each grid side length of a, and forming (A +1) grid nodes (B + 1);
step 2, the effective coverage rate P of the nodekcMaximum as optimization target one T1Taking the minimum number N of nodes as an optimization target two T2The maximum convex hull area S formed by the beacon nodes deployed in the two-dimensional plane spaceNThree T as optimization target3. The prohibition of deploying other beacons on 8 mesh points adjacent to the beacon is taken as a constraint.
And 3, performing iterative optimization by adopting an NSGA2 algorithm, and selecting a solution which is consistent with the actual situation from the optimal solution set obtained when the termination condition of the iteration is reached as an indoor positioning wireless sensor network node deployment scheme.
And 4, selecting an optimal solution according to actual requirements in the optimal solution set obtained in the step 3.
Preferably, the step 3 includes the following substeps:
and 3.01, determining a two-dimensional positioning space range, namely coordinates of each grid node.
And 3.02, initializing, defining the population scale pop, the cross rate, the variation rate and the maximum population algebra gen, wherein the chromosome gene coding mode is binary coding. Let gen be 1, randomly generate an initial solution set of pop individuals.
And 3.03, judging the topological structure, namely firstly, judging the gene of each individual in the population, namely judging whether the beacon node is deployed at the adjacent grid intersection point of each grid intersection point with the beacon node, and if so, modifying the gene value corresponding to the grid node to be 0.
And 3.04, calling the step 3.03 after carrying out selection operation and cross operation, and calling the step 3.03 after carrying out mutation operation.
And 3.05, judging whether the first generation population is generated or not, and if the first generation population is gen +1, turning to the step 3.06. Otherwise, go to step 3.04.
And 3.06, merging the parent filial population.
And 3.07, judging whether a new population is generated or not, if so, turning to step 3.08, otherwise, turning to step 3.10.
And 3.08, transferring to 3.09 after the step 3.04 is called.
And 3.09, judging whether the Gen is less than the maximum algebra, and if so, transferring the Gen +1 to a step 3.06. Otherwise, ending.
Step 3.10, fast non-dominated sorting.
And 3.11, calculating the crowding distance.
And 3.12, selecting suitable individuals to form a new population.
Step 3.13, go to step 3.07.
Preferably, in step 2, the effective coverage rate is mathematically expressed as:
Figure BDA0002255888180000021
optimization objective-a mathematical expression is:
Figure BDA0002255888180000022
Figure BDA0002255888180000031
wherein S iskcDenotes the K-fold coverage area, a denotes the area of the monitored area, and m denotes the highest coverage.
The second optimization objective is mathematically expressed as:
Figure DA00022558881864611
wherein N represents the number of beacons deployed in a two-dimensional plane space, SNRepresenting the convex hull area made up of N beacons.
The constraint condition is mathematically expressed as:
Figure BDA0002255888180000032
wherein d (i, j) represents the Euclidean distance from the beacon i to the beacon j, and
i≠j,i＝1,2,3,...N,j＝1,2,3,...N°
compared with the prior art, the invention has the following effects: the invention solves the problems that in indoor positioning, the positioning precision is poor, and the cost of the node number is too high when the beacon nodes of the wireless sensor network participating in positioning are deployed. Under the condition of not increasing additional beacon nodes, the indoor positioning accuracy is improved.
Drawings
Fig. 1 is a flowchart of a wireless sensor network node deployment method for irregular areas according to the present invention;
FIG. 2 is a partial area of an indoor parking lot of a Hangzhou university teaching building;
FIG. 3 is a two-dimensional planar spatial grid division;
FIG. 4 is a 4 degree overlay;
FIG. 5 is a sample diagram of constraints;
FIG. 6 is a gene chain for transforming an indoor two-dimensional planar space into a genetic algorithm through grid point coordinates based on the NSGA2 algorithm;
FIG. 7 is a flow chart of the NSGA2 algorithm;
fig. 8 is an optimized beacon deployment diagram.
Detailed Description
The invention is further described in detail with reference to the drawings and practical examples. Fig. 1 is a flowchart of a wireless sensor network node deployment method applied to indoor positioning according to the present invention.
Step 1: a part of the indoor parking lot area of a Hangzhou university teaching building is selected as an experiment area as shown in figure 2.
Dividing the planar space into two-dimensional planar grids with x as the horizontal axis and y as the vertical axis, and rectangular grids with 12m × 12m as the grid specification, each grid being a square with 1m side length, will form 169 grid nodes, as shown in fig. 3
Step 2: constructing the effective coverage rate P of the nodekcMaximum as optimization target one T1The effective coverage is schematically shown in fig. 4. Taking the minimum number N of nodes as an optimization target two T2The maximum convex hull area S formed by the beacon nodes deployed in the two-dimensional plane spaceNThree T as optimization mode target3. And forbidding other beacons to be deployed on 8 network points adjacent to the beacon as a constraint condition, wherein a constraint condition diagram is shown in fig. 5.
And step 3: the two-dimensional positioning space range, i.e., the coordinates of each grid point, is determined, and the indoor two-dimensional planar space is converted into a genetic algorithm gene chain as shown in fig. 6. All grid points are represented by a chromosome with 169 loci, and if a beacon is placed at a grid point, the locus has a gene value of 1, and if not, 0.
And 4, step 4: the NSGA2 is used as a topological structure optimization model solving algorithm, and the specific process is shown in FIG. 7. The method comprises the following specific steps:
step 4.01: initialization, defining the size of the population size (pop) as 200, the crossover rate as 0.9 and the mutation rate as 0.1. The maximum population generation number (Gen) is 10000, and the chromosome coding mode adopts binary coding.
Step 4.02: and (3) judging the topological structure, namely firstly, carrying out gene judgment on each individual in the population, namely judging whether the adjacent grid intersection point of each grid intersection point with the beacon node is provided with the beacon node or not, and if so, modifying the gene value corresponding to the grid node to be 0.
And 4.03, carrying out selection operation, calling the step 4.02 after carrying out cross operation, and calling the step 4.02 after carrying out mutation operation.
And 4.04, judging whether the first generation population is generated or not, and if the first generation population is Gen +1, turning to the step 4.05. Otherwise, go to step 4.03.
And 4.05, merging the parent filial population.
And 4.06, judging whether a new population is generated or not, if so, turning to the step 4.07, otherwise, turning to the step 4.09.
Step 4.07, call step 4.03, shift to 4.08,
and 4.08, judging whether the Gen is less than 200, and if so, transferring the Gen +1 to the step 4.05. Otherwise, ending.
Step 4.09, fast non-dominated sorting.
And 4.10, calculating the crowding distance.
And 4.11, selecting suitable individuals to form a new population.
Step 4.12, go to step 4.06.
And 5: and (4) selecting the optimal solution according to the optimal solution set obtained in the step (4) and the actual requirement, wherein the solution with the highest coverage rate in the 30 beacon nodes is selected as the optimal solution, as shown in fig. 8.

Claims (3)

1. A wireless sensor network node deployment method applied to indoor positioning is characterized by comprising the following steps:
step 1, dividing a two-dimensional plane space into two-dimensional plane grids with a horizontal axis of x and a vertical axis of y, assuming a rectangular grid with the grid specification of A & ltB & gt and a square with each grid side length of a, and forming (A +1) grid nodes (B + 1);
step 2, the effective coverage rate P of the nodekcMaximum as optimization target one T1Taking the minimum number N of nodes as an optimization target two T2The maximum convex hull area S formed by the beacon nodes deployed in the two-dimensional plane spaceNThree T as optimization target3(ii) a Prohibiting other beacons from being deployed on 8 mesh points adjacent to the beacons as a constraint condition;
step 3, performing iterative optimization by adopting an NSGA2 algorithm, and selecting a solution which is consistent with the actual situation from the optimal solution set obtained when the termination condition of the iteration is reached as an indoor positioning wireless sensor network node deployment scheme;
and 4, selecting an optimal solution according to actual requirements in the optimal solution set obtained in the step 3.
2. The wireless sensor network node deployment method of claim 1, wherein the step 3 comprises the following sub-steps:
step 3.01, determining a two-dimensional positioning space range, namely coordinates of each grid node;
step 3.02, initializing, defining population scale pop, cross rate, variation rate and maximum population algebra gen, wherein the coding mode of the chromosome gene is binary coding; let gen be 1, randomly generate an initial solution set of pop individuals;
step 3.03, judging a topological structure, namely firstly, judging genes of each individual in the population, namely judging whether a beacon node is deployed at an adjacent grid intersection point of each grid intersection point with the beacon node, and if so, modifying the gene value corresponding to the grid node to be 0;
step 3.04, calling step 3.03 after carrying out selection operation and cross operation, and calling step 3.03 after carrying out mutation operation;
step 3.05, judging whether a first generation population is generated or not, and if the first generation population is gen +1, turning to step 3.06; otherwise, go to step 3.04;
step 3.06, merging the parent offspring population;
step 3.07, judging whether a new population is generated, if so, turning to step 3.08, otherwise, turning to step 3.10;
step 3.08, transferring to step 3.09 after the step 3.04 is called;
step 3.09, judging whether Gen is less than the maximum algebra, if so, transferring Gen +1 to step 3.06; otherwise, ending;
step 3.10, fast non-dominated sorting;
step 3.11, calculating the crowding distance;
step 3.12, selecting proper individuals to form a new population;
step 3.13, go to step 3.07.
3. The method for deploying the wireless sensor network nodes according to claim 1, wherein in the step 2, the effective coverage rate is mathematically expressed as:
optimization objective-a mathematical expression is:
Figure FDA0002255888170000022
wherein S iskcRepresenting K re-coverage area, A representing monitoring area, and m representing highest coverage;
the second optimization objective is mathematically expressed as:
Maximize:T2＝N (3)
the optimization objective is expressed by three mathematics as follows:
Maximize:T3＝SN
wherein N represents the number of beacons deployed in a two-dimensional plane space, SNThe representation is formed by N beacons
The area of the formed convex hull;
the constraint condition is mathematically expressed as:
Figure FDA0002255888170000023
wherein d (i, j) represents the Euclidean distance from the beacon i to the beacon j, and
i≠j,i＝1,2,3,...N,j＝1,2,3,...N。
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CN111447627A (en) * 2020-03-16 2020-07-24 浙江邮电职业技术学院 WSN node positioning method based on differential evolution genetic algorithm
CN112996009A (en) * 2021-03-31 2021-06-18 建信金融科技有限责任公司 Wireless device deployment method and device, electronic device and storage medium
US11567158B2 (en) * 2020-08-28 2023-01-31 Solid, Inc. Method of beacon-based positioning system
CN115866807A (en) * 2022-11-17 2023-03-28 华东交通大学 Wireless sensor network node deployment method based on terrain information
CN116702400A (en) * 2023-08-07 2023-09-05 四川国蓝中天环境科技集团有限公司 Mobile city perception optimization method based on buses and mobile sensors

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CN106131862A (en) * 2016-07-01 2016-11-16 厦门大学 Optimization covering method based on multi-objective Evolutionary Algorithm in wireless sensor network
CN107343283A (en) * 2017-06-02 2017-11-10 电子科技大学 A kind of three-dimensional static radio sensing network dispositions method based on genetic algorithm
WO2022193642A1 (en) * 2021-03-15 2022-09-22 河海大学 Reservoir scheduling multi-objective optimization method based on graph convolutional network and nsga-ii

Cited By (9)

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Publication number Priority date Publication date Assignee Title
CN111447627A (en) * 2020-03-16 2020-07-24 浙江邮电职业技术学院 WSN node positioning method based on differential evolution genetic algorithm
CN111447627B (en) * 2020-03-16 2023-04-18 浙江邮电职业技术学院 WSN node positioning method based on differential evolution genetic algorithm
US11567158B2 (en) * 2020-08-28 2023-01-31 Solid, Inc. Method of beacon-based positioning system
CN112996009A (en) * 2021-03-31 2021-06-18 建信金融科技有限责任公司 Wireless device deployment method and device, electronic device and storage medium
CN112996009B (en) * 2021-03-31 2022-11-15 建信金融科技有限责任公司 Wireless device deployment method and device, electronic device and storage medium
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CN116702400A (en) * 2023-08-07 2023-09-05 四川国蓝中天环境科技集团有限公司 Mobile city perception optimization method based on buses and mobile sensors
CN116702400B (en) * 2023-08-07 2023-10-13 四川国蓝中天环境科技集团有限公司 Mobile city perception optimization method based on buses and mobile sensors

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