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Multi-sensor network collaborative deployment method and device based on improved particle swarm optimization

A technology for improving particle swarm and sensor networks, which is applied in specific environment-based services, network planning, wireless communication, etc. It can solve the problems of slow particle convergence and easy to fall into local optimum, and achieve a balance between global search ability and local improvement ability , Improve the effective coverage and expand the effect of coverage

Pending Publication Date: 2022-07-29
XIDIAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the existing wireless sensor network deployment problem based on the particle swarm optimization algorithm, there are problems that the particle convergence speed is slow during the optimization process, and it is easy to fall into local optimum.

Method used

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  • Multi-sensor network collaborative deployment method and device based on improved particle swarm optimization
  • Multi-sensor network collaborative deployment method and device based on improved particle swarm optimization
  • Multi-sensor network collaborative deployment method and device based on improved particle swarm optimization

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Experimental program
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Effect test

Embodiment 1

[0056] See figure 1 , figure 1 It is a schematic flowchart of a multi-sensor network collaborative deployment method based on an improved particle swarm algorithm provided by an embodiment of the present invention, including the following steps:

[0057] S1: Divide the detection area of ​​the sensor network into a uniform grid.

[0058] In this embodiment, it is not appropriate to divide the grid too much, too much will lead to too much calculation of the algorithm, and too few divisions, the sensor moves too much each time the deployment position is moved, and it is difficult to find the optimal deployment position. In order to balance the relationship between the improvement degree of the algorithm coverage and the computational complexity of the coverage, this embodiment is preferably divided according to the sensing radius of the sensor. Specifically, in the detection area of ​​the sensor network, the sensing radius of each sensor is evenly divided into 10 parts, and the...

Embodiment 2

[0101] In the following, the improved particle algorithm provided by the present invention is compared with the traditional random deployment algorithm and the existing standard particle algorithm through simulation experiments, so as to compare and illustrate the beneficial effects of the present invention.

[0102] 1. Test conditions:

[0103] Random deployment experiment: It is assumed that 3 static sensors are randomly distributed in the surveillance area [100km×100km]. All sensors have the same sensing radius r s =50km. The parameter of the sensor's uncertainty sensing range is δ=0.6, and the parameter of the probability measurement model is λ 1 =1, λ 2 =0.6, β 1 =3, β 2 = 2. The threshold perception probability of being able to detect the target is defined as 0.5, and 100 Monte Carlo experiments are carried out.

[0104] Particle swarm experiment: The relevant parameters of the sensor and monitoring area are the same as those of the random deployment experiment. The...

Embodiment 3

[0110] On the basis of the above-mentioned first embodiment, this embodiment provides a multi-sensor network cooperative deployment device based on an improved particle swarm algorithm. See Figure 5 , Figure 5 It is a schematic structural diagram of a multi-sensor network cooperative deployment device based on an improved particle swarm algorithm provided by an embodiment of the present invention, which includes:

[0111] The grid division module 1 is used to divide the detection area of ​​the sensor network into a uniform grid;

[0112] Sensor deployment module 2, for randomly deploying multiple sensors on grid points;

[0113] The calculation module 3 is used to calculate the effective coverage rate of the current sensor network area;

[0114] The iterative update module 4 is configured to use the effective coverage of the area as a fitness function, and use the improved particle swarm algorithm to find the global optimal solution, so as to obtain the optimal deployment...

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Abstract

The invention discloses a multi-sensor network collaborative deployment method based on an improved particle swarm algorithm. The method comprises the following steps: dividing a detection area of a sensor network into uniform grids; randomly deploying a plurality of sensors on the grid points; calculating the regional effective coverage rate of the current sensor network; and taking the effective coverage rate of the region as a fitness function, and searching a globally optimal solution by adopting an improved particle swarm algorithm so as to obtain the optimal deployment positions of the plurality of sensors. The method provided by the invention can improve the effective coverage rate of the detection area, enlarge the area coverage range of the sensor network, and accelerate the algorithm convergence speed while improving the utilization rate of sensor resources.

Description

technical field [0001] The invention belongs to the technical field of wireless sensor networks, and in particular relates to a method and device for cooperative deployment of multi-sensor networks based on an improved particle swarm algorithm. Background technique [0002] With the development of wireless sensor network technology, it has been applied in various fields, such as environmental and ecological monitoring, target detection and tracking, and traffic control. In the application of wireless sensor network, efficient sensor deployment strategy is of great significance to improve the detection performance of sensor network. In the case of a small number of sensors, how to use limited resources to achieve optimal detection performance is an important research content of sensor network collaborative deployment technology. Multi-sensor collaborative deployment technology is that multiple sensors realize the collection, perception and processing of target environment in...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W4/38H04W16/18H04W24/02
CPCH04W4/38H04W16/18H04W24/02
Inventor 刘峥周硕谢荣冉磊
Owner XIDIAN UNIV
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