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Wireless sensor network optimization method based on improved binary particle swarm and application

A wireless sensor and network optimization technology, applied in specific environment-based services, wireless communications, electrical components, etc., can solve the problems of shortened shortest route energy consumption network life cycle, slow particle convergence speed, easy to fall into local optimum, etc., to achieve The effect of shortened life cycle, fast solution speed and high accuracy

Active Publication Date: 2021-06-01
SOUTH CHINA AGRI UNIV
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Problems solved by technology

[0003] In the above method, the centralized approximation algorithm based on the Voronoi division of the target area converts the coverage of the target area into the coverage of vertices in the Voronoi diagram according to the shortest distance property of the Voronoi diagram, thereby transforming the area coverage problem into point coverage Therefore, it can only guarantee that the coverage is 1, and the minimum number of active nodes required for the calculation of the energy balance coverage model is much larger than the actual need; the wireless sensor network coverage optimization problem based on the particle swarm optimization algorithm has particles that converge during the optimization process The problem of slow speed and easy to fall into local optimum; in the wireless sensor network coverage optimization problem based on ant colony algorithm, although the algorithm can construct a shortest route, it will cause the energy consumption of the shortest route to be too fast and shorten the life cycle of the entire network

Method used

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  • Wireless sensor network optimization method based on improved binary particle swarm and application
  • Wireless sensor network optimization method based on improved binary particle swarm and application
  • Wireless sensor network optimization method based on improved binary particle swarm and application

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Embodiment 1

[0091] Such as figure 1 As shown, the present embodiment provides a wireless sensor network optimization method based on improved binary particle swarm optimization (BPSO), comprising the following steps:

[0092] Since a sensor node has only two states: "working" or "sleep", an intuitive encoding is 0 / 1 encoding; that is, each bit of an individual is either 0 or 1, corresponding to "sleep" and "work" state. The process appears as follows:

[0093] (1) Initialize the overall X=[N][S] is a matrix of N rows and S columns; the number of N populations, S is the number of sensor nodes, the inertia weight ω is set, the learning factors are c1, c2, thresholds θ, γ; One bit per row represents a sensor, and the corresponding value represents the state of the sensor: 0 for "sleep", 1 for "working". Compute coverage for each individual. If there is an individual that does not meet the requirements of COV_RATE, please initialize the individual (here COV_RATE is set to 0.9).

[0094] ...

Embodiment 2

[0136] This embodiment provides a wireless sensor network optimization system based on improved binary particle swarms, including: an initialization module, a parameter setting module, a fitness function building module, a fitness value calculation module, an individual speed and position update module, and an individual optimal position update module, global optimal position update module and output module;

[0137] In this embodiment, the initialization module is used to initialize the overall matrix X, X=[N][S], where N represents the number of populations, S represents the number of sensor nodes, and is used to initialize the best historical position of each particle and the current number of iterations;

[0138] In this embodiment, the parameter setting module is used to set the inertia weight, learning factor and threshold, is used to set the speed range, and sets the maximum number of iterations;

[0139] In this embodiment, the fitness function building block is used ...

Embodiment 3

[0148] This embodiment provides a storage medium, the storage medium can be a storage medium such as ROM, RAM, magnetic disk, optical disk, etc., and the storage medium stores one or more programs. Improved Binary Particle Swarm Optimization Method for Wireless Sensor Networks.

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Abstract

The invention discloses a wireless sensor network optimization method based on an improved binary particle swarm and application. The method comprises the following steps: initializing an overall matrix; setting a speed range, initializing the optimal historical position of each particle, and setting the maximum number of iterations; designing a fitness function; calculating the fitness value of each particle according to the fitness function, and initializing the value and the position of a global optimal position; updating the speed and the position of the individual according to an optimized binary particle swarm algorithm; calculating the fitness value of each individual, and if the fitness value of the current individual is higher than the fitness value of the optimal position of the individual, updating the optimal position of the individual by using the individual; comparing the optimal fitness value of the current individual with the fitness value of the global optimal position, and updating the global optimal position; and iterating repeatedly until an iteration termination condition is met, and then outputting the current global optimal position. According to the method, the wireless sensor network is optimized, the space resources of the network are reasonably distributed, and the accuracy and effectiveness of data measurement are improved.

Description

technical field [0001] The invention relates to the technical field of wireless sensor networks, in particular to a wireless sensor network optimization method and application based on improved binary particle swarms. Background technique [0002] When the number of sensor network nodes and network energy are generally limited, how to control the coverage of wireless sensor networks according to different application environment requirements has become an urgent problem to be solved in wireless sensor networks. Many scholars have focused on the coverage of wireless sensor networks. Research has been carried out, and different coverage control algorithms have been proposed for different applications. The most primitive coverage optimization algorithm is usually based on graph theory and probes, which is only suitable for small-scale wireless sensor networks. Viera et al. divide the network area according to the location of sensor nodes Divided into several Voronoi polygonal r...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W24/02H04W4/38
CPCH04W24/02H04W4/38
Inventor 李康顺刘琼冯颖王文祥
Owner SOUTH CHINA AGRI UNIV
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