Wireless sensor network node deployment method based on particle swarm optimization and mutation operator

Abstract

The invention provides a wireless sensor network node deployment method based on particle swarm optimization and a mutation operator. All sensors are randomly distributed in a target area, the initial coordinates of each sensor are taken as a particle initial position in a particle swarm algorithm, and the coverage rate of a wireless sensor network is taken as a target adaptive value function of the particle swarm algorithm. Through the particle swarm algorithm, a global historical optimal solution is obtained, an optimal value of a particle is obtained according to the optimal solution, and the optimal value is a best position of deploying a sensor in the target area. According to the method, through improving an inertia weight calculation method, the local search ability of the algorithm is improved, through giving a mutation probability to a particle individual, an individual historical optimal solution is recalculated, a condition that a whole algorithm falls into a local optimal solution is effectively avoided, the utilization rate of the sensors is improved, and the cost of constructing a wireless sensor network is reduced.

Description

technical field [0001] The invention belongs to the technical field of wireless sensor networks, and in particular relates to a method for deploying sensor nodes in a wireless sensor network. Background technique [0002] A wireless sensor network is a low-cost, high-adaptability wireless network, which generally consists of many miniature sensor nodes responsible for collecting data and a base station responsible for receiving data. Due to the low cost and high flexibility of the wireless sensor network, it can be applied in many monitoring environments, such as forest fire warning, battlefield monitoring, etc. When establishing a wireless sensor network, the sensors are generally randomly deployed in the target area that needs to be monitored. This deployment method can quickly deploy the sensors, but it will also cause some problems. Random deployment cannot guarantee that the target area is completely covered, and some areas may not be detected by sensors, resulting in ...

AI Technical Summary

Problems solved by technology

The traditional particle swarm optimization algorithm has a fast convergence speed, but it is easy to fall into a local optimal solution, causing the algorithm to end prematurely

Images