Based On Artificial Colony Algorithm In The Application Of Wireless Sensor Network Coverage Optimization Strategy Research

In recent years, with the rise of the Internet of Things technology, wireless sensornetworks in the international become more high-profile. It has broad market prospects,huge economic benefits, and will change people’s lives. An extension of man’s vision toexpand access to information, to improve the ability to get the most direct and effectiveinformation.As a swarm intelligence optimization algorithm, the artificial bee colonyalgorithm(ABC) derived from the natural behavior of honey bees activities. As anemerging part of the swarm intelligence algorithm, it has a strong advantage to solve theoptimization problem. At the same time, ABC algorithm has some defects, such as slowpost-search, easily trapped in local optimal solution. This paper presents an improvedartificial bee colony algorithm. Pushing skills, derived from the use of mathematics andforeign, guide the algorithm, and increase the perturbation factor, to post-convergencefaster. Through five classic benchmark functions test to prove that the algorithm validity,and effectively improve the convergence speed and accuracy.Coverage optimization is a very important issue in wireless sensor networks.Coverage reflects the network monitoring capabilities. The coverage efficiency is alsoone of quality metrics of the wireless sensor network. In the actual project, it shouldensure the quality of service and guarantee coverage to maximize. In this paper, animproved ABC algorithm for sensor network nodes covers optimization. And it hadestablished the coverage model by Matlab. Simulation test results show that networkoptimization is effective to maximize coverage monitoring area, and reduce theredundancy of the network, and adjust to improve network performance, and prolongthe network lifetime.