Wireless sensor networks optimization covering algorithms based on genetic algorithms

Sun Zeyu^{1, 2}, Yang Tao¹, Shu Yunxing¹

COMPUTER MODELLING & NEW TECHNOLOGIES 2014 18(4) 50-56

¹ Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang 471023, China
² Electrical and information Engineering, Xi’an JiaoTong University Xi’an 710061, China

This paper starts with two methods applied widely of computational intelligence; Evolutionary computing and swarm intelligence. It makes the Genetic Algorithms (GA) that is classic in evolutionary computing and genetic algorithm that is representative in swarm intelligence as its study foundation. It presents theory and characteristic of the two methods to seek the application of intelligent optimization in engineering practice. In application, in view of the feature that wireless sensor network (WSN) must possess auto-organization, auto-adaptation and robustness, especially, energy of WSN is very limited, this paper fully utilizes the advantages of computational intelligence, marries together both the research focuses. It proposes some methods and ideas for applying computational intelligence to solve optimization problems of WSN. This paper depicts coverage problem of WSN, for the feature that this problem is the problem of multi objective optimization, under the topology control of GA, it applies GA based on sorting to solve the problem, then improves this algorithm to maintain population diversity and obtain high-quality, well distributed solutions. The algorithm it proposes realizes the aim that using the least number of sensor nodes to achieve the best coverage, which is able to save energy of the network, decrease the interference between signals and prolong the network life-time.