Study Of Coverage Contral In Wireless Sensor Networks Based On Genetic Algorithm

With the emergence of a number of new technologies for the characteristics of the sensing and intelligent,the information technology switches internet communication into internet of things(IoT)communication,and promote the development of the networking,which is an important part of wireless sensor networks.Coverage control is an important indicator of quality of service of network.It is mainly that ensuring the quality of service of network,at the same time,reducing the energy loss and extending the network lifetime through a number of technology or protocol optimization.Coverage control algorithm not only affects network sensing information,but also affects the life cycle of network and quality of service and so on.Therefore,it is meaningful to study coverage control.This paper used the unique advantage of genetic algorithm for combinatorial optimization problems,improving genetic algorithm to applied in the wireless sensor networks coverage control.First of all,for the coverage redundancy of simple Boolean sensing model,this paper proposed a weighted multi-population genetic algorithm and constrained multi-population genetic algorithm for wireless sensor network coverage control,which used a node sleep scheduling,set the maximum coverage and node dormancy rate as the objective function,transformed multi-objective function optimization as a single objective function optimization,maintained the diversity of the population and avoided the premature convergence of genetic algorithm with the co-evolution in various groups.The simulation results show that,the proposed algorithm have validity and stability,and the performance of the algorithms are improved than the genetic algorithm and the weighted constraint genetic algorithm.Secondly,aiming at the coverage redundancy of complex probability sensing model,this paper proposed an energy efficient cover set selection with improvement NSGA-II,which introduced energy consumption coefficient as the objective function,took a multi-objective genetic algorithm to optimize the selection of set covering,avoided falling into local optimal solution with delete operator,retained the best individual with cycle crossover operator,prevented the algorithm from the later evolution stagnation with the adaptive crossover probability and mutation probability.Finally,this paper made a summary of the work and put forward the direction of research.