Study Of Coverage Control In Wireless Sensor Network Based On Genetic Algorithm

With the emergence of a number of new technologies for thecharacteristics of the sensing and intelligent, the information technologyswitches internet communication into internet of things(IoT) communication,and promote the development of the networking, which is an important part ofwireless sensor networks.Coverage control is an important indicator of quality of service ofnetwork. It is mainly that ensuring the quality of service of network, at thesame time, reducing the energy loss and extending the network lifetimethrough a number of technology or protocol optimization. Coverage controlalgorithm not only affects network sensing information, but also affects the lifecycle of network and quality of service and so on. Therefore, it is meaningfulto study coverage control. This paper used the unique advantage of geneticalgorithm for combinatorial optimization problems, improving geneticalgorithm 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 andconstrained multi-population genetic algorithm for wireless sensor networkcoverage control, which used a node sleep scheduling, set the maximumcoverage and node dormancy rate as the objective function, transformedmulti-objective function optimization as a single objective function optimization, maintained the diversity of the population and avoided thepremature convergence of genetic algorithm with the co-evolution in variousgroups. The simulation results show that, the proposed algorithm have validityand stability, and the performance of the algorithms are improved than thegenetic algorithm and the weighted constraint genetic algorithm.Secondly, aiming at the coverage redundancy of complex probabilitysensing model, this paper proposed an energy efficient cover set selection withimprovement NSGA-Ⅱ, which introduced energy consumption coefficient asthe objective function, took a multi-objective genetic algorithm to optimize theselection of set covering, avoided falling into local optimal solution with deleteoperator, retained the best individual with cycle crossover operator, preventedthe algorithm from the later evolution stagnation with the adaptive crossoverprobability and mutation probability. The simulation results show that,compared with the current popular PEAS and OGDC, NSGA-II which isimproved has obvious advantages in the quality of coverage and the solutiontime is less than PEAS.Then, according to the property of coverage holes appeared in the latedeployment, this paper adopted improved NSGA-II to coverage control ofhybrid wireless sensor networks. This paper introduced mobile nodes, set themaximum coverage，maximum node dormancy rate and minimum averagemoving distance as the objective function. The simulation results show thevalidity and the stability of the algorithm.Finally, this paper made a summary of the work and put forward thedirection of research.