Research On Coverage Enhancement For WSNs Based On Ant Lion Optimization Algorithm

Wireless sensor networks(WSNs),as a new product of the Internet of Things rapid development,has played an important role in agriculture,military industry and environmental monitoring due to its low energy consumption,low cost and strong scalability.The coverage enhancement of wireless sensor networks is one of the important research fields.In practical production and life applications,most sensor nodes are randomly deployed in the designated monitoring area,which may lead to the network service quality not reaching the expected effect.At the same time,WSNs may be deployed in remote areas such as battlefields,and the failure to supply energy to sensor nodes in time will lead to network paralysis,resulting in serious consequences.In the process of sensor node redeployment,the energy consumption of sensor nodes movement is far more than that of signal transmission between sensor nodes.Therefore,the main research work of this paper is to optimize the network coverage rate and the sensor nodes moving distance in the secondary deployment process by single-objective optimization algorithm and multi-objective optimization algorithm.The following points are the research emphases of this paper:(1)Based on the single-objective ant lion optimization algorithm,this paper proposes the virtual force-directed improved ant lion optimization algorithm(VF-IALO).Firstly,an improved ant lion optimization algorithm(IALO)is proposed.Based on the original ant lion optimization algorithm,this paper re-assign antlions and dynamically reduce the number of antlions.The strategy of continuous ant random walk boundary shrinkage factor is combined.Dynamic weight coefficients of the antlion and elite antlion are changed to update the ant position.Secondly,based on the IALO algorithm,aiming at the network coverage of WSNs and the sensor nodes moving distance during the secondary deployment,this paper proposes VF-IALO algorithm,this paper limit the range of ants’ random walk to reduce the moving distance of the sensor node during the secondary deployment process.this paper introduces the virtual force composed of neighbor nodes force,grid point gravity,and boundary repulsion.The weight coefficients of the virtual force,antlion,and elite antlion dynamically changed to update the ant position.(2)Based on the multi-objective ant lion optimization algorithm,an improved multi-objective ant lion optimization algorithm(NSIMOALO)based on fast non-dominated sorting algorithm is proposed in this paper.Based on the original multi-objective ant-lion optimization algorithm,this algorithm introduces the idea of NSGA-II’s fast non-dominated sorting algorithm and elite strategy.And put forward a more reasonable calculation method of congestion degree;Levy flight strategy is introduced,and the weight coefficient among antlion,elite antlion and Levy flight is dynamically changed,so as to update the position of ants.(3)All the algorithms proposed in this paper are simulated in software,and the results of standard test function show that the IALO algorithm effectively improves the slow convergence speed and easy falling into local optimal solution of the traditional single-objective antlion optimization algorithm,and improves the global optimization ability of the traditional ALO algorithm.NSIMOALO algorithm increases the diversity of population,and has higher convergence and distribution.The two algorithms are applied to the coverage enhancement of WSNs,which can improve the network coverage rate and effectively reduce the sensor nodes average moving distance during the second deployment.