Research On Node Coverage Optimization Strategy For Wireless Sensor Network

As the key link of wireless sensor networks application,the coverage of wireless sensor network,the quality of coverage performance will be directly related to the quality of service and monitoring performance of the network.Therefore,the paper will analyze the main objective function of the coverage problem by establishing the problem model of node coverage,and study a series of scientific problems related to coverage optimization of wireless sensor networks.Swarm intelligence algorithm and Pareto non-dominated solution set are used to realize the establishment of coverage optimization model.The main work of this paper is as follows:1.Coverage and Connectivity Optimization of Wireless Sensor Networks Based on Swarm Intelligence Algorithm: Aiming at the optimization of coverage and connectivity in wireless sensor network coverage,a node coverage connectivity optimization strategy based on Dynamic Hybrid Search based Artificial Bee Colony Algorithm(DHSABC)is proposed.The chaotic initialization strategy is used to solve the problem of poor algorithm stability,and the dynamic hybrid search strategy is introduced to improve the ergodicity of algorithm search,and the global exploration and development capabilities of the algorithm are balanced through elite solution information.Finally,the DHSABC algorithm is applied to the coverage connectivity optimization problem of wireless sensor networks,which effectively improves the coverage and connectivity of the network.2.Wireless Sensor Networks Node Coverage Optimization Under MultiObjective Swarm Intelligence Algorithm: Aiming at the contradiction between coverage and node connectivity in wireless sensor networks,a multi-objective node coverage optimization model with minimum coverage and node connectivity as constraints is constructed.The node coverage strategy of Improved Multi-Objective Ant-lion Algorithm(IMOALO)is improved.Firstly,the initialized population is improved by the FUCH chaotic map,which increases the diversity of the population,and introduces the disadvantage that the adaptive shrinking boundary improvement algorithm is easy to fall into the local optimum.Then,the time-varying strategy is used to perturb the position of the ants to enhance the optimization ability of the algorithm.The effectiveness of the proposed strategy is verified by comparing the test function with other algorithms.Finally,the IMOALO algorithm is applied to the node coverage optimization problem in wireless sensor networks.The simulation results show that IMOALO can effectively solve the multi-objective optimization coverage problem of wireless sensor network nodes and provide more feasible solutions for decision makers.3.Energy consumption optimization strategy of node network based on swarm intelligence algorithm: Aiming at the problem of energy consumption holes in wireless sensor network coverage problems due to node energy exhaustion and failure,relay nodes are introduced into the network,a network energy consumption optimization strategy based on multi-objective ant lion algorithm optimization FCM(MOALO-FCM)is proposed.The energy consumption of the perception layer,the energy consumption of the aggregation layer,and the number of relay nodes are used as the main optimization objective functions,and the cluster center is optimized by using the multi-objective ant lion algorithm,and the cluster center is used as the deployment location of the relay nodes.Aiming at the problem of uneven clustering caused by the increase of the number of relay nodes,an adaptive membership function revision strategy is proposed,and the elite archive is used to maintain the Pareto optimal solution set.The simulation results show that the MOALO-FCM algorithm can effectively reduce the network energy consumption,prolong the node survival time,and improve the network life cycle.