One of the important research issues in wireless sensor networks(WSNs)is the optimal layout designing for the deployment of sensor nodes.It directly affects the quality of monitoring,cost,and detection capability of W...One of the important research issues in wireless sensor networks(WSNs)is the optimal layout designing for the deployment of sensor nodes.It directly affects the quality of monitoring,cost,and detection capability of WSNs.Layout optimization is an NP-hard combinatorial problem,which requires optimization of multiple competing objectives like cost,coverage,connectivity,lifetime,load balancing,and energy consumption of sensor nodes.In the last decade,several meta-heuristic optimization techniques have been proposed to solve this problem,such as genetic algorithms(GA)and particle swarm optimization(PSO).However,these approaches either provided computationally expensive solutions or covered a limited number of objectives,which are combinations of area coverage,the number of sensor nodes,energy consumption,and lifetime.In this study,a meta-heuristic multi-objective firefly algorithm(MOFA)is presented to solve the layout optimization problem.Here,the main goal is to cover a number of objectives related to optimal layouts of homogeneous WSNs,which includes coverage,connectivity,lifetime,energy consumption and the number of sensor nodes.Simulation results showed that MOFA created optimal Pareto front of non-dominated solutions with better hyper-volumes and spread of solutions,in comparison to multi-objective genetic algorithms(IBEA,NSGA-II)and particle swarm optimizers(OMOPSO,SMOPSO).Therefore,MOFA can be used in real-time deployment applications of large-scale WSNs to enhance their detection capability and quality of monitoring.展开更多
基金This research has been funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University through Research Group No.RG-21-07-09.
文摘One of the important research issues in wireless sensor networks(WSNs)is the optimal layout designing for the deployment of sensor nodes.It directly affects the quality of monitoring,cost,and detection capability of WSNs.Layout optimization is an NP-hard combinatorial problem,which requires optimization of multiple competing objectives like cost,coverage,connectivity,lifetime,load balancing,and energy consumption of sensor nodes.In the last decade,several meta-heuristic optimization techniques have been proposed to solve this problem,such as genetic algorithms(GA)and particle swarm optimization(PSO).However,these approaches either provided computationally expensive solutions or covered a limited number of objectives,which are combinations of area coverage,the number of sensor nodes,energy consumption,and lifetime.In this study,a meta-heuristic multi-objective firefly algorithm(MOFA)is presented to solve the layout optimization problem.Here,the main goal is to cover a number of objectives related to optimal layouts of homogeneous WSNs,which includes coverage,connectivity,lifetime,energy consumption and the number of sensor nodes.Simulation results showed that MOFA created optimal Pareto front of non-dominated solutions with better hyper-volumes and spread of solutions,in comparison to multi-objective genetic algorithms(IBEA,NSGA-II)and particle swarm optimizers(OMOPSO,SMOPSO).Therefore,MOFA can be used in real-time deployment applications of large-scale WSNs to enhance their detection capability and quality of monitoring.
