Hybrid Marine Predators Optimization and Improved Particle Swarm Optimization-Based Optimal Cluster Routing in Wireless Sensor Networks(WSNs)  

作　　者：A.Balamurugan Sengathir Janakiraman M.Deva Priya A.Christy Jeba Malar 

机构地区：[1]Department of Computer Science&Engineering,KPR Institute of Engineering and Technology,Coimbatore,Tamilnadu,India [2]Department of Information Technology,CVR College of Engineering,Hyderabad,Telangana,India [3]Department of Computer Science&Engineering,Sri Eshwar College of Engineering,Coimbatore,Tamilnadu,India [4]Department of Information Technology,Sri Krishna College of Technology,Coimbatore,Tamilnadu,India

出　　处：《China Communications》2022年第6期219-247,共29页中国通信（英文版）

摘　　要：Wireless Sensor Networks(WSNs)play an indispensable role in the lives of human beings in the fields of environment monitoring,manufacturing,education,agriculture etc.,However,the batteries in the sensor node under deployment in an unattended or remote area cannot be replaced because of their wireless existence.In this context,several researchers have contributed diversified number of cluster-based routing schemes that concentrate on the objective of extending node survival time.However,there still exists a room for improvement in Cluster Head(CH)selection based on the integration of critical parameters.The meta-heuristic methods that concentrate on guaranteeing both CH selection and data transmission for improving optimal network performance are predominant.In this paper,a hybrid Marine Predators Optimization and Improved Particle Swarm Optimizationbased Optimal Cluster Routing(MPO-IPSO-OCR)is proposed for ensuring both efficient CH selection and data transmission.The robust characteristic of MPOA is used in optimized CH selection,while improved PSO is used for determining the optimized route to ensure sink mobility.In specific,a strategy of position update is included in the improved PSO for enhancing the global searching efficiency of MPOA.The high-speed ratio,unit speed rate and low speed rate strategy inherited by MPOA facilitate better exploitation by preventing solution from being struck into local optimality point.The simulation investigation and statistical results confirm that the proposed MPOIPSO-OCR is capable of improving the energy stability by 21.28%,prolonging network lifetime by 18.62%and offering maximum throughput by 16.79%when compared to the benchmarked cluster-based routing schemes.

关 键 词：Marine Predators Optimization Algorithm(MPOA) Particle Swarm Optimization(PSO) Optimal Cluster-based Routing Cluster Head(CH)selection Wireless Sensor Networks(WSNs) 

分 类 号：TN929.5[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程] TP212.9[自动化与计算机技术—控制理论与控制工程]