Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm-Based Clustering Scheme for Augmenting Network Lifetime in WSNs  

作　　者：N Tamilarasan SB Lenin P Mukunthan NC Sendhilkumar 

机构地区：[1]Department of Electronics and Communications Engineering,Sri Indu College of Engineering and Technology,Hyderabad,Telangana,India [2]Department of Electronics and Communications Engineering,Sri Manakula Vinayagar Engineering College,Pondicherry,India

出　　处：《China Communications》2024年第9期159-178,共20页中国通信（英文版）

摘　　要：In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.

关 键 词：Adaptive Grasshopper Optimization Algorithm(AGOA) Cluster Head(CH) network lifetime Teaching-Learning-based Optimization Algorithm(TLOA) Wireless Sensor Networks(WSNs) 

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