Lens-Oppositional Wild Geese Optimization Based Clustering Scheme for Wireless Sensor Networks Assists Real Time Disaster Management  

作　　者：R.Surendran Youseef Alotaibi Ahmad F.Subahi 

机构地区：[1]Department of Computer Science and Engineering,Saveetha School of Engineering,Saveetha Institute of Medical and Technical Sciences,Chennai,India [2]Department of Computer Science,College of Computer and Information Systems,Umm Al-Qura University,Makkah,21955,Saudi Arabia [3]Department of Computer Science,University College of Al Jamoum,Umm Al-Qura University,Makkah,21421,Saudi Arabia

出　　处：《Computer Systems Science & Engineering》2023年第7期835-851,共17页计算机系统科学与工程（英文）

基　　金：This research is funded by the Deanship of Scientific Research at Umm Al-Qura University,Grant Code:22UQU4281755DSR01。

摘　　要：Recently,wireless sensor networks(WSNs)find their applicability in several real-time applications such as disaster management,military,surveillance,healthcare,etc.The utilization of WSNs in the disaster monitoring process has gained significant attention among research communities and governments.Real-time monitoring of disaster areas using WSN is a challenging process due to the energy-limited sensor nodes.Therefore,the clustering process can be utilized to improve the energy utilization of the nodes and thereby improve the overall functioning of the network.In this aspect,this study proposes a novel Lens-Oppositional Wild Goose Optimization based Energy Aware Clustering(LOWGO-EAC)scheme for WSN-assisted real-time disaster management.The major intention of the LOWGO-EAC scheme is to perform effective data collection and transmission processes in disaster regions.To achieve this,the LOWGOEAC technique derives a novel LOWGO algorithm by the integration of the lens oppositional-based learning(LOBL)concept with the traditional WGO algorithm to improve the convergence rate.In addition,the LOWGO-EAC technique derives a fitness function involving three input parameters like residual energy(RE),distance to the base station(BS)(DBS),and node degree(ND).The proposed LOWGO-EAC technique can accomplish improved energy efficiency and lifetime of WSNs in real-time disaster management scenarios.The experimental validation of the LOWGO-EAC model is carried out and the comparative study reported the enhanced performance of the LOWGO-EAC model over the recent approaches.

关 键 词：Disaster management real-time applications wireless sensor networks CLUSTERING bioinspired algorithms 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术] TP212