Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering Scheme for Cognitive Radio Wireless Sensor Networks  

作　　者：Sami Saeed Binyamin Mahmoud Ragab 

机构地区：[1]Computer and Information Technology Department,The Applied College,King Abdulaziz University,Jeddah,21589,Saudi Arabia [2]Information Technology Department,Faculty of Computing and Information Technology,King Abdulaziz University,Jeddah,21589,Saudi Arabia

出　　处：《Computer Systems Science & Engineering》2023年第10期105-119,共15页计算机系统科学与工程（英文）

基　　金：This research work was funded by Institutional Fund Projects under grant no.(IFPIP:14-611-1443);Therefore,the authors gratefully acknowledge technical and financial support provided by the Ministry of Education and Deanship of Scientific Research(DSR),King Abdulaziz University(KAU),Jeddah,Saudi Arabia.

摘　　要：Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited applications.CRWSN is widely utilized by future Internet of Things(IoT)applications.Since a promising technology,Cognitive Radio(CR)can be modelled to alleviate the spectrum scarcity issue.Generally,CRWSN has cognitive radioenabled sensor nodes(SNs),which are energy limited.Hierarchical clusterrelated techniques for overall network management can be suitable for the scalability and stability of the network.This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering(MDMO-EAC)Scheme for CRWSN.The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN.Besides,theMDMOEAC algorithm is based on the dwarf mongoose optimization(DMO)algorithm design with oppositional-based learning(OBL)concept for the clustering process,showing the novelty of the work.In addition,the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency.The presented model is validated using a comprehensive range of experiments,and the outcomes were scrutinized in varying measures.The comparison study stated the improvements of the MDMO-EAC method over other recent approaches.

关 键 词：Cognitive radio wireless sensor networks CLUSTERING dwarf mongoose optimization algorithm fitness function 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术] TP212