An Intelligent Cluster Optimization Algorithm for Smart Body Area Networks

作     者：Adil Mushtaq Muhammad Nadeem Majeed Farhan Aadil Muhammad Fahad Khan Sangsoon Lim 

作者机构：Department of Software EngineeringUniversity of Engineering and TechnologyTaxilaPakistan Department of Computer ScienceCOMSATS University IslamabadAttock CampusPakistan Department of Computer EngineeringSungkyul UniversityAnyang430010Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第67卷第6期

页      面：3795-3814页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)(No.NRF-2018R1C1B5038818) 

主　　题：Bodynets WBAN clustering ad-hoc networks whale optimizer artificial neural networks intelligent transportation system 

摘      要：Body Area Networks(BODYNETs)or Wireless Body Area Networks(WBAN),being an important type of ad-hoc network,plays a vital role in multimedia,safety,and traffic management *** BODYNETs,rapid topology changes occur due to high node mobility,which affects the scalability of the *** clustering is one mechanism among many others,which is used to overcome this issue in *** are many clustering algorithms used in this domain to overcome this ***,these algorithms generate a large number of Cluster Heads(CHs),which results in scarce resource utilization and degraded *** this research,an efficient clustering technique is proposed to handle these *** transmission range of BODYNET nodes is dynamically tuned accordingly as per their operational *** optimizing the transmission range,the packet loss ratio is minimized,and link quality is improved,which leads to reduced energy *** select optimal CHs the Whale Optimization Algorithm(WOA)is used based on their fitness,which enhances the network performance by reducing routing *** proposed scheme outclasses the existing state-of-the-art techniques,e.g.,Ant Colony Optimization(ACO),Gray Wolf Optimization(GWO),and Dragonfly Optimization Algorithm(DFA)in terms of energy consumption and cluster building time.