Machine Learning for QoS Optimization and Energy-Efficient in Routing Clustering Wireless Sensors

作     者：Rahma Gantassi Zaki Masood Yonghoon Choi 

作者机构：Department of Electrical Engineering Chonnam National UniversityGwangju61186Republic of Korea 

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

年 卷 期：2025年第82卷第1期

页      面：327-343页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2023S1A5C2A07096111) 

主　　题：LEACH-G-K MDC TSP QoS K-Means 

摘      要：Wireless sensor network(WSN)technologies have advanced significantly in recent *** in WSNs,machine learning algorithms are crucial in selecting cluster heads(CHs)based on various quality of service(QoS)*** paper proposes a new clustering routing protocol employing the Traveling Salesman Problem(TSP)to locate the optimal path traversed by the Mobile Data Collector(MDC),in terms of energy and QoS *** bemore specific,to minimize energy consumption in the CH election stage,we have developed the M-T protocol using the K-Means and the grid clustering *** addition,to improve the transmission phase of the Low Energy Adaptive Clustering-Grid-KMeans(LEACH-G-K)protocol,the MDC is employed as an intermediary between the CH and the sink to improve the wireless sensor network(WSN)*** results of the experiment demonstrate that the M-T protocol enhances various Low Energy Adaptive Clustering protocol(LEACH)improvements such as the LEACH-G-K,LEACH-C,Threshold sensitive Energy Efficient Sensor Networks(TEEN),MDC maximum residual energy leach protocol.