Hybrid localization scheme using K-fold optimization with machine learning in WSN

作     者：Yadav, Preeti Sharma, Subhash Chandra Rishiwal, Vinay 

作者机构：IIT Roorkee Cloud Comp & Wireless Sensor Lab SRE Campus Saharanpur Uttar Pradesh India MJP Rohilkhand Univ Bareilly Uttar Pradesh India 

出 版 物：《INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS》 (国际通信系统杂志)

年 卷 期：2022年第35卷第12期

页      面：e5206-e5206页

核心收录：

学科分类：0810[工学-信息与通信工程] 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] 

基　　金：M.J.P. Rohilkhand University  Bareilly 

主　　题：localization machine learning optimization algorithm RSSI RTT WSN 

摘      要：Node localization technology can identify and track nodes, making observing data more relevant;for example, information received at the sink node would be useless to the client if node localization data from the sensor region were not included. Localization is described as determining the location of unknown sensor nodes named destination nodes applying the recognized location of anchor nodes based on measurements such as time difference of occurrence, time of occurrence, angle of occurrence, triangulation, and maximum probability. The purpose of node localization is to assign coordinates points to all sensor nodes arbitrarily put in the monitoring region and have an unknown location. Localization of nodes is essential to account for the cause of events that help group sensor querying, routing, and network coverage. In this paper, data transmission among the nodes is done by comparing the received signal strength indicator (RSSI) value with the supervised learning value. If the RSSI value is less than the supervised learning value, the data transmission takes place;else, no transmission. This paper proposes a hybrid localization scheme that effectively uses K-fold optimization with supervised learning and gives good results for distance error and RSSI/energy efficiency. The proposed scheme can effectively detect the optimal path for data transmission, node localization for the destination, and overall performance enhancement using threshold decision making.