Optimizing Quality of Service of Clustering Protocols in Large-Scale Wireless Sensor Networks with Mobile Data Collector and Machine Learning

The rise of large-scale wireless sensor networks (LSWSNs), containing thousands nodes (SNs) that spread over large geographic areas, necessitates new Quality Service (QoS) efficient data collection techniques. Data and transmission in LSWSNs are considered the most challenging issues. This study presents a hybrid protocol called MDC-K is combination K-means machine learning clustering algorithm mobile collector (MDC) to improve QoS criteria protocols for LSWSNs. It based on routing model using approach These have capability adopt methods appropriate with best value criteria. Specifically, proposed uses reduce energy consumption cluster head (CH) election phase CH. In addition, used as an intermediate between CH base station (BS) further enhance WSN, minimize time delays during collection, protocol. obtained simulation results demonstrate improves metrics compared LEACH, LEACH-K, MDC maximum residual leach, TEEN protocols.