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

Rahma Gantassi, Zaki Masood, Yonghoon Choi^*
Department of Electrical Engineering Chonnam National University, Gwangju, 61186, Republic of Korea
* Corresponding Author: Yonghoon Choi. Email: email
(This article belongs to the Special Issue: From Nodes to Knowledge: Harnessing Wireless Sensor Networks)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.058143

Received 05 September 2024; Accepted 28 November 2024; Published online 17 December 2024

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Abstract

Wireless sensor network (WSN) technologies have advanced significantly in recent years. Within WSNs, machine learning algorithms are crucial in selecting cluster heads (CHs) based on various quality of service (QoS) metrics. This 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 efficiency. To be more 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 algorithms. In 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) QoS. The 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.

Keywords

LEACH-G-K; MDC; TSP; QoS; K-Means

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

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