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lechani, taous
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Journal : indonesian journal of electrical engineering and computer science

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EDK-LEACH: improving LEACH protocol-based machine learning in wireless sensor networks Lechani, Taous; Ourari, Samia; Rahmoune, Fayçal; Amari, Said; Termeche, Hayet
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1251-1261

Abstract

In wireless sensor networks (WSNs), many sensor devices are spread throughout the environment with the goal of collecting data and sending them to a base station (BS) for further studies. The issue of their limited battery power has aroused the interest of researchers, and several protocols were developed to optimize energy use and thus increase the network’s lifetime. The present research enhances the well-known low-energy adaptive clustering hierarchy (LEACH) protocol with a new artificial intelligence (AI) protocol named energy distance K-means LEACH (EDK-LEACH). For this purpose, an innovative clustering strategy built on the machine learning K-means algorithm is used in WSNs to improve the cluster formation process and maximise network stability. By implementing an objective function that considers each node’s residual energy and distance from the cluster centre when selecting the cluster head (CH) of each cluster, EDK-LEACH also eliminates the inherent randomness in LEACH during the CH election process. The proposed protocol has the advantage of ensuring better CH distribution throughout the network surface with a balanced load across all network nodes. In comparison with the known LEACH, the simulation results demonstrate the efficiency of our approach: the lifetime of the network is extended and the energy consumption is reduced.
Fuzzy logic–enhanced LEACH protocol for scalable wireless sensor networks Termeche, Hayet; Lechani, Taous; Rahmoune, Fayçal
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp225-236

Abstract

This study aims to enhance the LEACH protocol by mitigating its intrinsic stochasticity through the use of fuzzy c-means (FCM) clustering. This approach enables the design of WSN protocols with improved energy efficiency, stability, and scalability. To this end, two fuzzy logic–based protocols are proposed: CFFC-LEACH for small-scale deployments and VGFC-LEACH for large-scale environments. CFFC-LEACH employs artificial intelligence to generate optimal clusters by determining the appropriate number of clusters and efficiently partitioning the sensing area. VGFC-LEACH addresses wide-area monitoring challenges by dividing the network field into virtual zones of 100 x 100 m² to reduce communication distances. Within each zone, a leader is selected in every round based on residual energy and distance to the base station (BS). Clustering is performed using FCM, while cluster heads (CH) are selected through an objective function. Compared to LEACH and EDK-LEACH, network lifetime (NL) is extended by 61.26% and 46.59% with CFFC-LEACH, and by 245.81% and 657.44% with VGFC-LEACH, respectively. Which demonstrate that the proposed protocols significantly outperform LEACH and EDK-LEACH.
Co-Authors Amari, Said Ourari, Samia Rahmoune, Fayçal Termeche, Hayet