Azzeddine Oudjehane, SAIT Polytechnic, Canada

Clustering is a widely recognized and effective topology control strategy that plays a critical role in extending the operational lifetime and enhancing the scalability of wireless sensor networks (WSNs). The primary criteria employed in clustering techniques typically involve the selection of cluster heads that possess a higher amount of residual energy, in conjunction with periodic rotation of these cluster heads to balance energy consumption across the network. However, in practical network environments, the issue known as the hot spot problem often arises. This occurs when cluster heads located closer to the base station are burdened with a disproportionate amount of relay traffic, especially in multi-hop communication scenarios. As a result, these cluster heads may deplete their energy reserves much more quickly than those located further away, leading to a potential imbalance in energy consumption and reduced overall network performance. To address this issue, we introduce our Reselection-based Unequal Clustering Algorithm (RUCA). This innovative approach involves designing clusters that decrease in size as proximity to the base station increases. By doing so, we aim to minimize the energy load on cluster heads that are nearer to the base station, allowing for a more equitable distribution of energy consumption across the network. Once the clusters are established, the algorithm allows for the re-selection of cluster heads based on a combination of their residual energy levels and their distances from the base station. This dynamic selection process helps ensure that cluster heads are optimized for both energy efficiency and communication effectiveness. Our simulation results provide compelling evidence that the RUCA algorithm significantly improves energy consumption and prolongs the network's operational lifetime when compared to the well-known Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol. These findings suggest that RUCA could serve as a valuable enhancement for future applications of wireless sensor networks, ensuring more sustainable and resilient network performance.

APA:
Oudjehane, A. (2024). An Innovative Reselection Approach for Unequal Clustering in Wireless Sensor Networks. Journal of Science and Engineering Management, 5(1), 11-20. https://doi.org/10.33832/jsem.2024.5.1.02