Abstract
Abstract
Wireless Sensor Networks (WSNs) are crucial in applications that require minimal human intervention, such as remote habitat monitoring, environmental monitoring, healthcare, agriculture, and surveillance. They are also vital in disaster management, industrial automation, smart cities, and military operations. The increasing adoption of Internet of Things (IoT) and the evolution of Industry 4.0, WSNs have attracted increasing research attention. These networks often employ sensor nodes with limited energy resources, deployed in environments where recharging is not feasible, making energy efficiency a key concern. Consequently, energy depletion frequently leads to node and network failures. To address this challenge, numerous studies have focused on enhancing the energy efficiency of WSNs through clustering mechanisms and energy-efficient routing protocols. Clustering, especially through the application of fuzzy logic to select the CHs, has been shown to extend network longevity and decrease total energy usage. This research aims to optimize existing energy-efficient routing algorithms for clustered WSNs, incorporating fuzzy logic and network coding techniques to rise data transmission rates and extend the operational lifespan of the network. The objectives include conducting a comprehensive literature review to understand the impact of energy-consuming elements, studying clustering functions and protocols, extracting parameters from state-of-the-art approaches, and designing an optimized routing algorithm that increases energy efficiency and network lifetime. The proposed algorithm will be implemented and simulated in a WSN environment, and its performance will be analysed and evaluated against existing algorithms. Initial results from simulations indicate that the E--FL-NC-EEC/D protocol outperforms existing protocols like FL-NC-EEC/D, FL-EEC/D, K-LEACH, FL and LEACH relative to throughput, energy consumption, and network lifetime. The long-term implications of these results suggest that the optimized algorithm can appreciably improve the sustainability and effectiveness of WSNs in numerous applications by minimizing energy intake and extending operational lifespan of nodes, thus contributing to more reliable and efficient monitoring and data collection in remote and critical environments.