A novel network lifetime maximization technique in WSN using energy efficient algorithms.

Recent advances in wireless communication have enabled the development of small, low-cost, wearable sensors, which play a crucial role in applications such as healthcare monitoring, environmental sensing, and industrial automation. However, maximizing network lifetime (NL) and optimizing energy consumption remain key challenges in Wireless Sensor Networks (WSNs). Existing routing algorithms often struggle to balance energy efficiency and service quality, leading to premature network failures. To address this gap, this paper proposes a novel approach that integrates a near-optimal Single Objective Genetic Algorithm (SOGA) and an Advanced Exhaustive Search Algorithm (AESA) to enhance NL in fully connected WSNs. The proposed method optimizes energy-efficient routing by incorporating four quality parameters: proximity ranging, network lifetime, interaction counting, and link effectiveness. By leveraging multi-hop communication and efficient node connectivity, our approach significantly outperforms existing routing protocols in terms of energy conservation and data transmission reliability. A network simulator is utilized to evaluate key performance indicators, including average edge delay, latency, and packet delivery ratio, comparing them against conventional routing protocols such as ad hoc on-demand routing discovery, dynamic source routing, and simplified connection state routing. The findings demonstrate that the proposed method effectively extends NL while maintaining an optimal balance between energy consumption and service quality. This research contributes to the state of the art by providing an advanced energy-efficient routing solution for WSNs, with potential implications for various real-world applications, including smart cities, healthcare monitoring, and industrial IoT deployments.