Energy-Efficient IoT Network Design Using Optimization Techniques

Abstract

This paper addresses the critical challenge of energy efficiency in Internet of Things (IoT) networks, where resource-constrained devices operate under limited power availability. The rapid proliferation of IoT applications in smart cities, healthcare, industrial automation, and environmental monitoring has intensified the need for energy-aware network design strategies. The study proposes an optimization-driven framework that integrates mathematical modeling and intelligent algorithms to minimize energy consumption while maintaining network performance and reliability. Various optimization techniques, including linear programming, genetic algorithms, and particle swarm optimization, are explored for tasks such as routing, clustering, and resource allocation. The framework considers multiple parameters such as node residual energy, communication distance, data transmission frequency, and network topology dynamics. Simulation-based evaluation demonstrates that the proposed approach significantly reduces energy consumption, extends network lifetime, and enhances throughput compared to traditional routing protocols. The findings highlight the importance of adaptive and scalable optimization mechanisms in designing sustainable IoT ecosystems. This work contributes to the development of intelligent, energy-efficient IoT infrastructures capable of supporting large-scale deployments in real-world environments.