An Energy-Efficient IoT Sensor Network Framework Using Intelligent Energy Harvesting and Adaptive Routing

Abstract

The rapid growth of Internet of Things (IoT) applications has led to the deployment of large-scale sensor networks in smart cities, healthcare, agriculture, industrial automation, and environmental monitoring systems. However, the limited battery capacity of sensor nodes remains a major challenge affecting network lifetime and reliability. Frequent battery replacement increases maintenance costs and limits scalability, particularly in remote and inaccessible locations. This paper proposes an Energy-Efficient IoT Sensor Network Framework that integrates intelligent energy harvesting techniques, adaptive sleep scheduling, edge computing, and Artificial Intelligence (AI)-based routing algorithms to optimize power consumption and extend network longevity. The proposed system continuously monitors residual node energy, communication quality, and environmental conditions to dynamically select optimal routing paths and operational states. Machine learning algorithms predict energy consumption patterns and network traffic conditions, enabling proactive resource management. Experimental analysis demonstrates significant improvements in network lifetime, packet delivery ratio, energy utilization efficiency, and communication reliability compared to conventional routing approaches. The proposed framework offers a sustainable and scalable solution for next-generation IoT sensor networks operating in energy-constrained environments.