Self‐Evolving Energy Harvesting Framework for IoT Sensor Nodes Using Bio‐Inspired Scheduling Algorithms

The Internet of Things (IoT) market experiences rapid growth, which creates a requirement for energy management systems that can independently operate in a sustainable manner. Traditional energy harvesting (EH) techniques depend on either static methods or computation‐intensive processes, which limit their ability to function in changing environmental conditions. This paper presents a self‐evolving energy harvesting system that uses hybrid bio‐inspired scheduling algorithms to control energy harvesting and consumption throughout real‐time operations. ACO creates energy source priority lists based on past harvesting performance because PSO enables operators to shift their scheduling plans between different modes without incurring heavy computational requirements. The framework uses mathematical modeling to express its energy behavior and system flexibility and environmental sustainability performance. Simulation tests conducted in a diverse smart‐farm IoT environment show that our system achieves 35% better energy efficiency and 42% longer network operation time when compared to traditional static systems and learning‐based methods. The current research makes an important scientific contribution by developing an energy management system that operates with minimal resource demands and can adjust to various situations, which connects bio‐inspired research with actual resource‐limited IoT applications.