Energy-Aware adaptive virtualization and migration protocol for green IoT wireless sensor networks

Abstract

Abstract The swift expansion of the Internet of Things (IoT) has resulted in heightened energy requirements and sustainability issues inside extensive wireless sensor networks. This research presents the Energy-Aware Adaptive Virtualization and Migration (EAVM) protocol to tackle these difficulties in Green IoT-based Wireless Sensor Networks. The technique combines Federated Deep Reinforcement Learning (FDRL) with hybrid solar–RF energy harvesting to facilitate intelligent and sustainable resource management. EAVM allocates and migrates virtual resources dynamically according to real-time energy conditions, ensuring workload balance and extended network stability. A thorough simulation methodology assesses its performance relative to contemporary state-of-the-art techniques, illustrating that EAVM attains enhanced energy efficiency, scalability, and sustainability within dynamic IoT systems.