An energy-focused model for batteryless IoT: Vortex wireless power transfer and fog computing in 6 G networks

Abstract

The Internet of Things (IoT) refers to the networked interconnection of devices that collect, exchange, and analyze data to enable intelligent applications. In emerging sixth-generation (6 G) networks, batteryless IoT devices have gained significant attention, as they rely on ambient energy harvesting rather than traditional batteries. This paper presents an energy-focused model for a 6G-enabled batteryless IoT network that integrates Vortex Wireless Power Transfer (WPT) with fog node coordination to manage energy harvesting and computation offloading. WPT exploits electromagnetic resonance to deliver energy wirelessly. Our vortex‐based model applies exponential attenuation, enhancing energy harvesting for batteryless IoT devices. Then system dynamically assigns IoT devices to optimal WPT zones based on coverage and received power, while simultaneously determining whether tasks should be executed locally or offloaded to Mobile Edge Computing (MEC)-enabled fog nodes, based on real-time energy and latency constraints. To solve the result of the NP-hard optimization problem, we develop an Enhanced Adaptive Quantum Binary Particle Swarm Optimization (EAQBPSO) algorithm that effectively balances workload distribution, energy harvesting, and consumption. Simulation results indicate that our approach significantly outperform traditional methods, achieving improvements of up to 71 % in energy efficiency, nearly 87 % in energy harvesting efficiency, and reducing average energy consumption per task by over 40 %.