This letter investigates an uncrewed aerial vehicle (UAV)-enabled Internet of Things (IoT) architecture that integrates wake-up radio (WuR) and energy harvesting for sustainable device operation, and reveals a tradeoff between transmission frequency and energy consumption.
Abstract
This letter investigates an uncrewed aerial vehicle (UAV)-enabled Internet of Things (IoT) architecture that integrates wake-up radio (WuR) and energy harvesting for sustainable device operation. In the proposed system, UAVs transmit radio-frequency (RF) signals that both trigger device activation and replenish stored energy. The IoT device’s behavior is modeled as a discrete-time Markov chain, which captures the evolution of its battery level and operational state (asleep or awake), accounting for both energy harvesting and energy consumption. Leveraging stochastic geometry and discrete-time Markov-chain analysis, we develop a comprehensive mathematical framework to assess system performance. We reveal a tradeoff between transmission frequency and energy consumption, and demonstrate that tuning of system parameters, such as UAV density, can significantly improve both energy efficiency and transmission reliability.
This work presents a multi-mode energy harvesting-assisted edge computing architecture, integrated with a joint optimization of energy consumption and communication behaviour, aimed at enhancing the sustainability, reliability and autonomy of operation in an industrial IoT context.
Dr. Deepa, M. Mehfooza, Padmavathy Thiruppathi Raj· Microsystem Technologies· 0 citations
This paper investigates a solar-aware trajectory optimization for unmanned aerial vehicles (UAVs) deployed in Internet of Things (IoT) networks for environmental monitoring. We develop a comprehensive energy consumption model that integrates aerodynamic flight dynamics with solar energy harvesting under realistic condi...
Hanan Aljehani, Abdelhamid Salem, K. Hamdi· International Mediterranean...· 0 citations
An AI-driven framework integrates hybrid energy harvesting mechanisms with Deep Reinforcement Learning (DRL) to optimize energy efficiency in IoT systems and achieves up to 300% improvement in network lifetime under low-energy harvesting conditions.
Elkhatim Abuelysar Elmobarak Mohammed Ali· Islamic University Journal o...· 0 citations
This paper investigates the energy consumption minimization problem of MIoT-oriented ISAC systems and builds a layered solution architecture that divides the original problem into independent subproblems and optimizes each alternately according to its mathematical features.
An analytical framework for evaluating the outage probability of ambient Internet of Things device classes communicating directly with a base station is presented and it is highlighted that the optimal device choice depends critically on the operating regime and application demands.
Azzam Al-nahari, R. Jäntti, Yi Zhou et al.· 0 citations