Analysis of UAV-Enabled IoT Networks With Energy Harvesting and Wake-Up Radio

Multi-mode energy harvesting–enabled edge computing architecture for industrial IoT environments

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.

Solar-Aware Trajectory Optimization for UAV-Aide IoT Network

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 conditions. Building on this model, we formulate a trajectory optimization problem that considers UAV flight path, velocity, and energy dynamics to maximize reliable data collection and transmission throughput. The problem is inherently non-convex due to probabilistic constraints, energy harvesting non-linearities, and mobility restrictions. To overcome these challenges, we propose a successive convex approximation framework that reformulates the problem into tractable convex subproblems, which are solved iteratively with guaranteed convergence. Simulation results across diverse IoT deployment scenarios demonstrate that the proposed design substantially enhances UAV endurance, energy efficiency, and data throughput compared to benchmark approaches. The results show that solar-powered UAV communications are effective for large-scale IoT networks and contribute to more sustainable, reliable airborne sensing systems.

Towards Sustainable IoT: An AI-Driven Framework for Enhanced Energy Harvesting in Wireless Sensor Networks

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.

Energy Minimization Oriented Resource Allocation for Integrated Sensing and Communication in Marine IoT Networks

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.

Outage Analysis of Backscatter-Based Ambient IoT Device Classes with Energy Buffering

This paper presents an analytical framework for evaluating the outage probability of ambient Internet of Things (A-IoT) device classes communicating directly with a base station. Device 1 is a passive backscatter device with minimal storage, while Device 2 is equipped with a supercapacitor that enables energy buffering and optional amplification. The proposed framework jointly accounts for carrier-detection sensitivity, energy harvesting constraints, supercapacitor energy dynamics, and an energy-aware amplification policy, while the energy evolution of buffered devices is modeled using a discrete-time Markov chain (DTMC). The results highlight the interplay between energy availability and communication reliability. Device 2 achieves superior performance in energy-rich regimes due to buffering and amplification gains, whereas Device 1 becomes more robust in energy-constrained regimes, particularly at larger distances or under high payload requirements. These findings highlight that the optimal device choice depends critically on the operating regime and application demands.

Multi-UAV Energy Consumption Minimization for Multilayer Aerial Wireless-Powered MEC: An Online Stochastic Optimization Approach