TL;DR
This work is among the first to integrate DDQN with real-time energy prediction for holistic energy-aware control in RF-powered IoMT, offering a scalable solution for sustainable digital health infrastructure.
This review systematically examines recent progress in AI-enabled energy management strategies for self-powered flexible electronic systems and categorizes AI integration across three key dimensions: at the algorithmic level, predictive models and reinforcement learning enable dynamic power allocation and coordinate mu...
The results of simulations reveal that BISLE WSN demonstrates significant improvement in network lifetime, energy efficiency, reliability, and latency compared to the current protocols, making it suitable for healthcare monitoring in rural areas.
This study presents an AI-enabled symmetric wireless electric vehicle (EV) charging framework that integrates artificial neural networks (ANN) for real-time battery state estimation and reinforcement learning (RL) for adaptive power control. The proposed system addresses efficiency losses and battery degradation typica...
Energy management in battery-powered embedded Artificial Intelligence of Things (AIoT) nodes requires coordinated control of sensing, computation, and wireless communication. This structured review combines a documented search of Scopus, Web of Science Core Collection, and IEEE Xplore with screening of 548 unique recor...
Energy imbalance remains a key challenge in Wireless Sensor Networks (WSNs), as nodes near the base station deplete their energy faster due to heavy forwarding loads. While mobile agents (MAs) have been employed for either data collection or sensor charging, existing approaches lack adaptability and fail to integrate b...
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