TL;DR
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.
Smart buildings increasingly depend on dense, distributed sensing infrastructures to improve energy efficiency, indoor environmental quality and operational flexibility. However, large-scale IoT/WSN deployment is still constrained by wiring effort, battery maintenance and limited access to sensing locations. Energy har...
The proposed intelligent energy harvesting framework provides an efficient and sustainable power solution for next-generation Internet of Things (IoT) devices by integrating multi-source ambient energy harvesting, Maximum Power Point Tracking (MPPT), hybrid energy storage, and machine learning-based energy management....
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.
An Energy-Efficient IoT Sensor Network Framework that integrates intelligent energy harvesting techniques, adaptive sleep scheduling, edge computing, and Artificial Intelligence (AI)-based routing algorithms to optimize power consumption and extend network longevity is proposed.
This survey provides an exhaustive analysis of the existing routing protocols, clustering, optimization and energy management techniques in WSNs and their benefits and drawbacks, with emphasis on EH-WSNs, Cognitive WSNs, and EH-CWSNs.
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.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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