Towards Battery-Free Wireless Sensing via Radio-Frequency Energy Harvesting
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Abstract (arXiv)
Diverse Wi-Fi-based wireless applications have been proposed, ranging from daily activity recognition to vital sign monitoring. Despite their remarkable sensing accuracy, the high energy consumption and the requirement for customized hardware modification hinder the wide deployment of the existing sensing solutions. In this paper, we propose REHSense, an energy-efficient wireless sensing solution based on Radio-Frequency (RF) energy harvesting. Instead of relying on a power-hungry Wi-Fi receiver, REHSense leverages an RF energy harvester as the sensor and utilizes the voltage signals harvested from the ambient Wi-Fi signals to enable simultaneous context sensing and energy harvesting. We design and implement REHSense using a commercial-off-the-shelf (COTS) RF energy harvester. Extensive evaluation of three fine-grained wireless sensing tasks (i.e., respiration monitoring, human activity, and hand gesture recognition) shows that REHSense can achieve comparable sensing accuracy with conventional Wi-Fi-based solutions while adapting to different sensing environments, reducing the power consumption by 98.7% and harvesting up to 4.5mW of power from RF energy.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 85 references that are in the map. First posted on arXiv 26 Aug 2024.
Builds on
8 of 85 references.
References · 8
- 2019Making the Invisible Visible: Action Recognition Through Walls and Occlusionscited by 25
- 2020Eliminating the Barriers: Demystifying Wi-Fi Baseband Design and Introducing the PicoScenes Wi-Fi Sensing Platformcited by 10
- 2023Wital: A COTS WiFi Devices Based Vital Signs Monitoring System Using NLOS Sensing Modelcited by 6
- 2024Security Analysis of WiFi-based Sensing Systems: Threats from Perturbation Attackscited by 6
- 2023Password-Stealing without Hacking: Wi-Fi Enabled Practical Keystroke Eavesdroppingcited by 4
- 2021Explore BiLSTM-CRF-Based Models for Open Relation Extractioncited by 2
- 2018Real Time 3D Indoor Human Image Capturing Based on FMCW Radarcited by 1
- 2024EarDA: Towards Accurate and Data-Efficient Earable Activity Sensingcited by 1
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