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Towards Battery-Free Wireless Sensing via Radio-Frequency Energy Harvesting

Tao Ni, Zehua Sun, Mingda Han, Guohao Lan, Yaxiong Xie, Zhenjiang Li, Tao Gu, Weitao Xu

TL;DR

This work introduces REHSense, a battery-free wireless sensing system powered by RF energy harvesting that uses harvested voltage fluctuations caused by human activity as the sensing signal. By eliminating the need for power-hungry Wi-Fi NICs and hardware modification, REHSense achieves high accuracy in respiration monitoring, human activity recognition, and hand-gesture recognition—comparable to CSI-based approaches but with vastly reduced energy consumption (about a 98.7% reduction) and up to 4.5 mW of harvested power. The design combines a COTS RF energy harvester and an Arduino-based pipeline with Savitzky-Golay filtering, variance-based segmentation, normalization, and CNN classifiers to deliver RM, HAR, and HGR across diverse environments and routers. The work further demonstrates the potential for battery-free sensing and sensing-powered IoT devices, including integration into common smart home devices and prototype demonstrations of sensing-with-powering and sensing-with-communication applications.

Abstract

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.

Towards Battery-Free Wireless Sensing via Radio-Frequency Energy Harvesting

TL;DR

This work introduces REHSense, a battery-free wireless sensing system powered by RF energy harvesting that uses harvested voltage fluctuations caused by human activity as the sensing signal. By eliminating the need for power-hungry Wi-Fi NICs and hardware modification, REHSense achieves high accuracy in respiration monitoring, human activity recognition, and hand-gesture recognition—comparable to CSI-based approaches but with vastly reduced energy consumption (about a 98.7% reduction) and up to 4.5 mW of harvested power. The design combines a COTS RF energy harvester and an Arduino-based pipeline with Savitzky-Golay filtering, variance-based segmentation, normalization, and CNN classifiers to deliver RM, HAR, and HGR across diverse environments and routers. The work further demonstrates the potential for battery-free sensing and sensing-powered IoT devices, including integration into common smart home devices and prototype demonstrations of sensing-with-powering and sensing-with-communication applications.

Abstract

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.
Paper Structure (43 sections, 6 equations, 16 figures, 1 table, 1 algorithm)

This paper contains 43 sections, 6 equations, 16 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. (I) Customized hardware modification.
  3. (II) High power consumption.
  4. Key insights.
  5. Contributions.
  6. Preliminary
  7. Primer on RF Energy Harvesting
  8. Modeling Harvested Energy
  9. Diffraction model formulation.
  10. Modeling the harvested energy.
  11. Feasibility Study
  12. Design and Implementation
  13. System Overview
  14. Signal Pre-processing
  15. Noise cancellation.
  16. ...and 28 more sections

Figures (16)

  • Figure 1: Literature survey on $50$ representative CSI-based sensing works: (a) Wi-Fi NICs in investigated works; (b) Required hardware modifications; and (c) power consumption of $11$ Wi-Fi NICs.
  • Figure 2: Workflow of RF energy harvesting.
  • Figure 3: Illustration of the diffraction model in the FFZ.
  • Figure 4: Feasibility study: harvested voltage signals (normalized) in three wireless sensing tasks.
  • Figure 5: Overview of REHSense.
  • ...and 11 more figures