Towards Energy Efficient Wireless Sensing by Leveraging Ambient Wi-Fi Traffic

Aryan Sharma, Junye Li, Deepak Mishra (), Sanjay Jha and Aruna Seneviratne
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Aryan Sharma: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
Junye Li: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
Deepak Mishra: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
Sanjay Jha: School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Aruna Seneviratne: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia

Energies, 2024, vol. 17, issue 2, 1-22

Abstract: Wireless-based sensing of physical environments has garnered tremendous attention recently, and its applications range from intruder detection to environmental occupancy monitoring. Wi-Fi is positioned as a particularly advantageous sensing medium, due to the ubiquity of Wi-Fi-enabled devices in a more connected world. Although Wi-Fi-based sensing using Channel State Information (CSI) has shown promise, existing sensing systems commonly configure dedicated transmitters to generate packets for sensing. These dedicated transmitters substantially increase the energy requirements of Wi-Fi sensing systems, and hence there is a need for understanding how ambient transmissions from nearby Wi-Fi devices can be leveraged instead. This paper explores the potential of Wi-Fi-based sensing using CSI derived from ambient transmissions of Wi-Fi devices. We demonstrate that CSI sensing accuracy is dependent on the underlying traffic type and the Wi-Fi transceiver architecture, and that control packets yield more robust CSI than payload packets. We also show that traffic containing upload data is more suitable for human occupancy counting, using the Probability Mass Function (PMF) of CSI. We further demonstrate that multiple spatially diverse streams of Wi-Fi CSI can be combined for sensing to an accuracy of 99 % . The experimental study highlights the importance of training Wi-Fi sensing systems for multiple transmission sources to improve accuracy. This research has significant implications for the development of energy-efficient Wi-Fi sensing solutions for a range of applications.

Keywords: wireless sensing testbed; Wi-Fi; Channel State Information; machine learning and AI; occupancy counting; IoT; MISO (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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