TY - GEN
T1 - WiSenseHub
T2 - 30th International Conference on Mobile Computing and Networking, ACM MobiCom 2024
AU - Mundra, Pratyaksh
AU - Huang, Zhengyu
AU - Hunter, William
AU - Arun, Aditya
AU - Khadela, Dharmi
AU - Sinha, Prachi
AU - Ayyalasomayajula, Roshan
AU - Bharadia, Dinesh
N1 - Publisher Copyright: © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.
PY - 2024/12/4
Y1 - 2024/12/4
N2 - The smart buildings of the future need to understand the movement and occupancy of the people in the environment. Using cameras to provide this context can be privacy-invasive. Alternatively, installing dedicated hardware to sense the environment can be cost-prohibitive and limit ubiquitous adoption. WiFi-based sensing has hence been championed to provide this building-scale sensing, as it allows for both privacy and is ubiquitously deployed in most buildings. However, industry-translatable research in this space has been challenging as no building-scale systems can provide WiFi sensing data. Consequently, many real-world challenges of deploying these sensing systems remain a mystery. To overcome this veil of mystery, we develop and open-source WiSenseHub, a building-scale WiFi-sensing system. We build our system on commercially available WiFi radios, deploy our backend services to collect data on infinitely scalable AWS cloud or a local server desktop, and build a front-end phone-based interface to collect diverse WiFi sensing data. We deployed multiple WiFi radios in our building and collected data for user devices for over 38 hours.
AB - The smart buildings of the future need to understand the movement and occupancy of the people in the environment. Using cameras to provide this context can be privacy-invasive. Alternatively, installing dedicated hardware to sense the environment can be cost-prohibitive and limit ubiquitous adoption. WiFi-based sensing has hence been championed to provide this building-scale sensing, as it allows for both privacy and is ubiquitously deployed in most buildings. However, industry-translatable research in this space has been challenging as no building-scale systems can provide WiFi sensing data. Consequently, many real-world challenges of deploying these sensing systems remain a mystery. To overcome this veil of mystery, we develop and open-source WiSenseHub, a building-scale WiFi-sensing system. We build our system on commercially available WiFi radios, deploy our backend services to collect data on infinitely scalable AWS cloud or a local server desktop, and build a front-end phone-based interface to collect diverse WiFi sensing data. We deployed multiple WiFi radios in our building and collected data for user devices for over 38 hours.
KW - data open-sourcing
KW - server
KW - wi-fi channel
KW - wi-fi localization
UR - https://www.scopus.com/pages/publications/105002556830
U2 - 10.1145/3636534.3697313
DO - 10.1145/3636534.3697313
M3 - Conference contribution
T3 - ACM MobiCom 2024 - Proceedings of the 30th International Conference on Mobile Computing and Networking
SP - 1858
EP - 1865
BT - ACM MobiCom 2024 - Proceedings of the 30th International Conference on Mobile Computing and Networking
PB - Association for Computing Machinery, Inc
Y2 - 18 November 2024 through 22 November 2024
ER -