{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T14:51:49Z","timestamp":1777128709843,"version":"3.51.4"},"reference-count":37,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T00:00:00Z","timestamp":1656892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["62061146001"],"award-info":[{"award-number":["62061146001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2022,7,4]]},"abstract":"<jats:p>The ubiquity of Wi-Fi infrastructure has facilitated the development of a range of Wi-Fi based sensing applications. Wi-Fi sensing relies on weak signal reflections from the human target and thus only supports a limited sensing range, which significantly hinders the real-world deployment of the proposed sensing systems. To extend the sensing range, traditional algorithms focus on suppressing the noise introduced by the imperfect Wi-Fi hardware. This paper picks a different direction and proposes to enhance the quality of the sensing signal by fully exploiting the signal diversity provided by the Wi-Fi hardware. We propose DiverSense, a system that combines sensing signal received from all subcarriers and all antennas in the array, to fully utilize the spatial and frequency diversity. To guarantee the diversity gain after signal combining, we also propose a time-diversity based signal alignment algorithm to align the phase of the multiple received sensing signals. We implement the proposed methods in a respiration monitoring system using commodity Wi-Fi devices and evaluate the performance in diverse environments. Extensive experimental results demonstrate that DiverSense is able to accurately monitor the human respiration even when the sensing signal is under noise floor, and therefore boosts sensing range to 40 meters, which is a 3x improvement over the current state-of-the-art. DiverSense also works robustly under NLoS scenarios, e.g., DiverSense is able to accurately monitor respiration even when the human and the Wi-Fi transceivers are separated by two concrete walls with wooden doors.<\/jats:p>","DOI":"10.1145\/3536393","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T18:50:18Z","timestamp":1657219818000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":47,"title":["DiverSense"],"prefix":"10.1145","volume":"6","author":[{"given":"Yang","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science, Peking University, Beijing, China"}]},{"given":"Dan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Peking University, Beijing, China"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Science and Technology Beijing, Beijing, China and University of Leeds, Leeds, United Kingdom"}]},{"given":"Xuhai","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, United States"}]},{"given":"Yaxiong","family":"Xie","sequence":"additional","affiliation":[{"name":"Princeton University, Princeton, United States"}]},{"given":"Tao","family":"Gu","sequence":"additional","affiliation":[{"name":"Macquarie University, Sydney, Australia"}]},{"given":"Daqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Peking University, Beijing, China, Telecom SudParis and Institut Polytechnique de Paris, Paris, France"}]}],"member":"320","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2789168.2790109"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3463504"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2785956.2787487"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971738"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2746285.2746303"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2014.30"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2015.2504935"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3191755"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3281411.3281425"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3161183"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2011.2146774"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3264944"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2789168.2790129"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2942358.2942393"},{"key":"e_1_2_1_15_1","volume-title":"13th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2016","author":"Vasisht Deepak","year":"2016","unstructured":"Deepak Vasisht, Swarun Kumar, and Dina Katabi. 2016. Decimeter-Level Localization with a Single WiFi Access Point. In 13th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2016, Santa Clara, CA, USA, March 16-18, 2016, Katerina J. Argyraki and Rebecca Isaacs (Eds.). USENIX Association, 165--178. https:\/\/www.usenix.org\/conference\/nsdi16\/technical-sessions\/presentation\/vasisht"},{"key":"e_1_2_1_16_1","volume-title":"We can hear you with Wi-Fi! IEEE Transactions on Mobile Computing 15, 11","author":"Wang Guanhua","year":"2016","unstructured":"Guanhua Wang, Yongpan Zou, Zimu Zhou, Kaishun Wu, and Lionel M Ni. 2016. We can hear you with Wi-Fi! IEEE Transactions on Mobile Computing 15, 11 (2016), 2907--2920."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971744"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2016.2557795"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2789168.2790093"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2017.206"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3078855"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2015.2430294"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411822"},{"key":"e_1_2_1_24_1","volume-title":"WiTraj: Robust Indoor Motion Tracking with WiFi Signals","author":"Wu Dan","year":"2021","unstructured":"Dan Wu, Youwei Zeng, Ruiyang Gao, Shengjie Li, Yang Li, Rahul C Shah, Hong Lu, and Daqing Zhang. 2021. WiTraj: Robust Indoor Motion Tracking with WiFi Signals. IEEE Transactions on Mobile Computing (2021)."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1700143"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2018.2860991"},{"key":"e_1_2_1_27_1","first-page":"1","article-title":"QGesture: Quantifying gesture distance and direction with WiFi signals","volume":"2","author":"Yu Nan","year":"2018","unstructured":"Nan Yu, Wei Wang, Alex X Liu, and Lingtao Kong. 2018. QGesture: Quantifying gesture distance and direction with WiFi signals. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 (2018), 1--23.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3494979"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3264958"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411816"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351279"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2826227"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3369839"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2019.2939791"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287079"},{"key":"e_1_2_1_36_1","volume-title":"HandGest: Hierarchical Sensing for Robust in-the-air Handwriting Recognition with Commodity WiFi Devices","author":"Zhang Jie","year":"2022","unstructured":"Jie Zhang, Yang Li, Haoyi Xiong, Dejing Dou, Chunyan Miao, and Daqing Zhang. 2022. HandGest: Hierarchical Sensing for Robust in-the-air Handwriting Recognition with Commodity WiFi Devices. IEEE Internet of Things Journal (2022)."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3131672.3131695"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3536393","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3536393","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T04:30:32Z","timestamp":1752467432000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3536393"}},"subtitle":["Maximizing Wi-Fi Sensing Range Leveraging Signal Diversity"],"short-title":[],"issued":{"date-parts":[[2022,7,4]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,7,4]]}},"alternative-id":["10.1145\/3536393"],"URL":"https:\/\/doi.org\/10.1145\/3536393","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,4]]},"assertion":[{"value":"2022-07-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}