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Each node content for the spectrum is assigned one channel with a specific bandwidth. However, classical channel\u2010based spectrum sensing and sharing algorithms have great limitations to further optimize spectrum utilization when multiple IoT with different wireless technologies coexisting in the same environment. Therefore, exploring the fine\u2010grained spectrum sensing algorithm becomes an essential work to further improve the spectrum utilization efficiency, especially in the Industrial Scientific Medical (ISM) band. This paper proposes <jats:italic>Subcarrier-Sniffer<\/jats:italic>, a novel subcarrier\u2010level spectrum sensing and sharing method, which utilizes channel state information (CSI) to sense the fine\u2010grained status of each subcarrier of the traditional channel. To evaluate the performance of <jats:italic>Subcarrier-Sniffer<\/jats:italic>, we implemented <jats:italic>Subcarrier-Sniffer<\/jats:italic> by USRP B200min, and the experimental results show that the accuracy of subcarrier\u2010level spectrum sensing could achieve 100% in our settings that the distance between <jats:italic>Subcarrier-Sniffer<\/jats:italic> and the monitor is not greater than 7\u2009m. <jats:italic>Subcarrier-Sniffer<\/jats:italic> could be applied in WiFi and ZigBee, WiFi and BLE, and WiFi and LTE\u2010U coexisted environments for better spectrum utilization.<\/jats:p>","DOI":"10.1155\/2021\/6639894","type":"journal-article","created":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T21:23:21Z","timestamp":1620422601000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Subcarrier\u2010Level Spectrum Sensing Method by Utilizing Fine\u2010Grained Channel State Information in Wireless Networks"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1215-7473","authenticated-orcid":false,"given":"Zheng","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Handong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9182-4827","authenticated-orcid":false,"given":"Hongyu","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4958-2043","authenticated-orcid":false,"given":"Chin-Ling","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0552-6813","authenticated-orcid":false,"given":"Zhenjiang","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,5,7]]},"reference":[{"volume-title":"Cisco visual networking index (VNI) global mobile data traffic, 2017-2022","year":"2018","author":"Barnett T.","key":"e_1_2_9_1_2"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2019.2949815"},{"key":"e_1_2_9_3_2","unstructured":"ZhuY. 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