{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T15:38:09Z","timestamp":1782833889150,"version":"3.54.5"},"reference-count":29,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T00:00:00Z","timestamp":1620086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wireless sensor nodes are heavily resource-constrained due to their edge form factor, which has motivated increasing battery life through low-power techniques. This paper proposes a power management method that leads to less energy consumption in an idle state than conventional power management systems used in wireless sensor nodes. We analyze and benchmark the power consumption between Sleep, Idle, and Run modes. To reduce sensor node power consumption, we develop fine-grained power modes (FGPM) with five states which modulate energy consumption according to the sensor node\u2019s communication status. We evaluate the proposed method on a test bench Mica2. As a result, the power consumed is 74.2% lower than that of conventional approaches. The proposed method targets the reduction of power consumption in IoT sensor modules with long sleep mode or short packet data in which most networks operate.<\/jats:p>","DOI":"10.3390\/s21093198","type":"journal-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T22:51:42Z","timestamp":1620255102000},"page":"3198","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Low-Power Wireless Sensor Network Using Fine-Grain Control of Sensor Module Power Mode"],"prefix":"10.3390","volume":"21","author":[{"given":"Seongwon","family":"You","sequence":"first","affiliation":[{"name":"Department of Information and Communication Engineering, Chungbuk National University, Chungbuk 28644, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason K.","family":"Eshraghian","sequence":"additional","affiliation":[{"name":"Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor, MI 48104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Herbert C.","family":"Iu","sequence":"additional","affiliation":[{"name":"School of Electrical, Electronic and Computer Engineering, University of Western Australia, Perth 6009, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8945-5743","authenticated-orcid":false,"given":"Kyoungrok","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Chungbuk National University, Chungbuk 28644, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/JSEN.2012.2215733","article-title":"Contextadaptive multimodal wireless sensor network for energy-efficient gas monitoring","volume":"13","author":"Magno","year":"2013","journal-title":"IEEE Sens. 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