{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T17:14:37Z","timestamp":1783790077984,"version":"3.55.0"},"reference-count":55,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy, Advanced Research Projects Agency-Energy (ARPA-E)","doi-asserted-by":"publisher","award":["DE-AR0000945"],"award-info":[{"award-number":["DE-AR0000945"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy, Advanced Research Projects Agency-Energy (ARPA-E)","doi-asserted-by":"publisher","award":["CMMI 1851635"],"award-info":[{"award-number":["CMMI 1851635"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy, Advanced Research Projects Agency-Energy (ARPA-E)","doi-asserted-by":"publisher","award":["ECCS 2021081"],"award-info":[{"award-number":["ECCS 2021081"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"name":"U.S. National Science Foundation","award":["DE-AR0000945"],"award-info":[{"award-number":["DE-AR0000945"]}]},{"name":"U.S. National Science Foundation","award":["CMMI 1851635"],"award-info":[{"award-number":["CMMI 1851635"]}]},{"name":"U.S. National Science Foundation","award":["ECCS 2021081"],"award-info":[{"award-number":["ECCS 2021081"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human presence within more expansive areas while delivering enhanced accuracy compared to a system that relies on stand-alone sensor nodes. The present work reviews the studies from 2012 to 2022 that use networked sensor nodes to detect indoor occupancy, focusing on PIR-based sensors. Methods are compared based on pivotal ADPs that play a significant role in selecting an occupancy detection system for applications such as Health and Safety or occupant comfort. These parameters include accuracy, information requirement, maximum sensor failure and minimum observation rate, and feasible detection area. We briefly describe the overview of occupancy detection criteria used by each study and introduce a metric called \u201csensor node deployment density\u201d through our analysis. This metric captures the strength of network-level data filtering and fusion algorithms found in the literature. It is hinged on the fact that a robust occupancy estimation algorithm requires a minimal number of nodes to estimate occupancy. This review only focuses on the occupancy estimation models for networked sensor nodes. It thus provides a standardized insight into networked nodes\u2019 occupancy sensing pipelines, which employ data fusion strategies, network-level machine learning algorithms, and occupancy estimation algorithms. This review thus helps determine the suitability of the reviewed methods to a standard set of application areas by analyzing their gaps.<\/jats:p>","DOI":"10.3390\/fi15030116","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T06:00:01Z","timestamp":1679464801000},"page":"116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Indoor Occupancy Sensing via Networked Nodes (2012\u20132022): A Review"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7951-5538","authenticated-orcid":false,"given":"Muhammad","family":"Emad-Ud-Din","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843, USA"},{"name":"J. Mike Walker \u201866 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA"},{"name":"Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA"},{"name":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112704","DOI":"10.1016\/j.rser.2022.112704","article-title":"A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment","volume":"167","author":"Zhang","year":"2022","journal-title":"Renew. Sustain. 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