{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T15:36:00Z","timestamp":1781883360848,"version":"3.54.5"},"reference-count":84,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SMART PAESI PROJECT and collectivity of Corsica"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wireless sensor network (WSN) deployment is an intensive field of research. In this paper, we propose a novel approach based on machine learning (ML) and metaheuristics (MH) for supporting decision-makers during the deployment process. We suggest optimizing node positions by introducing a new hybridized version of the \u201cHitchcock bird-inspired algorithm\u201d (HBIA) metaheuristic algorithm that we named \u201cIntensified-Hitchcock bird-inspired algorithm\u201d (I-HBIA). During the optimization process, our fitness function focuses on received signal maximization between nodes and antennas. Signal estimations are provided by the machine learning \u201cK Nearest Neighbors\u201d (KNN) algorithm working with real measured data. To highlight our contribution, we compare the performances of the canonical HBIA algorithm and our I-HBIA algorithm on classical optimization benchmarks. We then evaluate the accuracy of signal predictions by the KNN algorithm on different maps. Finally, we couple KNN and I-HBIA to provide efficient deployment propositions according to actual measured signal on areas of interest.<\/jats:p>","DOI":"10.3390\/s22249927","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T09:31:01Z","timestamp":1671442261000},"page":"9927","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Efficient WSN Node Placement by Coupling KNN Machine Learning for Signal Estimations and I-HBIA Metaheuristic Algorithm for Node Position Optimization"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3253-6868","authenticated-orcid":false,"given":"Bastien","family":"Poggi","sequence":"first","affiliation":[{"name":"UMR CNRS 6134 SPE, University of Corsica, 20250 Corte, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3718-9447","authenticated-orcid":false,"given":"Chabi","family":"Babatounde","sequence":"additional","affiliation":[{"name":"UAR CNRS 3514 Stella Mare, University of Corsica, 20250 Corte, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7451-1039","authenticated-orcid":false,"given":"Evelyne","family":"Vittori","sequence":"additional","affiliation":[{"name":"UMR CNRS 6134 SPE, University of Corsica, 20250 Corte, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6645-9311","authenticated-orcid":false,"given":"Thierry","family":"Antoine-Santoni","sequence":"additional","affiliation":[{"name":"UMR CNRS 6240 LISA, University of Corsica, 20250 Corte, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"ref_1","first-page":"100123","article-title":"Low cost air pollution monitoring systems: A review of protocols and enabling technologies","volume":"17","author":"Idrees","year":"2020","journal-title":"J. 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