{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T14:05:25Z","timestamp":1773583525275,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,4,23]],"date-time":"2023-04-23T00:00:00Z","timestamp":1682208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sicilian Region"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Network"],"abstract":"<jats:p>With the explosion of big data, the implementation of distributed machine learning mechanisms in wireless sensor networks (WSNs) is becoming required for reducing the number of data traveling throughout the network and for identifying anomalies promptly and reliably. In WSNs, the above need has to be considered along with the limited energy and processing resources available at the nodes. In this paper, we tackle the resulting complex problem by designing a multi-criteria protocol CINE that stands for \u201cClustered distributed learnIng exploiting Node centrality and residual Energy\u201d for distributed learning in WSNs. More specifically, considering the energy and processing capabilities of nodes, we design a scheme that assumes that nodes are partitioned in clusters and selects a central node in each cluster, called cluster head (CH), that executes the training of the machine learning (ML) model for all the other nodes in the cluster, called cluster members (CMs). In fact, CMs are responsible for executing the inference only. Since the CH role requires the consumption of more resources, the proposed scheme rotates the CH role among all nodes in the cluster. The protocol has been simulated and tested using real environmental data sets.<\/jats:p>","DOI":"10.3390\/network3020013","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T06:20:42Z","timestamp":1682317242000},"page":"253-268","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Clustered Distributed Learning Exploiting Node Centrality and Residual Energy (CINE) in WSNs"],"prefix":"10.3390","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6644-0787","authenticated-orcid":false,"given":"Laura","family":"Galluccio","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering (DIEEI), University of Catania, Viale A. Doria 6, 95125 Catania, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joannes Sam","family":"Mertens","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering (DIEEI), University of Catania, Viale A. Doria 6, 95125 Catania, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giacomo","family":"Morabito","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering (DIEEI), University of Catania, Viale A. Doria 6, 95125 Catania, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1996","DOI":"10.1109\/COMST.2014.2320099","article-title":"Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications","volume":"16","author":"Alsheikh","year":"2014","journal-title":"IEEE Commun. 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