{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:53:17Z","timestamp":1740149597536,"version":"3.37.3"},"reference-count":77,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T00:00:00Z","timestamp":1677024000000},"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":"In applications of the Internet of Things (IoT), where many devices are connected for a specific purpose, data is continuously collected, communicated, processed, and stored between the nodes. However, all connected nodes have strict constraints, such as battery usage, communication throughput, processing power, processing business, and storage limitations. The high number of constraints and nodes makes the standard methods to regulate them useless. Hence, using machine learning approaches to manage them better is attractive. In this study, a new framework for data management of IoT applications is designed and implemented. The framework is called MLADCF (Machine Learning Analytics-based Data Classification Framework). It is a two-stage framework that combines a regression model and a Hybrid Resource Constrained KNN (HRCKNN). It learns from the analytics of real scenarios of the IoT application. The description of the Framework parameters, the training procedure, and the application in real scenarios are detailed. MLADCF has shown proven efficiency by testing on four different datasets compared to existing approaches. Moreover, it reduced the global energy consumption of the network, leading to an extended battery life of the connected nodes.<\/jats:p>","DOI":"10.3390\/s23052427","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T07:01:25Z","timestamp":1677135685000},"page":"2427","source":"Crossref","is-referenced-by-count":7,"title":["Machine Learning Analytic-Based Two-Staged Data Management Framework for Internet of Things"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4364-0548","authenticated-orcid":false,"given":"Omar","family":"Farooq","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, India"}]},{"given":"Parminder","family":"Singh","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, India"},{"name":"School of Computer Science, University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco"}]},{"given":"Mustapha","family":"Hedabou","sequence":"additional","affiliation":[{"name":"School of Computer Science, University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2133-0757","authenticated-orcid":false,"given":"Wadii","family":"Boulila","sequence":"additional","affiliation":[{"name":"Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 12435, Saudi Arabia"},{"name":"RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba 2010, Tunisia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3057-4924","authenticated-orcid":false,"given":"Bilel","family":"Benjdira","sequence":"additional","affiliation":[{"name":"Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 12435, Saudi Arabia"},{"name":"SE & ICT Lab, LR18ES44, ENICarthage, University of Carthage, Tunis 1054, Tunisia"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6722","DOI":"10.1109\/JIOT.2020.3004500","article-title":"Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things","volume":"7","author":"Liu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"150936","DOI":"10.1109\/ACCESS.2019.2947652","article-title":"Foundations and Evolution of Modern Computing Paradigms: Cloud, IoT, Edge, and Fog","volume":"7","author":"Donno","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7768","DOI":"10.1109\/JIOT.2020.3039296","article-title":"Trust Management in Social Internet of Things: Architectures, Recent Advancements, and Future Challenges","volume":"8","author":"Khan","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.future.2021.08.006","article-title":"Recent advancements and challenges of Internet of Things in smart agriculture: A survey","volume":"126","author":"Sinha","year":"2022","journal-title":"Future Gener. 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