{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T16:21:14Z","timestamp":1776788474479,"version":"3.51.2"},"reference-count":36,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T00:00:00Z","timestamp":1711411200000},"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>Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems.<\/jats:p>","DOI":"10.3390\/s24072125","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T13:16:41Z","timestamp":1711459001000},"page":"2125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["LP-OPTIMA: A Framework for Prescriptive Maintenance and Optimization of IoT Resources for Low-Power Embedded Systems"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9849-7187","authenticated-orcid":false,"given":"Alexios","family":"Papaioannou","sequence":"first","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"},{"name":"Management Science and Technology Department, Democritus University of Thrace (DUTH), 65404 Kavala, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9372-7070","authenticated-orcid":false,"given":"Asimina","family":"Dimara","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"},{"name":"Management Science and Technology Department, Democritus University of Thrace (DUTH), 65404 Kavala, Greece"},{"name":"Intelligent Systems Laboratory, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8829-504X","authenticated-orcid":false,"given":"Charalampos S.","family":"Kouzinopoulos","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"}]},{"given":"Stelios","family":"Krinidis","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"},{"name":"Management Science and Technology Department, Democritus University of Thrace (DUTH), 65404 Kavala, Greece"}]},{"given":"Christos-Nikolaos","family":"Anagnostopoulos","sequence":"additional","affiliation":[{"name":"Intelligent Systems Laboratory, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5747-2186","authenticated-orcid":false,"given":"Dimosthenis","family":"Ioannidis","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"}]},{"given":"Dimitrios","family":"Tzovaras","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4821","DOI":"10.1109\/TCSI.2021.3095622","article-title":"The challenges and emerging technologies for low-power artificial intelligence IoT systems","volume":"68","author":"Ye","year":"2021","journal-title":"IEEE Trans. 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