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Netw."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>The prolonged lifetime of energy-harvesting\u00a0(EH) LoRa networks requires that all EH LoRa sensors utilize available harvested energy in an energy-neutral manner to avoid power failures. This requirement is challenging to fulfill due to the unpredictability of ambient-energy sources and the spatio-temporal heterogeneity of sensors\u2019 harvesting abilities. We present RACEME, a novel predictive EH-management framework by exploiting the embedded intelligence capability of EH LoRa devices. It empowers energy-neutral operation in EH LoRa networks by leveraging the spatio-temporal correlation between EH LoRa sensors to optimize the harvested-energy utilization. RACEME is an integrated system that consists of embedded machine learning\u00a0for predictive EH management on the sensors and online cluster-based data reduction and recovery on the server. To maximize the accuracy of EH predictions, RACEME allows each sensor to implement a machine learning pipeline locally. Coupled with the cluster-based data-reduction feedback from the server, RACEME enables the sensors with higher harvested-energy availability and communication quality to transmit critical data more frequently in a probabilistic manner without sacrificing data quality. Compared with the state of the art, the experimental results reveal that RACEME improves EH-prediction accuracy, network lifetime, and transmission overhead by up to 1.7, 2, and 8 times, respectively.<\/jats:p>","DOI":"10.1145\/3715129","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T11:46:25Z","timestamp":1737719185000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["RACEME: Embedded Intelligence for Correlation-driven Predictive Energy-harvesting Management in LoRa Networks"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8370-9984","authenticated-orcid":false,"given":"Sukanya","family":"Jewsakul","sequence":"first","affiliation":[{"name":"The University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3454-8731","authenticated-orcid":false,"given":"Edith C. 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