RACEME: Embedded Intelligence for Correlation-driven Predictive Energy-harvesting Management in LoRa Networks

The prolonged lifetime of energy-harvesting (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’ harvesting abilities. We present RACEME, a novel predictive EH-management framework by exploiting the embedded intelligence capability of energy-harvesting 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 (ML) for 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 an ML 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.