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

Abstract

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 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 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 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.