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Bluetooth Low Energy (BLE) is a de-facto technology for Internet of Things(IoT) applications, promising very low energy consumption. However, this lowenergy consumption accounts only for the radio part, and it overlooks theenergy consumption of other hardware and software components. Monitoring andpredicting the energy consumption of IoT nodes after deployment cansubstantially aid in ensuring low energy consumption, calculating the remainingbattery lifetime, predicting needed energy for energy-harvesting nodes, anddetecting anomalies. In this paper, we introduce a Long Short-Term MemoryProjection (LSTMP)-based BLE energy consumption prediction framework togetherwith a dataset for a healthcare application scenario where BLE is widelyadopted. Unlike radio-focused theoretical energy models, our framework providesa comprehensive energy consumption prediction, considering all components ofthe IoT node, including the radio, sensor as well as microcontroller unit(MCU). Our measurement-based results show that the proposed framework predictsthe energy consumption of different BLE nodes with a Mean Absolute PercentageError (MAPE) of up to 12%, giving comparable accuracy to state-of-the-artenergy consumption prediction with a five times smaller prediction model size.

ecoBLE: A Low-Computation Energy Consumption Prediction Framework for Bluetooth Low Energy