ecoBLE: A Low-Computation Energy Consumption Prediction Framework for
  Bluetooth Low Energy
Luisa Schuhmacher Sofie Pollin Hazem Sallouha
Bluetooth Low Energy (BLE) is a de-facto technology for Internet of Things
(IoT) applications, promising very low energy consumption. However, this low
energy consumption accounts only for the radio part, and it overlooks the
energy consumption of other hardware and software components. Monitoring and
predicting the energy consumption of IoT nodes after deployment can
substantially aid in ensuring low energy consumption, calculating the remaining
battery lifetime, predicting needed energy for energy-harvesting nodes, and
detecting anomalies. In this paper, we introduce a Long Short-Term Memory
Projection (LSTMP)-based BLE energy consumption prediction framework together
with a dataset for a healthcare application scenario where BLE is widely
adopted. Unlike radio-focused theoretical energy models, our framework provides
a comprehensive energy consumption prediction, considering all components of
the IoT node, including the radio, sensor as well as microcontroller unit
(MCU). Our measurement-based results show that the proposed framework predicts
the energy consumption of different BLE nodes with a Mean Absolute Percentage
Error (MAPE) of up to 12%, giving comparable accuracy to state-of-the-art
energy consumption prediction with a five times smaller prediction model size.