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

• URL: http://arxiv.org/abs/2309.16686v1
• Date: Wed, 2 Aug 2023 13:04:23 GMT
• Title: ecoBLE: A Low-Computation Energy Consumption Prediction Framework for Bluetooth Low Energy
• Authors: Luisa Schuhmacher, Sofie Pollin, Hazem Sallouha
• Abstract summary: Bluetooth Low Energy (BLE) is a de-facto technology for Internet of Things (IoT) applications, promising very low energy consumption. This paper introduces a Long Short-Term Memory Projection (LSTMP)-based BLE energy consumption prediction framework. Our 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%.
• Score: 9.516475567386768
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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