ecoBLE: A Low-Computation Energy Consumption Prediction Framework for Bluetooth Low Energy
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.
References
[1]
Making the ICT sector energy efficient. [online] accessed on: July 2022.
[2]
A. M. Alonso, F. J. Nogales, and C. Ruiz. A single scalable LSTM model for short-term forecasting of massive electricity time series. Energies, 2020.
[3]
J. Beitner. PyTorch Forecasting, 2020.
[4]
Q. Chen and L. Tang. A wearable blood oxygen saturation monitoring system based on Bluetooth low energy technology. Computer Communications, 2020.
[5]
W. Falcon and The PyTorch Lightning team. PyTorch Lightning, 3 2019.
[6]
U. Farouk, M. Asante, and J. Ben. A survey of machine learning’s electricity consumption models. Communications on Applied Electronics, 2018.
[7]
S. Hochreiter and J. Schmidhuber. Long Short-Term Memory. Neural Computation, 1997.
[8]
C. Hu, W. Bao, D. Wang, and F. Liu. Dynamic adaptive DNN surgery for inference acceleration on the edge. In IEEE Conference on Computer Communications, 2019.
[9]
IRNAS. Power Profiling Kit 2 unofficial Python API. [online] accessed on: October 2022.
[10]
S. Jeddou, A. Baina, N. Abdallah, and H. El Alami. Power consumption prediction of IoT application protocols based on Linear Regression. International Journal of Artificial Intelligence and Machine Learning, 2021.
[11]
P. H. Kindt, D. Yunge, R. Diemer, and S. Chakraborty. Energy modeling for the Bluetooth Low Energy protocol. ACM Transactions on Embedded Computing Systems, 2020.
[12]
W. Kong, Z. Y. Dong, Y. Jia, D. J. Hill, Y. Xu, and Y. Zhang. Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid, 2019.
[13]
Z. Project. Bluetooth samples. readme.html. [online] accessed on: October 2022.
[14]
Z. Project. Zephyr project. [online] accessed on: October 2022.
[15]
S. Raza, P. Misra, Z. He, and T. Voigt. Bluetooth Smart: An enabling technology for the Internet of Things. In IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), 2015.
[16]
H. Sak, A. Senior, and F. Beaufays. Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. arXiv, 2014.
[17]
H. Sallouha, A. Chiumento, and S. Pollin. Aerial vehicles tracking using noncoherent crowdsourced wireless networks. IEEE Transactions on Vehicular Technology, 2021.
[18]
H. Sallouha, B. Van den Bergh, Q. Wang, and S. Pollin. ulora: Ultra low-power, low-cost and open platform for the LoRa networks. In Proceedings of the 4th ACM Workshop on Hot Topics in Wireless, 2017.
[19]
M. Savi and F. Olivadese. Short-term energy consumption forecasting at the edge: A federated learning approach. IEEE Access, 2021.
[20]
R. Schwartz, J. Dodge, N. A. Smith, and O. Etzioni. Green AI. arXiv, 2019.
[21]
N. Semiconductor. nRF Connect SDK. [online] accessed on: October 2022.
[22]
N. Semiconductor. nRF52 DK. [online]
[23]
N. Semiconductor. nRF52832. [online] accessed on: October 2022.
[24]
N. Semiconductor. Power Profiler Kit II. [online] accessed on: October 2022.
[25]
G. Shan, S.-y. Im, and B.-h. Roh. Optimal AdvInterval for BLE scanning in different number of BLE devices environment. In IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2016.
[26]
M. Shao, X. Wang, Z. Bu, X. Chen, and Y. Wang. Prediction of energy consumption in hotel buildings via support vector machines. Sustainable Cities and Society, 2020.
[27]
M. Siekkinen, M. Hiienkari, J. K. Nurminen, and J. Nieminen. How low energy is Bluetooth Low Energy? Comparative measurements with ZigBee/802.15.4. In IEEE Wireless Communications and Networking Conference Workshops (WCNCW), 2012.
[28]
J. Wang, M. K. Lim, C. Wang, and M.-L. Tseng. The evolution of the Internet of Things (IoT) over the past 20 years. Computers & Industrial Engineering, 2021.
[29]
J. Ye. Using digitalization to achieve decarbonization goals. Center for Climate and Energy Solutions, 2021.
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Published In
December 2023
426 pages
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Association for Computing Machinery
New York, NY, United States
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Published: 15 December 2023
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EWSN '23
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EWSN '23 Paper Acceptance Rate 31 of 56 submissions, 55%;
Overall Acceptance Rate 81 of 195 submissions, 42%
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