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.

通过测量结果，我们的框架能够对不同的蓝牙低功耗节点的能耗进行预测，其平均绝对百分比误差为 12％，预测模型的尺寸比现有的能耗预测模型小五倍，具有可比较的准确度。

ecoBLE：蓝牙低功耗的低计算能耗预测框架

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

An important task in the Internet of Things (IoT) is field monitoring, where
multiple IoT nodes take measurements and communicate them to the base station
or the cloud for processing, inference, and analysis. This communication
becomes costly when the measurements are high-dimensional (e.g., videos or
time-series data). The IoT networks with limited bandwidth and low power
devices may not be able to support such frequent transmissions with high data
rates. To ensure communication efficiency, this article proposes to model the
measurement compression at IoT nodes and the inference at the base station or
cloud as a deep neural network (DNN). We propose a new framework where the data
to be transmitted from nodes are the intermediate outputs of a layer of the
DNN. We show how to learn the model parameters of the DNN and study the
trade-off between the communication rate and the inference accuracy. The
experimental results show that we can save approximately 96% transmissions with
only a degradation of 2.5% in inference accuracy. Our findings have the
potentiality to enable many new IoT data analysis applications generating large
amount of measurements.

本文研究了如何通过深度神经网络的测量压缩和推理，实现 IoT 数据通信的有效率，并在实验中取得了 96% 的数据传输量节省和 2.5% 推理准确度下降的成果。