This dataset consists of power consumption measurements of four different Wireless Sensor Network (WSN) protocols. We consider Bluetooth Low Energy (BLE), Thread, the EPhESOS [1] protocol, and UWB in this dataset. We focus on a wireless sensor node use case, i.e. a sensor node gathers every 100 ms 2 bytes of data and transmits it to the network. We compare the energy consumption of the different network protocols and also investigate how much the power consumption decreases if more data is transmitted less frequently. Since the energy consumption is measured in 10 µs resolution, this dataset can also be used to evaluate the influence of the different parts of the communication.
This dataset is a work from Silicon Austria Labs GmbH (SAL), the Linz Center of Mechatronics (LCM), and the Institute for Communications Engineering and RF-Systems (NTHFS) of the Johannes Kepler University (JKU) in Linz for the InSecTT project.
For the measurements the NRF52840 [2] Development kit of Nordic Semiconductor is used. Since the NRF52840 supports multiple wireless protocols, we can cover all proposed network protocols but the UWB scenario. For this, we use the same hardware as described in [3] which combines the NRF52832 with the Qorvo DW1000 UWB transceiver unit. We assume that the power consumption in low-power sleep mode of both boards are comparable and differences in the measurements are due to the additional UWB transceiver unit and different network protocols. To make the individual measurements comparable, no acknowledgments or retransmissions are considered in the evaluations.
For the energy consumption measurements, we use the Power Profiler Kit II developed by Nordic Semiconductor. It is a low-cost solution for power profiling, which achieves a considerable measurement resolution of down to 0.2 µA and a sampling speed of 100 kS/s. The Power Profiler Kit is used in source mode to simultaneously power the device and measure the power consumption.
In our example use case, a sensor node has to transmit 2-byte sensor data periodically to the network. We want to evaluate how the energy consumption depends on the transmission period including the overhead of the network protocol. Additionally, we want to investigate how much the energy consumption decreases if larger packets are transmitted, although less frequently. We define a data acquisition interval of 100 ms, where we acquire 2 bytes of sensor data that needs to be transmitted. This interval is kept constant while increasing the communication period. E.g., for a communication period of 400 ms we will transmit 8 bytes of data. The following table depicts the considered communication periods and the corresponding data size.
| Update Period [ms] | 100 | 200 | 400 | 800 | 1600 |
|---|---|---|---|---|---|
| Data Size [byte] | 2 | 4 | 8 | 16 | 32 |
As a BLE network stack, we use the implementation of the NRF Connect SDK [4] and adapted the provided example programs. We concentrate on the Peripheral device since it allows more energy-saving features compared to the Central. Here we choose a connection interval of 45 ms and allowed the device to skip up to 40 connection events if no data is available. For communucation, the 1M BLE PHY layer is used.
As an OpenThread network stack, we use the implementation of the NRF Connect SDK [4] and adapted the provided example programs. We implemented a Sleepy End Device that can transmit data anytime and periodically polls its parent node with a configured interval of 1 s. For the periodic message transmission to the parent node, we are using User Datagram Protocol (UDP) messages which are directly supported by Thread.
For UWB we simply transmit periodic beacons to the anchor nodes for localisation. Here we performed our measurements with a centre frequency of 4.5 GHz and a bandwidth of 499.2 MHz. We choose a PRF of 64 MHz and 64 preamble symbols.
For the measurement, we use the 1M BLE PHY layer combined with the implementation as described in [1].
The dataset folder contains the measurements for the different communication protocols, e.g. BLE.zip. Since the individual measurements are relatively large, the CSV files are compressed with zip. Each zipped CSV contains one column for the timestamp in 10 µs steps and the measured current in µA for the different transmission periods. For each dataset, the power consumption is measured for 60 seconds. (The different configurations are measured separately and the starting point of the measurement is random.)
The measurements are provided as CSV files which allow opening the data in various programming languages and programs. For Python we shall give a small code example to open the sniffer measurement with Pandas where we can directly handle the zip compression:
#import needed packages
import pandas as pd
protocol = "BLE"
# Load the metadata of the dataset
data = pd.read_csv(f"{protocol}.zip", compression='zip')
data = data.set_index("timestamp")For other programms it is recomended to first unzip the measurements and then import it.
If you have any comments, questions or feedback please contact
- Julian Karoliny: [email protected]
This dataset is distributed under the Creative Commons Attributions License 4.0 (CC-BY 4.0).
This work is funded by the InSecTT project (https://www.insectt.eu/). InSecTT has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 876038. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Sweden, Spain, Italy, France, Portugal, Ireland, Finland, Slovenia, Poland, Netherlands, Turkey. The document reflects only the author’s view and the Commission is not responsible for any use that may be made of the information it contains.
[1] H. P. Bernhard, A. Springer, A. Berger, and P. Priller, “Life cycle of wireless sensor nodes in industrial environments,”IEEE International Workshop on Factory Communication Systems - Proceedings, WFCS, 7 2017
[2] Nordic Semiconductor, NRF52840 Product Specification v1.1, Feb. 2019.
[3] L. B. Hörmann, T. Hölzl, C. Kastl, P. Priller, H.-P. Bernhard, P. Peterseil, and A. Springer, “Evaluation of solar-based energy harvesting for indoor iot applications,” in European Test and Telemetry Conference (ettc2022), 2022, pp. 190–198.
[4] Nordic Semiconductor, nRF Connect SDK v2.0.2,” https://github.com/nrfconnect/sdk-nrf, 2023.