Abstract
When the software in embedded system runs, the current may change dynamically in wide ranges. It is difficult to accurately measure the energy consumption of software in embedded system. Some existing methods can provide the functions that measuring the currents of different regions by using different ranges through adjusting shunt resistor or amplification. However, these methods are hard to give the value of current accurately during range switching, and bring some measuring errors. To address this problem, this paper designs hardware and software schemes to measure the current of software in embedded system by using small, medium and large ranges simultaneously. Further, a two-stage calibration method based on machine learning is proposed. And a Measurement System for Energy consumption of software in Embedded system (MSee) is presented. The experimental results show that both the average relative errors of MSee in small, medium and large ranges and the average relative errors of MSee in transitional neighborhood of ranges are better than those existing methods of range switching.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Georgiou, K., et al.: The IoT energy challenge: a software perspective. IEEE Embed. Syst. Lett. 10(3), 53–56 (2017)
Ni, Y., Du, X., Ye, P., Xiao, R., Yuan, Y., Li, W.: Frequent pattern mining assisted energy consumption evolutionary optimization approach based on surrogate model at GCC compile time. Swarm Evol. Comput. 50, 1–23 (2019)
Hergenröder, A., Furthmüller, J.: On energy measurement methods in wireless networks, pp. 6268–6272 (2012)
Pötsch, A., Berger, A., Springer, A.: Efficient analysis of power consumption behaviour of embedded wireless IoT systems, pp. 1–6 (2017)
Guo, C., Ci, S., Zhou, Y., Yang, Y.: A survey of energy consumption measurement in embedded systems. IEEE Access 9, 60516–60530 (2021)
Hartung, R., Kulau, U., Wolf, L.: Distributed energy measurement in WSNs for outdoor applications, pp. 1–9 (2016)
Milenkovic, A., Milenkovic, M., Jovanov, E., Hite, D., Raskovic, D.: An environment for runtime power monitoring of wireless sensor network platforms, pp. 406–410 (2005)
Andersen, J., Hansen, M.T.: Energy bucket: a tool for power profiling and debugging of sensor nodes, pp. 132–138 (2009)
Naderiparizi, S., Parks, A.N., Parizi, F.S., Smith, J.R.: \(\mu \)Monitor: in-situ energy monitoring with microwatt power consumption, pp. 1–8 (2016)
Zhou, R., Xing, G.: Nemo: a high-fidelity noninvasive power meter system for wireless sensor networks, pp. 141–152 (2013)
Jiang, X., Dutta, P., Culler, D., Stoica, I.: Micro power meter for energy monitoring of wireless sensor networks at scale, pp. 186–195 (2007)
Haratcherev, I., Halkes, G., Parker, T., Visser, O., Langendoen, K.: PowerBench: a scalable testbed infrastructure for benchmarking power consumption, pp. 37–44 (2008)
Dezfouli, B., Amirtharaj, I., Li, C.-C.C.: EMPIOT: an energy measurement platform for wireless IoT devices. J. Netw. Comput. Appl. 121, 135–148 (2018)
Gomez, K., Riggio, R., Rasheed, T., Miorandi, D., Granelli, F.: Energino: a hardware and software solution for energy consumption monitoring, pp. 311–317 (2012)
Keranidis, S., Kazdaridis, G., Passas, V., Korakis, T., Koutsopoulos, I., Tassiulas, L.: NITOS energy monitoring framework: real time power monitoring in experimental wireless network deployments. ACM SIGMOBILE Mob. Comput. Commun. Rev. 18(1), 64–74 (2014)
Dutta, P., Feldmeier, M., Paradiso, J., Culler, D.: Energy metering for free: augmenting switching regulators for real-time monitoring, pp. 283–294 (2008)
Hussain, S.M.A., Satya Narayana, T., Subramanyachari: Intend and accomplishment of power utilization monitoring and controlling system by using IoT. In: Balas, V., Kumar, R., Srivastava, R. (eds.) Recent Trends and Advances in Artificial Intelligence and Internet of Things. Intelligent Systems Reference Library, vol. 172, pp. 155–163. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-32644-9_17
Li, D., Zhang, Z., Zong, Z.: Simulation with open source date mining tool of WEKA. Coal Technol. 5, 092 (2011)
Carstens, H., Xia, X., Yadavalli, S.: Low-cost energy meter calibration method for measurement and verification. Appl. Energy 188, 563–575 (2017)
Li, C.-C., Dezfouli, B.: ProCal: a low-cost and programmable calibration tool for IoT devices. In: Georgakopoulos, D., Zhang, L.-J. (eds.) ICIOT 2018. LNCS, vol. 10972, pp. 88–105. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94370-1_7
Acknowledgements
This work is supported by the National Natural Science Foundation of China (No. 62172097), the Natural Science Foundation of Fujian Province (Nos. 2020J01165, 2021J01166), Talent support program of High school in the new century of Fujian Province, the National Key Research and Development Program of China (2018AAA0100400).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, W. et al. (2022). Measurement System for Energy Consumption of Runtime Software in Embedded System. In: Li, K., Liu, Y., Wang, W. (eds) Exploration of Novel Intelligent Optimization Algorithms. ISICA 2021. Communications in Computer and Information Science, vol 1590. Springer, Singapore. https://doi.org/10.1007/978-981-19-4109-2_13
Download citation
DOI: https://doi.org/10.1007/978-981-19-4109-2_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-4108-5
Online ISBN: 978-981-19-4109-2
eBook Packages: Computer ScienceComputer Science (R0)