ProCal: A Low-Cost and Programmable Calibration Tool for IoT Devices

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10972)


Calibration is an important step towards building reliable IoT systems. For example, accurate sensor reading requires ADC calibration, and power monitoring chips must be calibrated before being used for measuring the energy consumption of IoT devices. In this paper, we present ProCal, a low-cost, accurate, and scalable power calibration tool. ProCal is a programmable platform which provides dynamic voltage and current output for calibration. The basic idea is to use a digital potentiometer connected to a parallel resistor network controlled through digital switches. The resistance and output frequency of ProCal is controlled by a software communicating with the board through the SPI interface. Our design provides a simple synchronization mechanism which prevents the need for accurate time synchronization. We present mathematical modeling and validation of the tool by incorporating the concept of Fibonacci sequence. Our extensive experimental studies show that this tool can significantly improve measurement accuracy. For example, for ATMega2560, the ADC error reduces from 0.2% to 0.01%. ProCal not only costs less than 2% of the current commercial solutions, it is also highly accurate by being able to provide extensive range of current and voltage values.


Sensors Accuracy Measurement ADC Power emulation Interfacing 


  1. 1.
    Bennett, W.R.: Spectra of quantized signals. Bell Labs Tech. J. 27(3), 446–472 (1948)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bychkovskiy, V., Megerian, S., Estrin, D., Potkonjak, M.: A collaborative approach to in-place sensor calibration. In: Zhao, F., Guibas, L. (eds.) IPSN 2003. LNCS, vol. 2634, pp. 301–316. Springer, Heidelberg (2003). Scholar
  3. 3.
    Cao, J., Meng, X., Temes, G.C., Yu, W.: Power-on digital calibration method for delta-sigma ADCs. In: IEEE International Symposium on Circuits and Systems (ISCAS), pp. 2002–2005. IEEE (2016)Google Scholar
  4. 4.
    Creech, J., Rice, D.: Digital potentiometers vs. mechanical potentiometers: Important design considerations to maximize system performance. Analog Devices, MA, USA, Technical article (2015)Google Scholar
  5. 5.
    Dezfouli, B., Amirtharaj, I., Li, C.C.: EMPIOT: An energy measurement platform for wireless IoT devices. arXiv preprint arXiv:1804.04794 (2018)
  6. 6.
    Dezfouli, B., Radi, M., Chipara, O.: REWIMO: a real-time and reliable low-power wireless mobile network. ACM Trans. Sensor Netw. (TOSN) 13(3), 17 (2017)Google Scholar
  7. 7.
    Haratcherev, I., Halkes, G., Parker, T., Visser, O., Langendoen, K.: Powerbench: A scalable testbed infrastructure for benchmarking power consumption. In: International Workshop on Sensor Network Engineering (IWSNE), pp. 37–44 (2008)Google Scholar
  8. 8.
    Hartung, R., Kulau, U., Wolf, L.: Distributed energy measurement in WSNs for outdoor applications. In: 13th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp. 1–9. IEEE (2016)Google Scholar
  9. 9.
    Jiang, X., Dutta, P., Culler, D., Stoica, I.: Micro power meter for energy monitoring of wireless sensor networks at scale. In: Proceedings of the 6th International Conference on Information Processing in Sensor Networks, pp. 186–195. ACM (2007)Google Scholar
  10. 10.
    Karanicolas, A.N., Lee, H.S., Barcrania, K.L.: A 15-b 1-Msample/s digitally self-calibrated pipeline ADC. IEEE J. Solid-State Circuits 28(12), 1207–1215 (12 1993)CrossRefGoogle Scholar
  11. 11.
    Lee, H.S., Hodges, D.A., Gray, P.R.: A self-calibrating 15 bit CMOS A/D converter. IEEE J. Solid-State Circuits 19(6), 813–819 (1984)CrossRefGoogle Scholar
  12. 12.
    Lim, R., Ferrari, F., Zimmerling, M., Walser, C., Sommer, P., Beutel, J.: Flocklab: a testbed for distributed, synchronized tracing and profiling of wireless embedded systems. In: Proceedings of the 12th International Conference on Information Processing in Sensor Networks, pp. 153–166. ACM (2013)Google Scholar
  13. 13.
    Milenkovic, A., Milenkovic, M., Jovanov, E., Hite, D., Raskovic, D.: An environment for runtime power monitoring of wireless sensor network platforms. In: Proceedings of the Thirty-Seventh Southeastern Symposium on System Theory (SSST), pp. 406–410. IEEE (2005)Google Scholar
  14. 14.
    Moon, U.K., Song, B.S.: Background digital calibration techniques for pipelined ADCs. IEEE Trans. Circuits Syst. II: Analog Digit. Signal Process. 44(2), 102–109 (1997)CrossRefGoogle Scholar
  15. 15.
    Pötsch, A., Berger, A., Springer, A.: Efficient analysis of power consumption behaviour of embedded wireless IoT systems. In: IEEE International on Instrumentation and Measurement Technology Conference (I2MTC), pp. 1–6. IEEE (2017)Google Scholar
  16. 16.
    Analog Devices Inc.: 256-Position Digital Potentiometers (2012).
  17. 17.
  18. 18.
    Arduino Inc.: ARDUINO MEGA 2560 REV3 (2017).
  19. 19.
    Freescale Semiconductor: Official SPI block guide v03. 06 (2014)Google Scholar
  20. 20.
    IEEE collaboration and others: IEEE Standard for Terminology and Test Methods for Analog-To-Digital Converters. IEEE Std., pp. 1241–2000 (2011)Google Scholar
  21. 21.
    Keysight Technologies: Keysight N6700 Modular Power System (2017).
  22. 22.
    Microchip Inc.: MCP3204/3208 2.7V 4-Channel/8-Channel 12-Bit A/D Converters (2017).
  23. 23.
    Tektronix, INC.: DMM7510 7\(\frac{1}{2}\)-Digit Graphical Sampling Multimeter (2017).
  24. 24.
    Texas Instruments: INA219 Zerø -Drift, Bidirectional Current/Power Monitor With I2C Interface (2015).
  25. 25.
    Texas Instruments: Selecting an A/D Converter (2015).
  26. 26.
    Vishay Spectrol: Pot 10K \(\varOmega \) (2014).
  27. 27.
    Suchanek, P., Haasz, V., Slepicka, D.: ADC nonlinearity correction based on INL (n) approximations. In: IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), pp. 137–140. IEEE (2009)Google Scholar
  28. 28.
    Szewczyk, R., Mainwaring, A., Polastre, J., Anderson, J., Culler, D.: An analysis of a large scale habitat monitoring application. In: Proceedings of the 2nd international conference on Embedded networked sensor systems, pp. 214–226. ACM (2004)Google Scholar
  29. 29.
    Taft, R.C., Menkus, C.A., Tursi, M.R., Hidri, O., Pons, V.: A 1.8-V 1.6-Gsample/s 8-b self-calibrating folding ADC with 7.26 ENOB at nyquist frequency. IEEE J. Solid-State Circuits 39(12), 2107–2115 (2004)CrossRefGoogle Scholar
  30. 30.
    Tsang, C., Chiu, Y., Vanderhaegen, J., Hoyos, S., Chen, C., Brodersen, R., Nikolic, B.: Background ADC calibration in digital domain. In: IEEE Custom Integrated Circuits Conference (CICC), pp. 301–304. IEEE (2008)Google Scholar
  31. 31.
    Zhou, R., Xing, G.: Nemo: A high-fidelity noninvasive power meter system for wireless sensor networks. In: ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), pp. 141–152. IEEE (2013)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Internet of Things Research Lab, Department of Computer EngineeringSanta Clara UniversitySanta ClaraUSA
  2. 2.Intel CorporationSanta ClaraUSA

Personalised recommendations