Advertisement

Smart application-aware IoT data collection

  • Vasilios A. SirisEmail author
  • Nikos Fotiou
  • Alexandros Mertzianis
  • George C. Polyzos
Original Article
  • 13 Downloads

Abstract

We present and experimentally evaluate procedures for efficient IoT data collection while achieving target requirements in terms of data accuracy, response time, energy, and privacy protection. Different strategies are considered because different IoT applications can have different requirements. Specifically, the accuracy-driven strategy adjusts the time period between consecutive measurements following an additive increase and multiplicative decrease (AIMD) scheme based on a target data accuracy, while the time-driven strategy adjusts the time period between measurement requests to achieve delay less than a given maximum delay between consecutive measurements. The energy-driven strategy considers both the data accuracy and the energy costs for the corresponding measurements. Finally, the privacy-driven strategy adds noise to measurements using differential privacy techniques. The experimental evaluation involves real temperature, humidity, and ozone (O3) measurements obtained from three testbeds through the FIESTA-IoT platform. Our results show that the AIMD adaptation of the measurement period is robust to different types of measurements from different testbeds, without having any tuning parameters. Also, the experimental results show the trade-offs between the target data accuracy and the number of measurements and between the target data accuracy and the corresponding energy costs. For the privacy-driven strategy, the results show that the addition of noise to the sensor measurements using differential privacy has a negligible effect on the aggregate statistics.

Keywords

Data accuracy Adaptive data collection Differential privacy Testbed experiments 

Notes

Acknowledgements

This work was supported in part by the EU FIESTA-IoT project (Grant Agreement number: 643943 / H2020-ICT-2014-1) through the Open Call 3 Experiment “BeSmart: Smart IoT Data Collection” and by the Athens University of Economics and Business Research Center.

References

  1. 1.
    Dwork C, McSherry F, Nissim K, Smith A (2006) Calibrating noise to sensitivity in private data analysis. In: Halevi S, Rabin T (eds) Theory of cryptography. Springer, Berlin, pp 265–284CrossRefGoogle Scholar
  2. 2.
    Warner SL (1965) Randomized response: a survey technique for eliminating evasive answer bias. J Am Stat Assoc 60(309):63–69CrossRefzbMATHGoogle Scholar
  3. 3.
    Erlingsson U, Pihur V, Korolova A (2014) RAPPOR: randomized aggregatable privacy-preserving ordinal response. In: Proceedings of ACM SIGSAC conference on computer and communications securityGoogle Scholar
  4. 4.
    Chu D, Deshpande A, Hellerstein, J, Hong W (2006) Approximate data collection in sensor networks using probabilistic models. In: Proceedings of IEEE ICDEGoogle Scholar
  5. 5.
    Deshpande A, Guestrin C, Madden S, Hellerstein J, Hong W (2004) Model-driven data acquisition in sensor networks. In: Proceedings of VLDBGoogle Scholar
  6. 6.
    Jain A, Chang E, Wang Y.-F (2004) Adaptive stream resource management using Kalman filters. In: Proceedings of ACM SIGMODGoogle Scholar
  7. 7.
    Han Q, Mehrotra S, Venkatasubramanian N (2004) Energy efficient data collection in distributed sensor environments. In: Proceedings of IEEE ICDCSGoogle Scholar
  8. 8.
    Han Q, Hakkarinen D, Boonma P, Suzuki J (2010) Quality-aware sensor data collection. Int J Sens Netw 7(3):127–140CrossRefGoogle Scholar
  9. 9.
    Tang X, Xu J (2008) Adaptive data collection strategies for lifetime-constrained wireless sensor networks. IEEE Trans Parallel Distrib Syst 19(6):721–734Google Scholar
  10. 10.
    Gedik B, Liu L, Yu PS (2007) ASAP: an adaptive sampling approach to data collection in sensor networks. IEEE Trans Parallel Distrib Syst 18(12):1766–1783Google Scholar
  11. 11.
    Liu C, Wu K, Pei J (2007) an energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation. IEEE Trans Parallel Distrib Syst 18(7):1010–1023 (2007)Google Scholar
  12. 12.
    Wang C, Ma H, He Y, Xiong S (2012) Adaptive approximate data collection for wireless sensor networks. IEEE Trans Parallel Distrib Syst 23(6):1004–1016CrossRefGoogle Scholar
  13. 13.
    Riahi M, Papaioannou T, Trummer I, Aberer (2013) Utility-driven data acquisition in participatory sensing. In: International conference on extending database technology: advances in database technology (EDBT)Google Scholar
  14. 14.
    Marjanovic M, Skorin-Kapov L, Pripuzic K, Antonic A, Zarko I (2016) Energy-aware and quality-driven sensor management for green mobile crowd sensing. J Netw Comput Appl 59:95–108CrossRefGoogle Scholar
  15. 15.
    Andres M, Bordenabe N, Chatzikokolakis K, Palamidessi C (2013) Geo-indistinguishability: differential privacy for location-based systems. In: Proceedings of ACM SIGSAC conference on computer and communications securityGoogle Scholar
  16. 16.
    Fink GA (2016) Differentially private distributed sensing. In: Proceeding of 3rd IEEE world forum on internet of things (WF-IoT) (2016)Google Scholar
  17. 17.
    Chen J, Huadong M, Dong Z (2017) Private data aggregation with integrity assurance and fault tolerance for mobile crowd-sensing. Wirel Netw 23(1):131–144CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Mobile Multimedia Laboratory, Department of Informatics, School of Information Sciences and TechnologyAthens University of Economics and BusinessAthensGreece

Personalised recommendations