Trusting Sensors Measurements in a WSN: An Approach Based on True and Group Deviation Estimation

  • Noureddine Boudriga
  • Paulvanna N. Marimuthu
  • Sami J. Habib
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)


Quality-of-service (QoS) and accuracy are of prime importance in WSN-based monitoring applications, as they may need to report real-time measurements leading to efficient decision making. The tiny sensors are often subject to measurement errors, say noise, and prone to failures and attacks, as their physical characteristics change easily due to environmental abnormality and mechanical shock. Faulty information may induce erroneous decisions, which may significantly impact the performance of the network and its service quality. Thus, the sensors’ need to be calibrated periodically and its data has to be trustworthy in making a good decision. In this paper, we have proposed a trust management framework based on true and group deviation metrics to analyze the accuracy and trustworthiness of the sensors’ data. We have derived an analytical model to calibrate the sensors periodically and to examine the trustworthiness. Our simulation results on testing a real-time fire monitoring system showed that the proposed trust framework is efficient in producing 95% accurate and trusted measurements by limiting the frequency of sensor calibrations to a very low value and by setting a lower boundary of 5% deviation from the true and group value metrics.


Wireless sensor networks Sensor calibration Group deviation True value deviation Trust management 


  1. 1.
    Ishmanov, F., Malik, A.S., Kim, S.W., Begalov, B.: Trust management system in wireless sensor networks: design considerations and research challenges. Trans. Emerg. Telecommun. Technol. 26(2), 107–113 (2015)CrossRefGoogle Scholar
  2. 2.
    Lopez, J., Roman, R., Agudo, I., Fernandez-Gago, C.: Trust management systems for wireless sensor networks: best practices. Comput. Commun. 33(9), 1086–1093 (2010)CrossRefGoogle Scholar
  3. 3.
    Habib, S.J., Marimuthu, P.N.: Development of analytical model for data trustworthiness in sensor networks. In: The Proceedings of IEEE International Conference on Advanced Information Networking and Applications, Crans-Montana, Switzerland, 23–25 March 2016 (2016)Google Scholar
  4. 4.
    Habib, S.J., Marimuthu, P.N.: Reputation analysis of sensors’ trust within tabu search. In: The Proceedings of World Conference on Information Systems and Technologies, Madeira, Portugal, 11–13 April (2017)Google Scholar
  5. 5.
    Chen, Z., Tian, L., Lin, C.: Trust model of wireless sensor networks and its application in data fusion. Sensors 17, 703 (2017)CrossRefGoogle Scholar
  6. 6.
    Fan, C.Q., Wang, S.G., Sun, Q.B., Wang, H.M., Zhang, G.W., Yang, F.C.A.: Trust evaluation method of sensors based on energy monitoring. Acta Electron. Sin. 41, 646–651 (2013)Google Scholar
  7. 7.
    Shao, N., Zhou, Z., Sun, Z.: Lightweight and dependable trust model for clustered wireless sensor networks. In: Huang, Z., Sun, X., Luo, J., Wang, J. (eds.) The Proceedings of Cloud Computing and Security Conference. Lecture Notes in Computer Science, vol. 9483, pp. 157–168. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  8. 8.
    Zhang, B., Huang, Z.H., Xiang, Y.A.: Novel multiple-level trust management framework for wireless sensor networks. Comput. Netw. 72, 45–61 (2014)CrossRefGoogle Scholar
  9. 9.
    Meghanathan, N.: A distributed trust evaluation model for wireless mobile sensor networks. In: The Proceedings of International Conference on Information Technology: New Generations, 7–9 April 2014, pp. 186–191. Las Vegas, USA (2014)Google Scholar
  10. 10.
    Lee, S., Auriol, B.J., Jameel, H., Shaikh, R.A., Lee, H., Song, Y.A.: Group-based trust management scheme for clustered wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 20(11), 1698–1712 (2009)CrossRefGoogle Scholar
  11. 11.
    Salem, M.A.: An efficient distributed trust model for wireless sensor networks. Int. J. Mag. Eng. Technol. Manag. Res. 3(5), 459–464 (2017).Google Scholar
  12. 12.
    Bychkovskiy, V., Megerian, S., Estrin, D., Potkonjak, M.C.: A collaborative approach to in-place sensor calibration. In: The Proceedings of the 2nd International Workshop on Information Processing in Sensor Networks, Palo Alto, CA, USA, 22–23 April (2003)CrossRefGoogle Scholar
  13. 13.
    Whitehouse, K., Culler, D.: Calibration as parameter estimation in sensor networks. In: The Proceedings of ACM International Workshop on Wireless Sensor Networks and Applications, Atlanta, GA, USA (2002)Google Scholar
  14. 14.
    Khalid, O., Khan, S.U., Madani, S.A., Hayat, K., Khan, M.I., Allah, M.N., Kolodziej, J., Wang, L., Zeadally, S., Chen, D.: Comparative study of trust and reputation systems for wireless sensor networks. Secur. Commun. Netw. 6, 669–688 (2013)CrossRefGoogle Scholar
  15. 15.
    Badiru, A.B., Racz, L.: Handbook of Measurements: Benchmarks for Systems Accuracy and Precision. CRC Press, Boca Raton (2016)Google Scholar
  16. 16.
    Dyer, S.A.: Survey of Instrumentation and Measurement. John Wiley and Sons, Hoboken (2004)Google Scholar
  17. 17.
    National Institute of Standards and Measurements. Accessed 06 Aug 2017

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Noureddine Boudriga
    • 1
  • Paulvanna N. Marimuthu
    • 2
  • Sami J. Habib
    • 2
  1. 1.CNAS Research LabUniversity of CarthageTunisTunisia
  2. 2.Computer Engineering DepartmentKuwait UniversityKuwait CityKuwait

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