Trustworthy Data Collection for Cyber Systems: A Taxonomy and Future Directions

  • Hafiz ur Rahman
  • Guojun WangEmail author
  • Md Zakirul Alam Bhuiyan
  • Jianer Chen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1122)


Due to technology limitation and environmental influence (i.e., equipment faults, noises, clutter, interferences, and security attacks), the sensor data collected by Cyber-Physical System (CPS) is inherently noisy and may trigger many false alarms. These false or misleading data can lead to wrong decisions. Therefore, data trustworthiness (i.e., the data is free from error, up to date, and originate from a reputable source) is always preferred. However, it often has high cost and challenges to identify fault, noise, cyber-attack, and real-world facts, especially in heterogeneous and complex IoT environment. In this article, we briefly review the current developments and research trend in this research area. We highlighted all the challenges and potential solutions for the trustworthy data collections in CPS and propose a taxonomy for data trustworthiness in CPS. Taxonomy aims to describe different aspects of research in this field. Furthermore, it will help researchers as a reference point for the design of data reliability and data trustworthiness evaluation methods. Based on the observations, future directions are also suggested.


Trustworthy data Taxonomy Cyber-Physical System Internet of Things 



This work was supported in part by the National Natural Science Foundation of China under Grant 61632009 and 61872097, in part by the Guangdong Provincial Natural Science Foundation under Grant 2017A030308006, and in part by the High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01.


  1. 1.
    Cisco: Internet of Things (IoT) - Cisco IoT Product Portfolio - Cisco. Accessed 23 May 2019
  2. 2.
    Bao, F., Chen, I.-R.: Dynamic trust management for internet of things applications. In: Proceedings of International Workshop on Self-aware Internet of Things, pp. 1–6, September 2012Google Scholar
  3. 3.
    Aggarwal, C.C., Ashish, N., Sheth, A.: The Internet of Things: a survey from the data-centric perspective. In: Aggarwal, C. (ed.) Managing and Mining Sensor Data. Springer, Boston, MA (2013). Scholar
  4. 4.
    Wang, Y., Lu, Y.C., Chen, I.R., Cho, J.H., Swami, A.: LogitTrust: a logit regression-based trust model for mobile ad hoc networks. In: 6th ASE International Conference on Privacy, Security, Risk and Trust, Boston, MA, December 2014Google Scholar
  5. 5.
    Lim, H.-S., Ghinita, G., Bertino, E., Kantarcioglu, M.: A gametheoretic approach for high-assurance of data trustworthiness in sensor networks. In: 2012 IEEE 28th International Conference on Data (2012)Google Scholar
  6. 6.
    Li, M., Jiang, W., Li, K.: Recommendation systems in real applications: algorithm and parallel architecture. In: Wang, G., Ray, I., Alcaraz Calero, J., Thampi, S. (eds.) SpaCCS 2016. LNCS, vol. 10066, pp. 45–58. Springer, Cham (2016). Scholar
  7. 7.
    Karkouch, A., Mousannif, H., Al Moatassime, H., Noel, T.: Data quality in internet of things: a state-of-the-art survey. J. Netw. Comput. Appl. 73, 57–81 (2016)CrossRefGoogle Scholar
  8. 8.
    Bhuiyan, M.Z.A., Wu, J.: Trustworthy and protected data collection for event detection using networked sensing systems. In: 2016 IEEE 37th Sarnoff Symposium. IEEE (2016)Google Scholar
  9. 9.
    Bhuiyan, M.Z.A., Wang, G., Choo, K.-K.R.: Secured data collection for a cloud-enabled structural health monitoring system. In: 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE (2016)Google Scholar
  10. 10.
    Saied, Y.B., Olivereau, A., Zeghlache, D., Laurent, M.: Trust management system design for the Internet of Things: a context-aware and multi-service approach. Comput. Secur. 39, 351–365 (2014)CrossRefGoogle Scholar
  11. 11.
    Hui-hui, D., Ya-jun, G., Zhong-qiang, Y., Hao, C.: A wireless sensor networks based on multi-angle trust of node. In: International Forum on Information Technology and Applications, 2009. IFITA-2009, vol. 1, pp. 28–31 (2009)Google Scholar
  12. 12.
    Sathe, S., Papaioannou, T.G., Jeung, H., Aberer, K.: A survey of model-based sensor data acquisition and management. In: Aggarwal, C. (ed.) Managing and Mining Sensor Data. Springer, Boston (2013). Scholar
  13. 13.
    Javed, N., Wolf, T.: Automated sensor verification using outlier detection in the Internet of Things. In: 2012 32nd International Conference on Distributed Computing Systems Workshops (ICDCSW), pp. 291–296 (2012)Google Scholar
  14. 14.
    Bao, F., Chen, I.R.: Dynamic trust management for the Internet of Things applications. In: International Workshop on Self-Aware Internet of Things, San Jose, USA, September 2012Google Scholar
  15. 15.
    Elahi, H., Wang, G., Li, X.: Smartphone bloatware: an overlooked privacy problem. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, K.-K.R. (eds.) SpaCCS 2017. LNCS, vol. 10656, pp. 169–185. Springer, Cham (2017). Scholar
  16. 16.
    Makhdoom, I., et al.: Anatomy of threats to the Internet of Things. In: IEEE Communications Surveys and Tutorials, vol. 21, no. 2, pp. 1636–1675 (2018)CrossRefGoogle Scholar
  17. 17.
    Arif, M., Wang, G., Wang, T., Peng, T.: SDN-based secure VANETs communication with fog computing. In: Wang, G., Chen, J., Yang, L. (eds.) SpaCCS 2018. LNCS, vol. 11342. Springer, Cham (2018). Scholar
  18. 18.
    Haron, N., Jaafar, J., Aziz, I.A., Hassan, M.H., Shapiai, M. I.: Data trustworthiness in Internet of Things: a taxonomy and future directions. In 2017 IEEE Conference on Big Data and Analytics (ICBDA), Kuching, pp. 25–30 (2017)Google Scholar
  19. 19.
    Tang, L.A., et al.: Trustworthiness analysis of sensor data in cyber-physical systems. J. Comput. Syst. Sci. 79(3), 383–401 (2013)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Guo, J., Chen, R.: A classification of trust computation models for service-oriented internet of things systems. In: IEEE International Conference on Services Computing (SCC), vol. 2015, pp. 324–331 (2015)Google Scholar
  21. 21.
    Pouryazdan, M., Kantarci, B., Soyata, T., Foschini, L., Song, H.: Quantifying user reputation scores, data trustworthiness, and user incentives in mobile crowd-sensing. IEEE Access 5, 1382–1397 (2017)CrossRefGoogle Scholar
  22. 22.
    Chen, I.-R., Bao, F., Guo, J.: Trust-based service management for social Internet of Things systems. IEEE Trans. Dependable Secure Comput. 13(6), 684–696 (2015)CrossRefGoogle Scholar
  23. 23.
    Josang, A.: A logic for uncertain probabilities. Int. J. Uncertainty Fuzziness Knowl.-Based Syst. 9(3), 279–311 (2001)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Anjum, B., Rajangam, M., Perros, H., Fan, W.: Filtering Unfair Users: A Hidden Markov Model Approach. SciTePress, Setúbal (2015)Google Scholar
  25. 25.
    ur Rahman, H., Azzedin, F., Shawahna, A., Sajjad, F., Abdulrahman, A.S.: Performance evaluation of vdi environment. In 2016 Sixth International Conference on Innovative Computing Technology (INTECH), pp. 104–109. IEEE, August 2016Google Scholar
  26. 26.
    ur Rahman, H., Wang, G., Chen, J., Jiang, H.: Performance evaluation of hypervisors and the effect of virtual CPU on Performance. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 772–779. IEEE, October 2018Google Scholar
  27. 27.
    Zong, B., Xu, F., Jiao, J., Lv, J.: A broker-assisting trust and reputation system based on artficial neural network. In: IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 4710–4715, October 2009Google Scholar
  28. 28.
    Liu, X., Datta, A., Lim, E.P.: Computational Trust Models and Machine Learning. CRC Press, Boca Raton (2014)CrossRefGoogle Scholar
  29. 29.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Technical report, Stanford Digital Library Technologies Project (1998) Google Scholar
  30. 30.
    Kamvar, S.D., Schlosser, M.T., Molina, H.G.: The EigenTrust algorithm for reputation management in P2P networks. In: Proceedings of the 12th International World Wide Web Conference, Budapest, May 2003 Google Scholar
  31. 31.
    Carbone, M., Nielsen, M., Sassone, V.: A formal model for trust in dynamic networks. In: Proceedings of International Conference on Software Engineering and Formal Methods (SEFM 2003), Brisbane, September 2003Google Scholar
  32. 32.
    Dhulipala, V.S., Karthik, N., Chandrasekaran, R.M.: A novel heuristic approach based trust worthy architecture for wireless sensor networks. Wireless Personal Commun. 70(1), 189–205 (2013)CrossRefGoogle Scholar
  33. 33.
    Bertino, E.: Data trustworthiness—approaches and research challenges. In: Garcia-Alfaro, J., et al. (eds.) DPM/QASA/SETOP -2014. LNCS, vol. 8872, pp. 17–25. Springer, Cham (2015). Scholar
  34. 34.
    Khan, M.F., Wang, G., Bhuiyan, M.Z.A.: Wi-Fi frequency selection concept for effective coverage in collapsed structures. Fut. Gener. Comput.Syst. 97, 409–424 (2019)CrossRefGoogle Scholar
  35. 35.
    Han, G., Jiang, J., Shu, L., Niu, J., Chao, H.-C.: Management and applications of trust in wireless sensor networks: a survey. J. Comput. Syst. Sci. 80(3), 602–617 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Computer ScienceGuangzhou UniversityGuangzhouChina
  2. 2.Department of Computer and Information SciencesFordham UniversityNew YorkUSA

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