Skip to main content
SpringerLink
Log in

TMSRS: trust management-based secure routing scheme in industrial wireless sensor network with fog computing

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Based on fog computer, an industrial wireless sensor network (F-IWSN) is a novel wireless sensor network in the industry. It not only can more efficiently reduce information transmission latency, but also can more beneficially achieve the real-time control and the rapid resource scheduling. However, similar to other distributed networks, it also faces enormous security challenges, especially those internal attacks. The differences from those traditional security schemes are that, one is the trade-off between security, transmission performance and energy consumption to meet the requirements of information convergence and control, the other constructs a multi-dimensional selective forwarding scheme to achieve the real time transmission. In this paper, we propose a Gaussian distribution-based comprehensive trust management system (GDTMS) for F-IWSN. Furthermore, in its trust decision, the grey decision making is introduced to achieve the trade-off between security, transmission performance and energy consumption. The proposed trade-off can effectively select the secure and robust relay node, namely, a trust management-based secure routing scheme. In addition, the proposed schemes are also applicable to defending against bad mouthing attacks. Simulation results show that, the comprehensive performance of GDTMS is better than other similar algorithms. It can effectively prevent the appearance of network holes, and balance the network load, promote the survivability of the network.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Wang, H., Shao, L., Li, M., Wang, B., & Wang, P. (2018). Estimation of clock skew for time synchronization based on two-way message exchange mechanism in industrial wireless sensor networks. IEEE Transactions on Industrial Informatics,14(11), 4755–4765.

    Article  Google Scholar 

  2. Farag, H., Sisinni, E., Gidlund, M., & Österberg, P. (2019). Priority-aware wireless fieldbus protocol for mixed-criticality industrial wireless sensor networks. IEEE Sensors Journal,19(7), 2767–2780.

    Article  Google Scholar 

  3. Qin, B., Cai, J., Luo, Y., Zheng, F., Zhang, J., & Luo, Q. (2019). Research and application of intelligent internet of vehicles model based on fog computing. In IEEE 3rd information technology, networking, electronic and automation control conference (ITNEC) (pp. 1777–1783).

  4. Akram, J., Najam, Z., & Rafi, A. (2018). Efficient resource utilization in cloud-fog environment integrated with smart grids. In International conference on frontiers of information technology (FIT) (pp. 188–193).

  5. Bukhari, J. F., & Yoon, W. (2018). Design of scalable SDN based eMBMS/WLAN network architecture assisted by fog computing. In International conference on information and communication technology convergence (ICTC) (pp. 216–218).

  6. Zhu, J., Zou, Y., & Zheng, B. (2017). Physical-layer security and reliability challenges for industrial wireless sensor networks. IEEE Access,5, 5313–5320.

    Google Scholar 

  7. Qi, Y., Li, W., Luo, X., & Wang, Q. (2014). Security analysis of WIA-PA protocol (Vol. 295, pp. 287–298)., LNEE Berlin: Springer.

    Google Scholar 

  8. Mollah, M. B., Vasilakos, A., & Vasilakos, A. (2017). Security and privacy challenges in mobile cloud computing: Survey and way ahead. Journal of Network and Computer Applications,84(C), 34–54.

    Google Scholar 

  9. Zou, Y., Zhu, J., Wang, X., & Leung, V. (2014). Improving physical-layer security in wireless communications using diversity techniques. IEEE Network,29(1), 42–48.

    Article  Google Scholar 

  10. Sun, L., Ren, P., Du, Q., & Wang, Y. (2016). Fountain-coding aided strategy for secure cooperative transmission in industrial wireless sensor networks. IEEE Transactions on Industrial Informatics,12(1), 291–300.

    Article  Google Scholar 

  11. Pelechrinis, K., Iliofotou, M., & Krishnamurthy, S. V. (2011). Denial of service attacks in wireless networks: The case of jammers. IEEE Communications Surveys and Tutorials,13(2), 245–257.

    Article  Google Scholar 

  12. Chiwewe, T. M., Mbuya, C. F., & Hancke, G. P. (2015). Using cognitive radio for interference-resistant industrial wireless sensor networks: An overview. IEEE Transactions on Industrial Informatics,11(6), 1466–1481.

    Article  Google Scholar 

  13. Zhang, H. J., Xing, H., Nallanathan, Cheng J. A., & Leung, V. C. M. (2016). Secure Resource allocation for OFDMA two-way relay wireless sensor networks without and with cooperative jamming. IEEE Transactions on Industrial Informatics,12(10), 1714–1725.

    Article  Google Scholar 

  14. Wei, M., Kim, K., Wang, P., & Choe, J. (2011). Research and implementation on the security scheme of industrial wireless network. In International conference on information networking (pp. 37–42).

  15. Lee, J., Kim, L., & Kwon, T. (2016). FlexiCast: Energy-efficient software integrity checks to build secure industrial wireless active sensor networks. IEEE Transactions on Industrial Informatics,12(1), 6–14.

    Article  Google Scholar 

  16. Chen, X., Makki, K., Kang, Y., & Pissinou, N. (2009). Sensor network security: A survey. IEEE Communications Surveys and Tutorials,11(2), 52–73.

    Article  Google Scholar 

  17. Ganeriwal, S., & Srivastava, M. B. (2004). Reputation-based framework for high integrity sensor networks. In The 2nd ACM workshop on security of ad hoc and sensor networks (SASN ‘04) (pp. 66–77). Washington, DC, USA: ACM.

  18. Firoozi, F., Zadorozhny, V. I., & Li, F. Y. (2018). Subjective logic-based in-network data processing for trust management in collocated and distributed wireless sensor networks. IEEE Sensors Journal,18(15), 6446–6460.

    Article  Google Scholar 

  19. Sinha, R. K., & Jagannatham, A. K. (2014). Gaussian trust and reputation for fading MIMO wireless sensor networks. In International conference on IEEE electronics, computing and communication technologies (CONECCT) (pp. 1–6). IEEE.

  20. Janani, V. S., & Manikandan, M. S. K. (2018). Efficient trust management with Bayesian-evidence theorem to secure public key infrastructure-based mobile ad hoc networks. Eurasip Journal on Wireless Communications and Networking,1, 25.

    Google Scholar 

  21. Mahmud, M., Kaiser, M. S., Rahman, M. M., Rahman, M. A., Shabut, A., Al-Mamun, S., et al. (2018). A brain-inspired trust management model to assure security in a cloud based IoT framework for neuroscience applications. Cognitive Computation,10(5), 864–873.

    Article  Google Scholar 

  22. Wang, R., Zhang, Z., Zhang, Z., & Jia, Z. (2018). ETMRM: An energy-efficient trust management and routing mechanism for SDWSNs. Computer Networks,139(5), 119–135.

    Article  Google Scholar 

  23. Zhu, M., Chen, H., & Wu, H. (2010). A rank-based application-driven resilient reputation framework model for wireless sensor networks. In International conference on computer application and system modeling (ICCASM) (pp. V9-125–V9-129). IEEE.

  24. Labraoui, N. (2015). A reliable trust management scheme in wireless sensor networks. In 12th international symposium on programming and systems (ISPS) (pp. 1–6).

  25. Duan, J., Gao, D., Yang, D., Foh, C. H., & Chen, H.-H. (2014). An energy-aware trust derivation scheme with game theoretic approach in wireless sensor networks for IoT applications. IEEE Internet of Things Journal,1(1), 58–69.

    Article  Google Scholar 

  26. Fang, W., Zhang, C., Shi, Z., Zhao, Q., & Shan, L. (2016). BTRES: Beta-based trust and reputation evaluation system for wireless sensor networks. Journal of Network and Computer Applications,59(1), 88–94.

    Article  Google Scholar 

  27. Li, M., Hu, J., & Du, J. (2010). A data-centric trust evaluation mechanism in wireless sensor networks. In The 9th international symposium on distributed computing and applications to business engineering and science (DCABES) (pp. 466–470). IEEE.

  28. Zia, T. A., & Islam, M. Z. (2010). Communal reputation and individual trust (CRIT) in wireless sensor networks. In International conference on availability, reliability, and security (ARES’10) (pp. 347–352). IEEE.

  29. Fang, W., Shi, Z., Shan, L., Li, F., & Wang, X. (2015). Trusted scheme for defending on-off attack based on BETA distribution. Journal of System Simulation,27(11), 2722–2728.

    Google Scholar 

  30. Fang, W., Zhang, W., Yang, Y., Liu, Y., & Chen, W. (2017). A resilient trust management scheme for defending against reputation time-varying attacks based on BETA distribution. Science China Information Sciences,60(4), 040305.

    Article  Google Scholar 

  31. Gheorghe, L., Rughinis, R., & Tataroiu, R. (2013). Adaptive trust management protocol based on intrusion detection for wireless sensor networks. In Proceedings of networking in education and research, 2013 RoEduNet international conference 12th Edition (pp. 1–7). IEEE.

  32. Fang, F., Li, J., & Li, J. (2013). A reputation management scheme based on multi-factor in WSNs. In International conference on mechatronic sciences, electric engineering and computer (pp. 3843–3848). IEEE.

  33. Tajeddine, A., Kayssi, A., & Chehab, A. (2011). TRACE: A centralized trust and competence-based energy-efficient routing scheme for wireless sensor networks. In The 7th international wireless communications and mobile computing conference (IWCMC) (pp. 953–958). IEEE.

  34. Tajeddine, A., Kayssi, A., & Chehab, A. (2012). CENTER: A centralized trust-based efficient routing protocol for wireless sensor networks. In The 10th annual international conference on privacy, security and trust (pp. 195–202). IEEE.

  35. Li, X., Niu, J., Kumari, S., Liao, J., Liang, W., & Khan, M. K. (2016). A new authentication protocol for healthcare applications using wireless medical sensor networks with user anonymity. Security and Communication Networks,9(15), 2643–2655.

    Article  Google Scholar 

  36. Gerrigagoitia, K., Uribeetxeberria, R. U., & et al. (2012). Reputation-based intrusion detection system for wireless sensor networks. In Complexity in engineering (pp. 1–5). IEEE.

  37. Arijit, U. (2010). Trust and reputation based collaborating computing in wireless sensor networks. In The 2nd international conference on computational intelligence, modelling and simulation (pp. 464–469). IEEE.

  38. Li, G. D., Yamaguchi, D., & Nagai, M. (2007). A grey-based decision-making approach to the supplier selection problem. Mathematical and Computer Modelling,46(3), 573–581.

    Article  Google Scholar 

  39. Arce, M. E., Saavedra, Á., Míguez, J. L., et al. (2015). The use of grey-based methods in multi-criteria decision analysis for the evaluation of sustainable energy systems: A review. Renewable and Sustainable Energy Reviews,47, 924–932.

    Article  Google Scholar 

  40. Wang, S. J., & Hu, H. A. (2005). Application of rough set on supplier’s determination. In The 3rd annual conference on uncertainty (pp. 256–262).

  41. Perkins, C. E., & Royer, E. M. (1999). Ad hoc on-demand distance vector routing. In The 2nd workshop on mobile computing systems and applications (WMCSA) (pp. 90–100).

  42. Guerrero, M. (2001). Secure ad hoc on-demand distance vector (SAODV) routing. INTERNETDRAFT draft-guerrero-manet-saodv-00.txt.

  43. Fang, W., Zhang, W., Chen, W., Liu, Y., & Tang, C. (2019). TME2R: Trust management-based energy efficient routing scheme in fog-assisted industrial wireless sensor network. In The 2nd EAI international conference on 5G for future wireless networks (5GWN-2019), Feb 23–24, 2018, Changsha, China: EAI (to be published).

Download references

Acknowledgements

Part of this work has been presented at EAI the 2nd EAI International Conference on 5G for Future Wireless Networks (5GWN-2019), Feb 23–24, 2019, Changsha, China [43]. This work is partially supported by the National Natural Science Foundation of China (Nos. 61571004, 51874300), the National Natural Science Foundation of China and Shanxi Provincial People’s Government Jointly Funded Project of China for Coal Base and Low Carbon (No. U1510115), the Qing Lan Project, the China Postdoctoral Science Foundation (No. 2013T60574), the Shanghai Natural Science Foundation (No. 17ZR1429100), the Science and Technology Innovation Program of Shanghai (Nos. 115DZ1100400, 17511105903, 17DZ1200302), and the Scientific Instrument Developing Project of the Chinese Academy of Sciences (No. YJKYYQ20170074).

Author information

Authors and Affiliations

  1. Key Laboratory of Wireless Sensor Network and Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200051, China

    Weidong Fang, Wuxiong Zhang & Yang Liu

  2. Shanghai Research Center for Wireless Communication, Shanghai, 201210, China

    Weidong Fang, Wuxiong Zhang & Yang Liu

  3. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China

    Wei Chen & Chaogang Tang

Authors
  1. Weidong Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wuxiong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chaogang Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Chen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fang, W., Zhang, W., Chen, W. et al. TMSRS: trust management-based secure routing scheme in industrial wireless sensor network with fog computing. Wireless Netw 26, 3169–3182 (2020). https://doi.org/10.1007/s11276-019-02129-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-019-02129-w

Keywords

Search

Navigation