Skip to main content
SpringerLink
Log in

Link-based penalized trust management scheme for preemptive measures to secure the edge-based internet of things networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

A large number of interconnected smart devices or objects interact with the physical environment known as the Internet of Things (IoT). These networks efficiently perform complex tasks with a high level of intelligence without human intervention and require run-time processing and computation. Edge computing is introduced where devices are placed at the edge between the data source and the cloud. These devices can provide more powerful computational and storage capabilities to IoT users. With various benefits, one of the significant requirements is secure and trustworthy data communication. Various trust management schemes are introduced to guarantee the trustworthiness and isolation of the malicious nodes and give preference to the most trustworthy ones. The existing schemes selected trustworthy nodes again and again from the path without considering the load factor which leads them to be more vulnerable for the Denial of Services (DoS) attacks. In this paper, we propose a Link-based Penalized Trust Management scheme to provide trust management and consider load factors for the selection of most trustworthy nodes and provide a preemptive measure to secure the network from DoS attacks based on its link's association. Simulation results indicate the better performance of the proposed scheme as compared with existing schemes.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Qureshi, K. N., Naveed, A., Kashif, Y., & Jeon, G. (2021). Internet of Things for education: A smart and secure system for schools monitoring and alerting. Computers & Electrical Engineering, 93, 107275.

    Article  Google Scholar 

  2. Sethi, P., & Sarangi, S. R. (2017). Internet of things: architectures, protocols, and applications. Journal of Electrical and Computer Engineering, 2017, 25.

    Article  Google Scholar 

  3. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of things (IoT): A vision, architectural elements, and future directions. Future generation computer systems, 29(7), 1645–1660.

    Article  Google Scholar 

  4. Sun, X., & Ansari, N. (2016). EdgeIoT: Mobile edge computing for the internet of things. IEEE Communications Magazine, 54(12), 22–29.

    Article  Google Scholar 

  5. Sharma, V., You, I., Andersson, K., Palmieri, F., Rehmani, M. H., & Lim, J. (2020). Security, privacy and trust for smart mobile-Internet of things (M-IoT): A survey. IEEE Access, 8, 167123–167163.

    Article  Google Scholar 

  6. S. Singh, R. Sulthana, T. Shewale, V. Chamola, A. Benslimane, and B. Sikdar, Machine learning assisted security and privacy provisioning for edge computing: A survey, IEEE Internet of Things Journal, 2021.

  7. G. S. S. Chalapathi, V. Chamola, A. Vaish, and R. Buyya, Industrial internet of things (iiot) applications of edge and fog computing: A review and future directions, Fog/Edge Computing For Security, Privacy, and Applications, 293–325, 2021.

  8. Xiao, Y., Jia, Y., Liu, C., Cheng, X., Yu, J., & Lv, W. (2019). Edge computing security: State of the art and challenges. Proceedings of the IEEE, 107(8), 1608–1631.

    Article  Google Scholar 

  9. Qureshi, K. N., Rana, S. S., Ahmed, A., & Jeon, G. (2020). A novel and secure attacks detection framework for smart cities industrial internet of things. Sustainable Cities and Society, 61, 102343.

    Article  Google Scholar 

  10. C. Ioannou and V. Vassiliou, Classifying security attacks in IoT networks using supervised learning, In 2019 15th International conference on distributed computing in sensor systems (DCOSS), 2019, pp. 652–658: IEEE.

  11. K. N. Qureshi, M. A. S. Sandila, I. T. Javed, T. Margaria, and L. Aslam, Authentication scheme for unmanned aerial vehicles based internet of vehicles networks, Egyptian Informatics Journal, 2021/07/29/ 2021.

  12. K. N. Qureshi and A. Iftikhar, 6 Contemplating Security, Security and Organization within IoT and Smart Cities, 93, 2020.

  13. Labraoui, N., Gueroui, M., & Sekhri, L. (2016). A risk-aware reputation-based trust management in wireless sensor networks. Wireless Personal Communications, 87(3), 1037–1055.

    Article  Google Scholar 

  14. Li, W., Song, H., & Zeng, F. (2017). Policy-based secure and trustworthy sensing for internet of things in smart cities. IEEE Internet of Things Journal, 5(2), 716–723.

    Article  Google Scholar 

  15. O. AlFarraj, A. AlZubi, and A. Tolba, "Trust-based neighbor selection using activation function for secure routing in wireless sensor networks, Journal of Ambient Intelligence and Humanized Computing, 1–11, 2018.

  16. D. Zhang, C. Gong, K. Jiang, X. Zhang, and T. Zhang, A kind of new method of intelligent trust engineering metrics (ITEM) for application of mobile ad hoc network, Engineering Computations, 2019.

  17. Yuan, J., & Li, X. (2018). A reliable and lightweight trust computing mechanism for IoT edge devices based on multi-source feedback information fusion. Ieee Access, 6, 23626–23638.

    Article  Google Scholar 

  18. Nie, S. (2019). A novel trust model of dynamic optimization based on entropy method in wireless sensor networks. Cluster Computing, 22(5), 11153–11162.

    Article  Google Scholar 

  19. Anwar, R. W., Zainal, A., Outay, F., Yasar, A., & Iqbal, S. (2019). BTEM: Belief based trust evaluation mechanism for wireless sensor networks. Future Generation Computer Systems, 96, 605–616.

    Article  Google Scholar 

  20. Zawaideh, F., & Salamah, M. (2019). An efficient weighted trust-based malicious node detection scheme for wireless sensor networks. International Journal of Communication Systems, 32(3), e3878.

    Article  Google Scholar 

  21. A. Sharma, E. S. Pilli, and A. P. Mazumdar, "RRAR: Robust recommendation aggregation using retraining in Internet of Things, In 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), 2019, pp. 76–80: IEEE.

  22. Li, H.-J., Wang, Q., Liu, S., & Hu, J. (2020). Exploring the trust management mechanism in self-organizing complex network based on game theory. Physica A: Statistical Mechanics and its Applications, 542, 123514.

    Article  MathSciNet  Google Scholar 

  23. Wu, X., Huang, J., Ling, J., & Shu, L. (2019). BLTM: Beta and LQI based trust model for wireless sensor networks. IEEE Access, 7, 43679–43690.

    Article  Google Scholar 

  24. Adewuyi, A. A., Cheng, H., Shi, Q., Cao, J., MacDermott, Á., & Wang, X. (2019). CTRUST: A dynamic trust model for collaborative applications in the internet of Things. IEEE Internet of Things Journal, 6(3), 5432–5445.

    Article  Google Scholar 

  25. Wang, E. K., Chen, C.-M., Zhao, D., Ip, W. H., & Yung, K. L. (2020). A dynamic trust model in internet of things. Soft Computing, 24(8), 5773–5782.

    Article  Google Scholar 

  26. Wang, T., Qiu, L., Sangaiah, A. K., Liu, A., Bhuiyan, M. Z. A., & Ma, Y. (2020). Edge-computing-based trustworthy data collection model in the internet of things. IEEE Internet of Things Journal, 7(5), 4218–4227.

    Article  Google Scholar 

  27. P. Wang, G. Wen, X. Yu, W. Yu, Y. J. I. T. o. S. Wan, Man, and C. Systems, Synchronization of resilient complex networks under attacks, 2019.

  28. Qureshi, K. N., Iftikhar, A., Bhatti, S. N., Piccialli, F., Giampaolo, F., & Jeon, G. (2020). Trust management and evaluation for edge intelligence in the Internet of Things. Engineering Applications of Artificial Intelligence, 94, 103756.

    Article  Google Scholar 

  29. Milani, A. S., & Navimipour, N. J. (2016). Load balancing mechanisms and techniques in the cloud environments: Systematic literature review and future trends. Journal of Network and Computer Applications, 71, 86–98.

    Article  Google Scholar 

  30. Zhong, X., Zhang, L., & Wei, Y. (2019). Dynamic load-balancing vertical control for a large-scale software-defined Internet of Things. IEEE Access, 7, 140769–140780.

    Article  Google Scholar 

  31. C. Tsai and M. Moh, Load balancing in 5G cloud radio access networks supporting IoT communications for smart communities, In 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2017, pp. 259–264: IEEE.

  32. Ganeriwal, S., Balzano, L. K., & Srivastava, M. B. (2008). Reputation-based framework for high integrity sensor networks. ACM Transactions on Sensor Networks (TOSN), 4(3), 1–37.

    Article  Google Scholar 

  33. A. Josang and R. Ismail, The beta reputation system, In Proceedings of the 15th bled electronic commerce conference, 2002, vol. 5, pp. 2502–2511.

  34. Jinhui, X., Yang, T., Feiyue, Y., Leina, P., Juan, X., & Yao, H. (2018). Intrusion detection system for hybrid DoS attacks using energy trust in wireless sensor networks. Procedia computer science, 131, 1188–1195.

    Article  Google Scholar 

  35. Sood, K., Yu, S., Xiang, Y., & Cheng, H. (2016). A general QoS aware flow-balancing and resource management scheme in distributed software-defined networks. IEEE Access, 4, 7176–7185.

    Article  Google Scholar 

  36. J. Liu, J. Li, G. Shou, Y. Hu, Z. Guo, and W. Dai, SDN based load balancing mechanism for elephant flow in data center networks, In: 2014 International Symposium on Wireless Personal Multimedia Communications (WPMC), 2014, pp. 486-490

Download references

Author information

Authors and Affiliations

  1. Centre of Excellence in Artificial Intelligence, Department of Computer Science, Bahria University, Islamabad, Pakistan

    Aneeqa Ahmed & Kashif Naseer Qureshi

  2. Department of Information Science, Division of Science and Technology, University of Education, Lahore, Pakistan

    Muhammad Anwar

  3. Department of Statistics & Computer Science, Faculty of Life Sciences Business Management, University of Veterinary and Animal Sciences, Lahore, 54000, Pakistan

    Farhan Masud

  4. Department of Electrical Engineering, Bahria University, Islamabad, Pakistan

    Junaid Imtiaz

  5. School of Electronic Engineering, Xidian University, Xi’an, 710071, China

    Gwanggil Jeon

  6. Department of Embedded Systems Engineering, Incheon National University, Incheon, 22012, Korea

    Gwanggil Jeon

Authors
  1. Aneeqa Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kashif Naseer Qureshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Anwar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Farhan Masud
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Junaid Imtiaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gwanggil Jeon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gwanggil Jeon.

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

Ahmed, A., Qureshi, K.N., Anwar, M. et al. Link-based penalized trust management scheme for preemptive measures to secure the edge-based internet of things networks. Wireless Netw (2022). https://doi.org/10.1007/s11276-022-02948-4

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11276-022-02948-4

Keywords

Search

Navigation