Skip to main content
SpringerLink
Log in

Discovering Non-Cooperating Nodes by Means of Learning Automata in the Internet of Things

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Internet of Things (IoT) refers to a set of things that are wirelessly connected. The lack of cooperation of nodes, which is due to the reduction of energy level, leads to non-cooperating nodes. Discovering non-cooperating nodes is regarded as one of the main challenges of IoT. In this paper, we addressed this issue by using learning automata where misbehavior of non-cooperating nodes is identified and removed from the network. Simulation results of the proposed method were compared with those of previous works and methods; it was found that the proposed method optimized the other methods in terms of power consumption, throughput, the precision of discovering non-cooperating nodes, and false-positive rate.

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

Similar content being viewed by others

References

  1. Asadi, M., Jamali, M. A. J., Parsa, S., & Majidnezhad, V. (2020). Detecting botnet by using particle swarm optimization algorithm based on voting system. Future Generation Computer Systems, 107, 95–111.

  2. Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer networks, 54(15), 2787–2805.

    Article  Google Scholar 

  3. Chang, J.-M., Tsou, P.-C., Woungang, I., Chao, H.-C., & Lai, C.-F. (2014). Defending against collaborative attacks by malicious nodes in MANETs: A cooperative bait detection approach. IEEE systems journal, 9(1), 65–75.

    Article  Google Scholar 

  4. Chong, Z. K., Tan, S. W., Goi, B. M., & Ng, B. C. K. (2013). Outwitting smart selfish nodes in wireless mesh networks. International Journal of Communication Systems, 26(9), 1163–1175.

    Article  Google Scholar 

  5. Dehestani, F., & Jamali, M. A. J (2020). Load balanced clustering based on imperialist competitive algorithm in wireless sensor networks. Wireless Personal Communications, 1–15.

  6. Ding, Q., Li, X., Jiang, M., & Zhou, X. (2013). A novel reputation management framework for vehicular ad hoc networks. International Journal of Multimedia Technology, 3(2), 62–66.

    Google Scholar 

  7. Hayajneh, T., Almashaqbeh, G., & Ullah, S. (2015). A green approach for selfish misbehavior detection in 802.11-based wireless networks. Mobile Networks and Applications, 20(5), 623–635.

    Article  Google Scholar 

  8. Heidari, A., JabraeilJamali, M. A., JafariNavimipour, N., & Akbarpour, S. (2020). Internet of things offloading: Ongoing issues, opportunities, and future challenges. International Journal of Communication Systems, 33(14), e4474.

  9. Heidari, A., & Navimipour, N. J. (2021). A new SLA-aware method for discovering the cloud services using an improved nature-inspired optimization algorithm. PeerJ Computer Science, 7, e539.

  10. Heidari, A., Navimipour, N. J (2021). Service discovery mechanisms in cloud computing: A comprehensive and systematic literature review. Kybernetes.

  11. Huang, D., Williams, S. A., Shere S. (2012). Cheater detection in vehicular networks. 2012 IEEE 11th international conference on trust, security and privacy in computing and communications. IEEE.

  12. Jamali, J., Bahrami, B., Heidari, A., Allahverdizadeh, P., & Norouzi, F. (2020). Towards the internet of things. Springer.

  13. Jesudoss, A., Raja, S. K., & Sulaiman, A. (2015). Stimulating truth-telling and cooperation among nodes in VANETs through payment and punishment scheme. Ad Hoc Networks, 24, 250–263.

    Article  Google Scholar 

  14. Kerrache, C. A., Lakas, A., Lagraa, N., & Barka, E. (2018). UAV-assisted technique for the detection of malicious and selfish nodes in VANETs. Vehicular Communications, 11, 1–11.

    Article  Google Scholar 

  15. Khan, U., Agrawal, S., & Silakari, S. (2015). Detection of malicious nodes (DMN) in vehicular ad-hoc networks. Procedia computer science, 46, 965–972.

    Article  Google Scholar 

  16. Kumar, J. S., & Zaveri, M. A. (2016). Hierarchical clustering for dynamic and heterogeneous internet of things. Procedia Computer Science, 93, 276–282.

    Article  Google Scholar 

  17. Michiardi, P., & Molva, R. (2002). Core: A collaborative reputation mechanism to enforce node cooperation in mobile ad hoc networks (pp. 107–121). Springer.

  18. Patel, N. J., Jhaveri, R. H. (2015). Detecting packet dropping misbehaving nodes using support vector machine (SVM) in MANET. International Journal of Computer Applications. 122(4).

  19. Qadir, Z., Ullah, F., Munawar, H. S., & Al-Turjman, F. (2021). Addressing disasters in smart cities through UAVs path planning and 5G communications: A systematic review. Computer Communications, 168, 114–135.

    Article  Google Scholar 

  20. Sathyamoorthi, T., Vijayachakaravarthy, D., Divya, R., Nandhini, M. (2014). A simple and effective scheme to find malicious node in wireless sensor network. International Journal of Research In Engineering And Technology, 3(02).

  21. Selvan, M. A., & Selvakumar, S. (2019). Malicious node identification using quantitative intrusion detection techniques in MANET. Cluster computing, 22(3), 7069–7077.

    Article  Google Scholar 

  22. Tootaghaj, D. Z., Farhat, F., Pakravan, M., -R., Aref, M., -R. (2011). Game-theoretic approach to mitigate packet dropping in wireless ad-hoc networks. 2011 IEEE consumer communications and networking conference (CCNC). IEEE.

  23. Touati, M., El-Azouzi, R., Coupechoux, M., Altman, E., & Kelif, J.-M. (2017). A controlled matching game for WLANs. IEEE Journal on Selected Areas in Communications, 35(3), 707–720.

    Article  Google Scholar 

  24. Zannou, A., & Boulaalam, A. (2021). Relevant node discovery and selection approach for the internet of things based on neural networks and ant colony optimization. Pervasive and Mobile Computing, 70, 101311.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran

    Saber Niaz & Mohammad Ali Jabraeil Jamali

Authors
  1. Saber Niaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Ali Jabraeil Jamali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Ali Jabraeil Jamali.

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

Niaz, S., Jabraeil Jamali, M.A. Discovering Non-Cooperating Nodes by Means of Learning Automata in the Internet of Things. Wireless Pers Commun 121, 2477–2494 (2021). https://doi.org/10.1007/s11277-021-08832-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08832-2

Keywords

Search

Navigation