Skip to main content
SpringerLink
Log in

Clustering-Driven Intelligent Trust Management Methodology for the Internet of Things (CITM-IoT)

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

The growth and adoption of the Internet of Things (IoT) is increasing day by day. The large number of IoT devices increases the risk of security threats such as (but not limited to) viruses or cyber-attacks. One possible approach to achieve IoT security is to enable a trustworthy IoT environment in IoT wherein the interactions are based on the trust value of the communicating nodes. Trust management and trust assessment has been extensively studied in distributed networks in general and the IoT in particular, but there are still outstanding pressing issues such as bad-mouthing of trust values which prevent them from being used in practical IoT applications. Furthermore, there is no research in ensuring that the developed IoT trust solutions are scalable across billions of IoT nodes. To address the above-mentioned issues, we propose a methodology for scalable trust management solution in the IoT. The methodology addresses practical and pressing issues related to IoT trust management such as trust-based IoT clustering, intelligent methods for countering bad-mouthing attacks on trust systems, issues of memory-efficient trust computation and trust-based migration of IoT nodes from one cluster to another. Experimental results demonstrate the effectiveness of the proposed approaches.

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

Similar content being viewed by others

References

  1. Alshehri MD, Hussain FK (2015) A comparative analysis of scalable and context-aware trust management approaches for internet of things. In: International conference on neural information processing. Springer, Berlin, pp 596–605

  2. Bao F, Chen R, Guo J (2013) Scalable, adaptive and survivable trust management for community of interest based internet of things systems. In: 2013 IEEE 11th international symposium on autonomous decentralized systems (ISADS). IEEE, pp 1–7

  3. Bellavista P, Zanni A (2016) Towards better scalability for IoT-cloud interactions via combined exploitation of MQTT and CoAP. In: 2016 IEEE 2nd international forum on research and technologies for society and industry leveraging a better tomorrow (RTSI). IEEE, pp 1–6

  4. Gharbieh M, ElSawy H, Bader A, Alouini M-S (2017) Spatiotemporal stochastic modeling of IoT enabled cellular networks: Scalability and stability analysis. IEEE Transactions on Communications

  5. Chen R, Guo J, Bao F (2016) Trust management for SOA-based IoT and its application to service composition. IEEE Trans Serv Comput 9(3):482–495

    Article  Google Scholar 

  6. Han S, NDN-Based Woo H (2016) Pub/sub system for scalable IoT cloud. In: Cloud computing technology and science (CloudCom). IEEE

  7. Jabeur N, Yasar AU-H, Shakshuki E, Haddad H (2017) Toward a bio-inspired adaptive spatial clustering approach for IoT applications. Future Generation Computer Systems

  8. Jiang H, Shen F, Chen S, Li K-C, Jeong Y-S (2015) A secure and scalable storage system for aggregate data in IoT. Futur Gener Comput Syst 49:133–141

    Article  Google Scholar 

  9. Khan ZA, Herrmann PA (2017) Trust based distributed intrusion detection mechanism for internet of things. In: 2017 IEEE 31st international conference on advanced information networking and applications (AINA). IEEE, pp 1169–1176

  10. Kokoris-Kogias E, Voutyras O, Varvarigou T (2016) TRM-SIoT: A scalable hybrid trust & reputation model for the social internet of things. In: 2016 IEEE 21st international conference on emerging technologies and factory automation (ETFA). IEEE, pp 1–9

  11. Li F, Vögler M, Claeßens M, Dustdar S (2013) Efficient and scalable IoT service delivery on cloud. In: 2013 IEEE 6th international conference on cloud computing (CLOUD). IEEE, pp 740– 747

  12. Yan Z, Zhang P, Vasilakos AV (2014) A survey on trust management for internet of things. J Netw Comput Appl 42:120–134

    Article  Google Scholar 

  13. Ahmed A, Bakar KA, Channa MI, Khan AW (2016) A secure routing protocol with trust and energy awareness for wireless sensor network. Mobile Netw Appl 21(2):272–285

    Article  Google Scholar 

  14. Ma T, Liu Y, Zhang Z-J (2015) An energy-efficient reliable trust-based data aggregation protocol for wireless sensor networks. Int J Control Autom 8(3):305–318

    Article  Google Scholar 

  15. Sicari S, Rizzardi A, Grieco LA, Coen-Porisini A (2015) Security, privacy and trust in internet of things: the road ahead. Comput Netw 76:146–164

    Article  Google Scholar 

  16. Varghese R, Chithralekha T, Kharkongor C (2016) Self-organized cluster based energy efficient meta trust model for internet of things. In: 2016 IEEE international conference on engineering and technology (ICETECH). IEEE, pp 382–389

  17. Tayeb S, Latifi S, Kim Y (2017) A survey on IoT communication and computation frameworks: An industrial perspective. In: 2017 IEEE 7th annual computing and communication workshop and conference (CCWC). IEEE, pp 1–6

  18. Williams R, McMahon E, Samtani S, Patton M, Chen H (2017) Identifying vulnerabilities of consumer Internet of Things (IoT) devices: A scalable approach. In: 2017 IEEE international conference on intelligence and security informatics (ISI). IEEE, pp 179–181

  19. Vögler M, Schleicher JM, Inzinger C, Dustdar S (2016) A scalable framework for provisioning large-scale IoT deployments. ACM Trans Internet Technol 16(2):11

    Article  Google Scholar 

  20. Wolf T, Nagurney A (2016) A layered protocol architecture for scalable innovation and identification of network economic synergies in the internet of things. In: 2016 IEEE 1st international conference on internet-of-things design and implementation (IoTDI). IEEE, pp 141–151

  21. Yu Y, Jia Z, Tao W, Xue B, Lee C (2017) An efficient trust evaluation scheme for node behavior detection in the internet of things. Wirel Pers Commun 93(2):571–587

    Article  Google Scholar 

  22. Ngu AH, Gutierrez M, Metsis V, Nepal S, Sheng QZ (2017) IoT middleware: A survey on issues and enabling technologies. IEEE Internet Things J 4(1):1–20

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Artificial Intelligence, School of Software, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia

    Mohammad Dahman Alshehri & Farookh Khadeer Hussain

  2. Computer Science Department, Computers and Information Technology College, Taif University, Taif, Saudi Arabia

    Mohammad Dahman Alshehri

  3. School of Business, Australian Defence Force Academy University of New South Wales Canberra, Canberra, ACT, Australia

    Omar Khadeer Hussain

Authors
  1. Mohammad Dahman Alshehri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farookh Khadeer Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Omar Khadeer Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Dahman Alshehri.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alshehri, M.D., Hussain, F.K. & Hussain, O.K. Clustering-Driven Intelligent Trust Management Methodology for the Internet of Things (CITM-IoT). Mobile Netw Appl 23, 419–431 (2018). https://doi.org/10.1007/s11036-018-1017-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-018-1017-z

Keywords

Search

Navigation