skip to main content
10.1145/3460620.3460748acmotherconferencesArticle/Chapter ViewAbstractPublication PagessensysConference Proceedingsconference-collections
research-article

Trust Models in IoT-enabled WSN: A review

Published:04 June 2021Publication History

Editorial Notes

NOTICE OF CONCERN: ACM has received evidence that casts doubt on the integrity of the peer review process for the DATA 2021 Conference. As a result, ACM is issuing a Notice of Concern for all papers published and strongly suggests that the papers from this Conference not be cited in the literature until ACM's investigation has concluded and final decisions have been made regarding the integrity of the peer review process for this Conference.

ABSTRACT

Wireless Sensor Network (WSN) enables the digital world to hear, see, and smell the physical world without the interaction of human beings. It is an essential enabler of the Internet of Things (IoT) in many domains. A WSN is a group of a large number of sensor nodes and a base station. The sensor nodes are characterized by their limited processing, storage, and communication capabilities. In addition, they might get deployed in harsh physical environments where reliability is not guaranteed. Because of that, the IoT-enabled WSNs are challenged by the need to determine the trust of the sensor nodes. Thus, many research studies considered the trust of the sensor nodes in all the IoT layers. This paper overviewed the well-known attacks in the field of IoT-enabled WSN. In addition, it reviewed the trust models in the perception and the network layers of IoT. Also, it discussed the limitations and the challenges of the existing trust models to be considered by the researchers.

References

  1. Shoukat Ali, Muazzam A Khan, Jawad Ahmad, Asad W Malik, and Anis ur Rehman. 2018. Detection and prevention of Black Hole Attacks in IOT & WSN. In 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC). IEEE, Barcelona, Spain, 217–226.Google ScholarGoogle ScholarCross RefCross Ref
  2. X Anita, MA Bhagyaveni, and J Manickam. 2014. Fuzzy-based trust prediction model for routing in WSNs. The Scientific World Journal 2014 (2014), 1–11.Google ScholarGoogle ScholarCross RefCross Ref
  3. Jijeesh Baburajan and Jignesh Prajapati. 2014. A review paper on watchdog mechanism in wireless sensor network to eliminate false malicious node detection. Int. J. Res. Eng. Technol 3, 1 (2014), 381–384.Google ScholarGoogle ScholarCross RefCross Ref
  4. M Bharat, K Santhi Sree, and T Mahesh Kumar. 2014. Authentication solution for security attacks in VANETs. vol 3(2014), 7661–7664.Google ScholarGoogle Scholar
  5. Cinzia Cappiello and Fabio A Schreiber. 2009. Quality-and energy-aware data compression by aggregation in WSN data streams. In 2009 IEEE International Conference on Pervasive Computing and Communications. IEEE, Galveston, TX, USA, 1–6.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Younghun Chae, Lisa Cingiser DiPippo, and Yan Lindsay Sun. 2014. Trust management for defending on-off attacks. IEEE Transactions on Parallel and Distributed Systems 26, 4 (2014), 1178–1191.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Haiguang Chen. 2009. Task-based trust management for wireless sensor networks. International Journal of Security and its applications 3, 2(2009), 21–26.Google ScholarGoogle Scholar
  8. Haiguang Chen, Huafeng Wu, Xi Zhou, and Chuanshan Gao. 2007. Agent-based trust model in wireless sensor networks. In Eighth ACIS international conference on software engineering, artificial intelligence, networking, and parallel/distributed computing (SNPD 2007), Vol. 3. IEEE, Washington, DCUnited States, 119–124.Google ScholarGoogle ScholarCross RefCross Ref
  9. VR Sarma Dhulipala, N Karthik, and RM Chandrasekaran. 2013. A novel heuristic approach based trust worthy architecture for wireless sensor networks. Wireless personal communications 70, 1 (2013), 189–205.Google ScholarGoogle Scholar
  10. Mohammed El-hajj, Ahmad Fadlallah, Maroun Chamoun, and Ahmed Serhrouchni. 2019. A survey of internet of things (IoT) Authentication schemes. Sensors 19, 5 (2019), 1141.Google ScholarGoogle ScholarCross RefCross Ref
  11. Eiman Elnahrawy and Badri Nath. 2004. Context-aware sensors. In European Workshop on Wireless Sensor Networks. Springer, Linz, Austria, 77–93.Google ScholarGoogle ScholarCross RefCross Ref
  12. Saurabh Ganeriwal, Laura K Balzano, and Mani B Srivastava. 2008. Reputation-based framework for high integrity sensor networks. ACM Transactions on Sensor Networks (TOSN) 4, 3 (2008), 1–37.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Andrew Gelman, John B Carlin, Hal S Stern, David B Dunson, Aki Vehtari, and Donald B Rubin. 2013. Bayesian data analysis. CRC press, United States.Google ScholarGoogle Scholar
  14. Sunil Ghildiyal, Amit Kumar Mishra, Ashish Gupta, and Neha Garg. 2014. Analysis of denial of service (dos) attacks in wireless sensor networks. IJRET: International Journal of Research in Engineering and Technology 3 (2014), 2319–1163.Google ScholarGoogle Scholar
  15. Edwin Prem Kumar Gilbert, Baskaran Kaliaperumal, Elijah Blessing Rajsingh, and M Lydia. 2018. Trust based data prediction, aggregation and reconstruction using compressed sensing for clustered wireless sensor networks. Computers & Electrical Engineering 72 (2018), 894–909.Google ScholarGoogle ScholarCross RefCross Ref
  16. Guangjie Han, Jinfang Jiang, Lei Shu, Jianwei Niu, and Han-Chieh Chao. 2014. Management and applications of trust in Wireless Sensor Networks: A survey. J. Comput. System Sci. 80, 3 (2014), 602–617.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Farruh Ishmanov, Aamir Saeed Malik, Sung Won Kim, and Bahodir Begalov. 2015. Trust management system in wireless sensor networks: design considerations and research challenges. Transactions on Emerging Telecommunications Technologies 26, 2(2015), 107–130.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Aimad Karkouch, Hajar Mousannif, Hassan Al Moatassime, and Thomas Noel. 2016. Data quality in internet of things: A state-of-the-art survey. Journal of Network and Computer Applications 73 (2016), 57–81.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Wazir Zada Khan, Xiang Yang, Mohammed Y Aalsalem, and Quratulain Arshad. 2011. Comprehensive study of selective forwarding attack in wireless sensor networks. International Journal of Computer Network and Information Security 3, 1(2011), 1.Google ScholarGoogle ScholarCross RefCross Ref
  20. Mirali Khanderiya and Mital Panchal. 2016. A Novel Approach for Detection of Sybil Attack in Wireless Sensor Networks. IJSRSET 2, 3 (2016), 113–117.Google ScholarGoogle Scholar
  21. Anja Klein and Wolfgang Lehner. 2009. Representing data quality in sensor data streaming environments. Journal of Data and Information Quality (JDIQ) 1, 2 (2009), 1–28.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Levi B Larkey, L Bettencourt, and A Hagberg. 2006. In-situ data quality assurance for environmental applications of wireless sensor networks. Technical Report. Los Alamos Laboratory.Google ScholarGoogle Scholar
  23. Wenjia Li and Houbing Song. 2015. ART: An attack-resistant trust management scheme for securing vehicular ad hoc networks. IEEE Transactions on Intelligent Transportation Systems 17, 4(2015), 960–969.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Atif Manzoor, Hong-Linh Truong, and Schahram Dustdar. 2008. On the evaluation of quality of context. In European Conference on Smart Sensing and Context. Springer, Zurich, Switzerland, 140–153.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. David Martins and Hervé Guyennet. 2010. Wireless sensor network attacks and security mechanisms: A short survey. In 2010 13th International Conference on Network-Based Information Systems. IEEE, Takayama, Gifu Japan, 313–320.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Yongrui Qin, Quan Z Sheng, Nickolas JG Falkner, Schahram Dustdar, Hua Wang, and Athanasios V Vasilakos. 2016. When things matter: A survey on data-centric internet of things. Journal of Network and Computer Applications 64 (2016), 137–153.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Md Ashfaqur Rahman and Hamed Mohsenian-Rad. 2012. False data injection attacks with incomplete information against smart power grids. In 2012 IEEE Global Communications Conference (GLOBECOM). IEEE, Anaheim, California, USA, 3153–3158.Google ScholarGoogle ScholarCross RefCross Ref
  28. Rashmi Ranjan Sahoo, Abdur Rahaman Sardar, Moutushi Singh, Sudhabindu Ray, and Subir Kumar Sarkar. 2016. A bio inspired and trust based approach for clustering in WSN. Natural Computing 15, 3 (2016), 423–434.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. M Selvi, K Thangaramya, Sannasi Ganapathy, Kanagasabai Kulothungan, H Khannah Nehemiah, and Arputharaj Kannan. 2019. An energy aware trust based secure routing algorithm for effective communication in wireless sensor networks. Wireless Personal Communications 105, 4 (2019), 1475–1490.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Kewei Sha and Weisong Shi. 2008. Consistency-driven data quality management of networked sensor systems. J. Parallel and Distrib. Comput. 68, 9 (2008), 1207–1221.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Sabrina Sicari, Cinzia Cappiello, Francesco De Pellegrini, Daniele Miorandi, and Alberto Coen-Porisini. 2016. A security-and quality-aware system architecture for Internet of Things. Information Systems Frontiers 18, 4 (2016), 665–677.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Yan Sun, Zhu Han, and KJ Ray Liu. 2008. Defense of trust management vulnerabilities in distributed networks. IEEE Communications Magazine 46, 2 (2008), 112–119.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Muhamed Turkanović, Boštjan Brumen, and Marko Hölbl. 2014. A novel user authentication and key agreement scheme for heterogeneous ad hoc wireless sensor networks, based on the Internet of Things notion. Ad Hoc Networks 20(2014), 96–112.Google ScholarGoogle ScholarCross RefCross Ref
  34. Po-Wah Yau and Chris J Mitchell. 2003. Security vulnerabilities in ad hoc networks. In Proceedings of the 7th International Symposium on Communication Theory and Applications. ACM, New YorkNYUnited States, 99–104.Google ScholarGoogle Scholar

Index Terms

  1. Trust Models in IoT-enabled WSN: A review
            Index terms have been assigned to the content through auto-classification.

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in
            • Published in

              cover image ACM Other conferences
              DATA'21: International Conference on Data Science, E-learning and Information Systems 2021
              April 2021
              277 pages
              ISBN:9781450388382
              DOI:10.1145/3460620

              Copyright © 2021 ACM

              Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 4 June 2021

              Permissions

              Request permissions about this article.

              Request Permissions

              Check for updates

              Qualifiers

              • research-article
              • Research
              • Refereed limited

              Acceptance Rates

              Overall Acceptance Rate74of167submissions,44%

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader

            HTML Format

            View this article in HTML Format .

            View HTML Format