Trusted Net: A Lightweight Cluster-Based Trust Sensing System for IoT Networks
DOI:
https://doi.org/10.46243/jst.2024.v9.i4.pp74-83Abstract
Abstract— The Internet of Things is founded on the premise that objects in the human living environment can be connected to the Internet. Adoption of the Internet of Things cannot be permitted until security issues are addressed. Security solutions for the Internet of Things can be built on unique designs such as partitioning a mobile network into clusters managed by a cluster head, often known as clustering. However, without any security considerations, the clustering process is vulnerable to a variety of security-related internal attacks. A trust management system, which has proven its security efficiency and friability in mobile networks, might be used to defend the IoT against rogue nodes within it. In this research, we provide a trust-based clustering technique for the Internet of Things. Trust Sensing has played an important part in dealing with security concerns. A novel Light Weight Clustered Trust Sensing (LWCTS) Mechanism has been created with the primary goal of reducing The amount of energy that IoT nodes use. The LWCTS first clusters the network and chooses highly resourced Cluster Heads. Additionally, two components are considered in the proposed trust model: the mobility component for trust sensing and the interactive trust factor. The primary purpose of mobility factor inclusion in the network is to reduce the number of false positives.
References
R. H. Weber, Internet of things new security and privacy challenges, Computer Law & Security Review, vol. 26, pp. 23-30, 2010J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.6873
The EPC global Architecture Framework. EPC global Final Version 1.3, Approved 19 March, 2009. http://www.epcglobalinc.org
Zhuankun Wu. : Initial Study on IOT Security architecture. J. Strategy and decision-making research (2010)
T. Grandison and M. Sloman. Specifying and analysing trust for internet applications. In Proceedings of the Second IFIP Conference on e-Commerce, e-Business and e-Government, 2002
Buchegger S, Boudec J-YL. Performance analysis of the CONFIDANT protocol. In: Proc. 3rd ACM int. symp. mobile ad hoc netw. comput., Lausanne, Switzerland 2002. p. 226e36
Michiardi P, Molva R. CORE: a Collaborative Reputation mechanism to enforce node cooperation in mobile ad hoc networks. In: Proc. 6th int. conf. commun. Multimedia security 2002. p. 107e21
Ganeriwal S, Srivastava MB. Reputation based framework for high integrity sensor networks. In: Proc. ACM security for adhoc and sensor networks 2004. p. 66e7
Chen D, Chang G, Sun D, Li J, Jia J, Wang X. TRM-IoT: a trust management model based on fuzzy reputation for Internet of Things. Comput Sci Inf Syst Oct. 2011;8(4):1207e28
Bao F, Chen IR. Dynamic trust management for Internet of Things Applications. In: 2012 International workshop on self-aware Internet of Things, San Jose, California, USA September 2012
Michele Nitti, Roberto Girau. A Subjective Model for Trustworthiness Evaluation in the Social Internet of Things. 23rd Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications
Chen D, Chang G, Sun D, Li J, Jia J, Wang X. TRM-IoT: a trust management model based on fuzzy reputation for Internet of Things. Comput Sci Inf Syst Oct. 2011;8(4):1207e28
Y.L. Sun, W. Yu, Z. Han, and K.J.R. Liu, Information Theoretic Framework of Trust Modelling and Evaluation for Ad Hoc Networks, IEEE J. Selected Areas in Comm., vol. 24, no. 2, pp. 305-317, Feb
G. Theodorakopoulos and J.S. Baras, On Trust Models and Trust Evaluation Metrics for Ad Hoc Networks, IEEE J. Selected Areas in Comm., vol. 24, no. 2, pp. 318-328, Feb
A.A. Pirzada and C. McDonald, Establishing Trust in Pure Ad-Hoc Networks, Proc. 27th Australasian Computer Science Conf. (ACSC 04), pp. 47-54, Jan. 2004
Yin, X.; Li, S. Trust evaluation model with entropy-based weight assignment for malicious node’s detection in wireless sensor networks. EURASIP Journal on Wireless Communications and Networking 2019, 198
Wu, X.; Huang, J.; Ling, J.; Shu, L. BLTM: beta and LQI based trust model for wireless sensor networks. IEEE Access 2019,7, 4367943690
Mohsenzadeh, A.; Bidgoly, A.J.; Farjami, Y. A novel reward and penalty trust evaluation model based on confidence interval using Petri Net. Journal of Network and Computer Applications 2020, 102533
Malik, NA.; Rai, M. Enhanced Secure and Efficient Key Management Algorithm and Fuzzy With Trust Management for MANETs. Available at SSRN 3565898
