A Novel Edge-Based Trust Management System for the Smart City Environment Using Eigenvector Analysis.

Autor: Nagarajan G; Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India., Simpson SV; Department of Computer Science and Engineering, SCMS School of Engineering and Technology, Kerala, India., Venkatachalam K; Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore 560074, India., Alrasheedi AF; Department of Statistics and Operations Research, College of Science, King Saud University, Riyadh 11451, Saudi Arabia., Askar SS; Department of Statistics and Operations Research, College of Science, King Saud University, Riyadh 11451, Saudi Arabia., Abouhawwash M; Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt.; Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, MI 48824, USA., P P; Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Erode, India.
Jazyk: angličtina
Zdroj: Journal of healthcare engineering [J Healthc Eng] 2022 May 26; Vol. 2022, pp. 5625897. Date of Electronic Publication: 2022 May 26 (Print Publication: 2022).
DOI: 10.1155/2022/5625897
Abstrakt: The proposed Edge-based Trust Management System (E-TMS) uses an Eigenvector-based approach for eliminating the security threats present in the Internet of Things (IoT) enabled smart city environment. In most existing trust management systems, the trust aggregation process completely depends on the direct trust ratings obtained from both legitimate and malicious neighboring IoT devices. E-TMS possesses an edge-assisted two-level trust computation approach for ensuring the malicious free trust evaluation of IoT devices. The E-TMS aims at removing the false contribution on aggregated trust data. It utilizes the properties of the Eigenvector for identifying compromised IoT devices. The Eigenvector Analysis also helps to avoid false detection. The analysis involves a comparison of all the contributed trust data about every single connected device. A spectral matrix will be generated corresponding to the contributions and the received trust will be scaled based on the obtained spectral values. The absolute sum of obtained values will contain only true contributions. The accurate identification of false data will remove the effect of malicious contributions from the final trust value of a connected IoT device. Since the final trust value calculated by the edge node contains only the trustworthy data, the prediction about the malicious nodes will be accurate. Eventually, the performance of E-TMS has been validated. Throughput and network resilience are higher than the existing system.
Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of the paper.
(Copyright © 2022 G. Nagarajan et al.)
Databáze: MEDLINE
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