Research Article A Novel Edge-Based Trust Management System for the Smart City Environment Using Eigenvector Analysis G. Nagarajan, 1 Serin V. Simpson, 2 K. Venkatachalam , 3 Adel Fahad Alrasheedi, 4 S.S. Askar , 4 Mohamed Abouhawwash , 5,6 and Parthasarathi P 7 1 Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India 2 Department of Computer Science and Engineering, SCMS School of Engineering and Technology, Kerala, India 3 Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore 560074, India 4 Department of Statistics and Operations Research, College of Science, King Saud University, Riyadh 11451, Saudi Arabia 5 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt 6 Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, MI 48824, USA 7 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Erode, India Correspondence should be addressed to K. Venkatachalam; venkatachalam.k@ieee.org Received 16 January 2022; Revised 6 April 2022; Accepted 5 May 2022; Published 26 May 2022 Academic Editor: Senthil kumar Copyright © 2022 G. Nagarajan et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. e proposed Edge-based Trust Management System (E-TMS) uses an Eigenvector-based approach for eliminating the security threats present in the Internet of ings (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. e E-TMS aims at removing the false contribution on aggregated trust data. It utilizes the properties of the Eigenvector for identifying compromised IoT devices. e Eigenvector Analysis also helps to avoid false detection. e 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. e absolute sum of obtained values will contain only true contributions. e 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. roughput and network resilience are higher than the existing system. 1. Introduction A smart city environment has been established by utilizing the capabilities of edge computing-assisted IoT networks [1, 2]. e edge computing-assisted IoT network provides a collaborative computing facility with the help of a wide range of heterogeneous smart devices. Such a heteroge- neous environment has the highest risk of being vulnerable to security attacks. Such networks require a robust trust management mechanism for maintaining a good device trust level. Trust management helps to keep users with increasing numbers. e traditional cloud-based trust evaluation approaches are incapable to analyze the context- aware trust relationships among connected IoT devices [3, 4]. e heterogeneity as well as the large size of the network became the prime reasons for the performance degradation of the centralized cloud servers. e central- ized cloud server can work efficiently with smaller net- works. But, it is hard to serve large-scale networks with centralized architecture. In such cases, the centralized server cannot offer real-time support to time-dependent applications. Also, it is not possible to make context-aware decisions for all the connected devices by a single cloud server. Hindawi Journal of Healthcare Engineering Volume 2022, Article ID 5625897, 10 pages https://doi.org/10.1155/2022/5625897