TrustE-VC: Trustworthy Evaluation Framework for Industrial Connected Vehicles in the Cloud

• Published in: IEEE transactions on industrial informatics 2020-09, Vol.16 (9), p.6203-6213
• Main Authors: Aladwan, Mohammad N., Awaysheh, Feras M., Alawadi, Sadi, Alazab, Mamoun, Pena, Tomas F., Cabaleiro, Jose C.
• Format: Article
• Language: English
• Subjects: Algorithms; Cloud computing; Connected vehicles; Data collection; Data privacy; Decision making; Decision-making (DM); industrial connected vehicles (CVs); industrial Internet of Things (IIoT); Informatics; Intelligent transportation systems; Mobile ad hoc networks; Multiple criterion; Privacy; Security; security analysis; security by design; Sensors; Trustworthiness; vehicular clouds (VCs); Weight
• ISSN: 1551-3203; 1941-0050; 1941-0050
• DOI: 10.1109/TII.2020.2966288

The integration between cloud computing and vehicular ad hoc networks, namely, vehicular clouds (VCs), has become a significant research area. This integration was proposed to accelerate the adoption of intelligent transportation systems. The trustworthiness in VCs is expected to carry more computing capabilities that manage large-scale collected data. This trend requires a security evaluation framework that ensures data privacy protection, integrity of information, and availability of resources. To the best of our knowledge, this is the first study that proposes a robust trustworthiness evaluation of vehicular cloud for security criteria evaluation and selection. This article proposes three-level security features in order to develop effectiveness and trustworthiness in VCs. To assess and evaluate these security features, our evaluation framework consists of three main interconnected components: 1) an aggregation of the security evaluation values of the security criteria for each level; 2) a fuzzy multicriteria decision-making algorithm; and 3) a simple additive weight associated with the importance-performance analysis and performance rate to visualize the framework findings. The evaluation results of the security criteria based on the average performance rate and global weight suggest that data residency, data privacy, and data ownership are the most pressing challenges in assessing data protection in a VC environment. Overall, this article paves the way for a secure VC using an evaluation of effective security features and underscores directions and challenges facing the VC community. This article sheds light on the importance of security by design, emphasizing multiple layers of security when implementing industrial VCs.