T-Safe: Trustworthy Service Provisioning for IoT-Based Intelligent Transport Systems

Abstract

In this paper, we propose a trustworthy service provisioning scheme for Safety-as-a-Service (Safe-aaS) infrastructure in IoT-based intelligent transport systems. Typically, a Safe-aaS infrastructure provides customized safety-related decisions dynamically to multiple end-users using the concept of decision virtualization. We consider road transportation as the application environment of Safe-aaS to generate trustworthy decisions. On the other hand, the efficiency and accuracy of the decisions generated depend on the security, privacy, and trustworthiness of the participating sensor nodes and the route through which data transit. We propose a trust evaluation model to compute the trustworthiness of the data generated from these nodes. Further, we consider direct and indirect trust mechanisms for each of the sensor nodes and update their trust measures at regular intervals of time. Based on these measures, we evaluate the trust of each data item sourced from the network. We formulate an integer linear programming (ILP) model to select the optimal data for decision-making, while alleviating the effects of illegitimate sensor nodes. Further, we show that the formulated ILP problem is NP-hard and use a dynamic programming approach to solve the problem. Experimental results show that our proposed trust evaluation model exhibits more than 8% attack detection rate and 13% reduction in false attack detection rate in a network with 50% malicious nodes, compared to the benchmark schemes. The proposed trustworthy data selection algorithm outperforms different existing greedy approaches.