In this work, we introduce an edge intelligence layer into the traditional Safety-as-a-Service (Safe-aaS) platform for Social Internet of Vehicles (SIoV) networks, to minimize the network latency incurred in delivery of decisions. The prior announcement of safety-related information in social IoV environment, minimizes the rate of accidents to a significant extent. Safe-aaS provides customized safety-related decisions dynamically to the end-users. On the other hand, the timely delivery of accurate decisions to the end-users in a social IoV network is a challenging task. We introduce the concept of edge servers in the edge layer of Safe-aaS, such that the bandwidth required for uploading data is minimized, and the problems associated with processing, storage, and complex analysis of data are eliminated. We apply Artificial Neural Network (ANN) at the edge nodes to select the appropriate edge server and fuzzy logic at the edge server side for the generation of a decision. Here social entities are not humans rather vehicles, distributed edge servers and cloud servers all are acting as intelligent objects. Extensive simulation of our proposed architecture demonstrates that the computing density of edge servers is normally distributed. Additionally, we analyze the classification of the edge servers using training data obtained from the edge nodes and network is tested with the test dataset. We apply fuzzified decision is generated method at the edge sever. Extensive simulation results demonstrate that the delay incurred in delivery of decision is reduced by 90:58%, after introduction of edge intelligence layer.

In this paper, we propose a Quality of Service (QoS)-aware pricing scheme, termed as Q-Safe, for provisioning safety-related decisions to the end-users. A Safe-aaS platform provides customized decisions to the end-users, as per their requirement. In this proposed pricing scheme, we consider the presence of multiple Safety Service Providers (SSPs) in the Safe-aaS platform. Therefore, the end-users possess the opportunity to select a SSP, depending on the price charged by them. The end-users may compromise with the quality of the decision provided through the selection of the available safety services at a low cost. Considering road transportation as the application scenario of Safe-aaS and to address these above-mentioned issues, we propose a dynamic pricing scheme, Q-Safe. We introduce the concept of varying price to be charged by the SSPs for each of the decision parameters, based on the fluctuation in the value of these parameters with time. Each of the end-users selects certain decision parameters, among the ones displayed in the Web portal. Thereafter, the SSPs suggest decision parameters to the end-users depending upon their present geographical location. To model these interactions between the SSPs and the end-users, we map the scenario with Non-Cooperative Multiple Leader Multiple Follower Stackelberg game.