In this paper, we present solution for the development of a novel infrastructure, safety-as-a-service (Safe-aaS) for the road transportation industry. We introduce the term -- decision virtualization, which enables multiple end-users to access the customized decisions, remotely. We present a cost analysis for the different entities involved in the system. Analytical results show the cost and the profit of the different entities. We observe the profit obtained by the mobile sensor owners is increased by 19.69%, as compared to the static sensor owners. Additionally, we present two case studies to depict a clear view of usage of Safe-aaS.

In this work, we propose a dynamic edge node selection scheme, named as DENSE, for Safe-aaS. To optimally select the edge node, we use cooperative coalition-based game theoretic approach. Further, we apply Karush-Kuhn-Tucker (KKT) conditions to find the existence of equilibrium.

In this paper, we propose a dynamic load balancing scheme, EdgeSafe, for provisioning Safety-as-a-Service (Safe-aaS). Typically, in a Safe-aaS infrastructure, the sensor nodes are either static or mobile in nature. With the variation in the geographical location of the vehicles, the sensor nodes attached to them attain mobility. Consequently, the distance between the mobile sensor node and the edge nodes present within their vicinity changes. Further, the average number of processes executed at the edge nodes varies with the type of edge nodes and time. As the data is time-critical, it is necessary to be primarily processed at the edge nodes. Considering road transportation as the application scenario of Safe-aaS, we perform load balancing in two stages. In the first stage, we calculate the preferred capacity ratio of the edge nodes present within the communication range of the sensor nodes. We apply Markowitz Portfolio Selection Theory in the second stage to select the appropriate edge node.

In this paper, we propose a trustworthy service provisioning scheme for Safety-as-a-Service (Safe-aaS) infrastructure in IoT-based intelligent transport systems.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 formulate an integer linear programming (ILP) model to select the optimal data for decision-making, while alleviating the effects of illegitimate sensor nodes.

In this work, we propose a dynamic decision query mapping mechanism, DQ-Map, for provisioning Safety- as-a-Service (Safe-aaS) [1]. A Safe-aaS infrastructure provides customized safety-related decisions simultaneously to multipleend-users. We consider road transportation as the application scenario of Safe-aaS and termed the safety-related decision to be delivered to the end-users as decision queries (DQs). These DQs are generated according to the decision parameters selected by the end-users. The primary aim of our proposed work is to reduce the total number of sensor nodes required to generate the safety-related decisions, which minimizes both energy and time consumption

—In this paper, we propose a region-based pricing scheme, named as RegPrice, for provisioning safety-related decisions dynamically to the end-users. Typically, heterogeneoustype of sensor nodes are present in the device layer of Safe-aaS. Considering the case of safety in road transportation, we compute the fixed and variable costs incurred in procurement, deployment, and maintenance for each of these different types of sensor nodes. We introduce the concept of tariff cost, which varies with the type of road in different regions and presence of similar homogeneous sensor nodes deployed in that region. Finally, we estimate the utility of a sensor node, which is a function of the sensing area, ratio of the fixed cost to total cost incurred, responsiveness factor, and rating given by an end-user for that sensor node.

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.

In this work, we provide an architecture of blockchain integration into the Safe-aaS infrastructure. Additionally, we discuss the implementation and management of the Blockchain-enabled Safe-aaS architecture. Extensive analytical results show that the profit of the safety service provider (SSP) improves with the adoption of blockchain technology into the Safe-aaS architecture. On the other hand, the overall throughput in the proposed architecture increases with the increase in the number of decisions delivered successfully and decreases with the increase in the number of registered end-users.

In this paper, we propose a microservices and deep learning-based scheme, termed as Micro-Safe, for provisioning Safety-as-a-Service (Safe-aaS) in a 6G environment. A Safe-aaS infrastructure provides customized safety-related decisions dynamically to the registered end-users. As the decisions are time-sensitive in nature, the generation of these decisions should incur minimum latency and high accuracy. Further, scalability and extension of the coverage of the entire Safe-aaS platform are also necessary. Considering road transportation as the application scenario, we propose Safe-aaS, which is a microservices- and deep learning-based platform for provisioning ultra-low latency safety services to the end-users in a 6G scenario. We design the proposed solution in two stages. In the first stage, we develop the microservices-enabled application layer to improve the scalability and adaptability of the traditional Safe-aaS platform. Moreover, we apply the state space model to represent the decision parameters requested and the decision delivered to the end-users. During the second stage, we use deep learning models to improve the accuracy in the decisions delivered to the end-users. Additionally, we apply an assortment of activation functions to analyze and compare the accuracy of the decisions generated in the proposed scheme.

In this work, we propose Soft-Safe, a Software Defined Safety-as-a-Service (Safe-aaS) model for provisioning safety-related decisions to the registered end-users. In Safe-aaS, the end-users register to the infrastructure, provide their initial and destination location, select certain decision parameters, and make payment through a Web portal. As the safety-related decisions are time-critical in nature, therefore timely delivery of these decisions is essential. Considering these facts and road transportation as the application scenario of Safe-aaS, we address the problem of efficient decision delivery to the end-users in two stages. In the first stage, we propose a Software Defined Safe-aaS platform to address the problems of heterogeneity among the SDN switches present in the edge layer. Further, based on the utility of each of the SDN switches present within the vicinity of the end-users, we optimally select a suitable SDN switch among the available ones, for delivering them decisions in the second stage. To obtain the maximum utility for delivering decisions to the end-users, we map the interactions between the SDN controller and SDN switches as a Non-cooperative Single Leader Multiple Follower game. Then, we estimate the optimal delay incurred by an SDN switch applying the Lagrangian function and Karush-Kuhn-Tucker (KKT) conditions. Exhaustive simulation results illustrate that the energy consumed and delay incurred using our proposed scheme, Soft-Safe, is reduced compared to the existing schemes, Traditional Safe-aaS and MoRule.