Safety-as-a-Service

With the significant increase in the number of vehicles on the road, safety becomes an essential aspect of concern for both the drivers and the user-organizations in the transportation industry. The prior information of the road conditions, weather, maneuver detection, probability of road accidents, and fatality, the vehicle owners, the drivers, and the user-organizations can reduce the risk of accidents. It motivates for architecting a novel road safety paradigm, Safety-as-a-Service (Safe-aaS), where the end-users receive decisions related to road safety on a rental basis. The inconveniences faced by the end-user for deployment, maintenance, and reallocation of sensor nodes are ameliorated with the help of the Safe-aaS architecture.

 

Decision Virtualization for Safety-As-a-Service


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.

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Edge Node Selection for Safety-as-a-Service

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.

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Edge Node Selection for Safety-as-a-Service

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.

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Load Balancing for Safety-as-a-Service

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.

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Trustworthy Safety-as-a-Service

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.

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Decision Query Mapping for Safety-as-a-Service

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

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Pricing for Safety-as-a-Service

—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.

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Application of Safety-as-a-Service for Industrial IoT

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.

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