In this paper, we propose a mechanism, termed as Opti-Safe, such that the demand of end-users and supply of mobile sensor nodes is fulfilled in a Safety-as-a-Service (Safe-aaS) platform for Social Internet of Vehicles (SIoV) environment. The end-users/customers request for decision parameters and make payment to the Safe-aaS platform. Based on their request, safety-related customized decisions are provided to them. None of the existing mechanisms provide the equilibrium condition between the sensor and vehicle owners, customers, and SSPs. The customers' requests for safety services and the presence of active, mobile sensor nodes present within a particular geographical region fluctuates with time. These mobile sensor nodes attain mobility with the variation in the geographical location of the vehicles, to which they are attached. Moreover, the service region of the SSPs is bounded and may comprise various types of geographical regions such as hilly and plane. To quantify the quality of service provided by SSPs, we define the term satisfaction factor (SF) for the registered users. The demand of customers is computed depending on the SF value. In order to find the equilibrium point, we model the utility of SSPs as an optimization function. We apply Karush-Kuhn-Tucker (KKT) conditions to find the optimal price charged by the SSPs and the number of mobile sensor nodes necessary for decision generation. We extensively simulate our proposed mechanism and observe that the satisfaction factor of users and utility of SSPs fluctuates with time.

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 article, we propose a scheme, named P-VERSE, to prioritize the emergency vehicles (ERVs) in the absence of a traffic signal at a multiway intersection point on a road. We consider the ERVs, such as ambulance, fire trucks, or police vehicles, as prioritized vehicles and provide them safe and quick passage. The traffic signal is a traditional approach to minimize congestion, avoid collision among the vehicles, and reduce accidents. However, this results in the repetitive interruption of vehicles, thereby increasing their waiting time. To address these issues, we formulate a scheme that executes in two stages. In the first stage, we use Markov Chain to predict the future path of each vehicular nodes. Based on the predicted path, the common and ERVs cooperatively form groups to avoid collision and congestion at the intersection point. On the other hand, in the second stage, we apply a cooperative coalition-based game-theoretic approach to design the strategic interactions among the vehicular nodes. These vehicular nodes act as players and dynamically form coalitions among them. Depending upon the utility of the coalition, the vehicular nodes either merge with or split out from a coalition. These nodes form a coalition to provide safe passage to the ERVs. Further, we constrain our optimization problem to the Karush-Kuhn–Tucker (KKT). Extensive simulation-based analysis of our proposed scheme, P-VERSE, demonstrates that the energy consumption is reduced and utility is improved by 31.37% and 42.28% compared to the existing schemes.

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.

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 propose a dynamic decision query mapping mechanism, DQ-Map, for provisioning Safety-as-a-Service (Safe-aaS) (Royet al., 2018). A Safe-aaS infrastructure provides customized safety-related decisions simultaneously to multiple end-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 safety-related decisions, which minimizes both energy and time consumption. Further, the requested DQs are processed and a decision is generated in three different stages. First, the DQs are categorized asemergency decision query(EDQ) andnonemergency decision query(NEDQ), depending upon the type of vehicle from where the end-users have requested safety services. The EDQs and NEDQs are mapped with the stored decisions present in the database of the decision virtualization layer during the second level. In case of mismatch with the stored decisions in the database, EDQs are directly executed from the sensor nodes deployed at a particular geographical location or into the vehicles, in the device layer of the Safe-aaS infrastructure. In the third level, the similarity score of NEDQs, which do not match with the parameters of the stored decisions, is computed. Based on the number of similar decision parameters present in them, the similarity score is computed. Extensive simulation results of the proposed scheme, DQ-Map, depict that the amount of energy consumed and time required to generate a decision is reduced by 55.16% and 54.55%, respectively, compared to the traditional Safe-aaS architecture.

In this article, we propose a software-defined fog architecture, named as Soft-Health, to serve various Internet-of-Things (IoT)-based healthcare applications. The health conditions of the patients fluctuate over time. Further, specialized medical care may not always be available in all healthcare facilities. The use of wireless body area network (WBAN) for continuous patient monitoring addresses the issue to a certain extent. However, as the physiological parameters of a patient are time-critical in nature, any delay, packet loss, and network overhead, may result in deterioration of the patient’s health conditions. Considering this, we design a Software-defined fog-enabled IoT platform for various healthcare applications. We consider that the fog layer comprises SDN switches that allocate the packet to the appropriate fog/cloud depending upon the criticality index (CI) of the data packets originating from patients. We mathematically formulate the CI, based on the physiological parameters sensed and transmitted to the switches. Further, we design an optimization function to obtain the maximum utility of a fog node, for an optimal number of processes executed by that node. We apply the Lagrangian method to simplify the optimization function and solve it using Karush–Kuhn–Tucker (KKT) conditions. We apply the auto-regression model to predict the total delay incurred and the total energy consumed by the proposed scheme. Exhaustive analysis of our proposed scheme, Soft-Health, demonstrates that the delay incurred decreases by 24.57% and 40.1% approximately, compared to the existing schemes, Mobi-Flow, and CARE, respectively.

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. Extensive simulation of our proposed scheme, Micro-Safe, demonstrates that latency is improved by 26.1 – 31.2%, energy consumption is reduced by 22.1 – 29.9%, throughput is increased by 26.1 – 31.7%, compared to the existing schemes.

In this article, we introduce anenergy-efficient decision deliverymechanism,Safe-Serv, in the Safety-as-a-Service (Safe-aaS) infrastructure for the road transportation industry. A Safe-aaS architecture provides safety-related customized dynamic decisions to the registered end-users. Moreover, the concept ofdecision virtualizationenables to deliver the same decision to multiple end-users at the same time. The sensor nodes sense and transmit the data to the edge node/cloud, which is further processed to generate a decision. As the sensor nodes are energy-constrained in nature, energy efficiency is one of the important parameters to be considered for Safe-aaS infrastructure. Safe-Serv reduces energy consumption through the elimination of redundant data transmission from the sensor node to the edge node or cloud. We use the cooperative Nash bargaining approach among different homogeneous sensor nodes, which bargain among themselves to transmit data to the edge node/cloud. Based on the total dissipated energy, effective proportional distance, duty factor, nodal delay, and cost-efficient state, the appropriate sensor node is chosen. Thus, the selected sensor node transmits data to the edge layer or cloud. We incorporate the cost of data transmitted by the sensor node, which leads to cost-effective utilization of the resources. Through extensive simulation, we observe that the energy dissipated by the sensor nodes using the proposed scheme, Safe-Serv, is reduced by 85 and 78 percent approximately compared to the existing schemes – SASPENCE and manoeuvre-based trajectory planning – respectively.

We propose “DROPS”, a scheme which dynamically selects radio protocols in an energy-constrained wearable IoT healthcare system. We consider the use of multiple radio protocols, which are capable of transmitting a patient's sensed physiological parameters to the server through Local Processing Units (LPUs). As the health parameters are non-stationary and temporally fluctuating, especially for critical patients, the selection of an appropriate radio protocol is essential to maintain the accuracy and timely delivery of data from the patient to the server. Additionally, the mobility of patients through various locations within the hospital mandates the selection of the best radio protocol among the multiple available ones for each location, to enable data to offload to the remote server. We use single-leader-multiple-follower Stackelberg non-cooperative game to map the strategic interactions between a patient's LPU and the hospital's server. “DROPS” dynamically selects the appropriate radio protocol, based on the criticality index of a patient, the reputation of the radio, the Euclidean distance between the radios and the LPU, and the load on the protocol. Results on real-life data and their large-scale emulation show that the data rate increases by almost 78% and throughput by approximately 7%, as compared to existing schemes.

In this paper, we propose a service handoff scheme, termed as Safe-Passé, for provisioning Safety-as-a-Service (Safe-aaS) to the end-users in a 5G environment. A Safe-aaS architecture provides customized safety-related decisions to the end-users. Practically, the service region of a Safety Service Provider (SSP) is bounded. On the other hand, the distance for which the end-users request for services may cover the service region of multiple SSPs. As a result, the services provided to the end-users are interrupted due to switching from the service region of one SSP to another. However, none of the existing handoff schemes provide customized safety-related decisions to the end-users. Considering road transportation as the application scenario of Safe-aaS in a 5G-enabled Intelligent Transportation System (ITS) environment, we propose a service handoff scheme among the multiple service providers for provisioning safety-related decisions to the end-users. Based on the eminence and profit of a SSP to which the end-users have initially registered for services, we compute the profit of the nearest SSP, who agrees to provide services to that end-user. We map these interactions among the SSPs to a cooperative coalition game, where the SSPs act as players. Extensive simulation-based analysis demonstrates that the ratio of the end-users served to the total number of end-users is improved by 4.19% and 3.65% using Safe-Passé compared to the existing handoff schemes, UCH and VHO.

In this paper, we propose a dynamic edge node selection scheme, named as DENSE, for the Safety- as-a-Service (Safe-aaS) architecture [1]. A Safe- aaS infrastructure provisions customized safety- related decisions remotely to the registered end- users. Depending on the time-criticality of data, the static and mobile sensor nodes sense and transmit data to the edge nodes. The number of edge nodes present within the proximity of a mobile sensor node vary with the change in the locations of the vehicle. Moreover, the distance between the mobile sensor node and the edge nodes, within its proximity, change with the variation in the vehicle's location. Therefore, in such a situation, dynamic selection of the appropriate edge node for processing the time-critical data is necessary. 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. The analytical results of our proposed scheme, DENSE, shows that the average utility increases by 11.33% with respect to the available storage space of the edge nodes. Moreover, the average utility increases by 50.43% with respect to the average number of tasks executed per unit time by the edge node.

In this work, we propose a scheme for the formation of Dynamic Virtual Sensor for Overlapping Region (DIVISOR) in a IoT-based sensor-cloud platform. Practically, the interest of deployment area of similar sensor nodes by respective sensor owners may be the same, and consequently, the areas of coverage of the deployed sensor nodes of the different owners overlap with one another. Thus, in such a scenario, each of the sensor owners must get equal opportunity to earn profit from the deployment of their sensor nodes. Therefore, in order to provide an equal privilege to all sensor owners, we propose the scheme, DIVISOR, in order to form virtual sensors. This is one of the first attempts for the dynamic formation of virtual sensors, where the overlapping area of deployed sensor nodes by different sensor owners is considered. The experimental results demonstrate that the proposed scheme is energy-efficient and the average number of participating nodes increases with the increase in the total number of nodes in the network. Moreover, each sensor owner gets almost equal opportunity to rent their nodes.

In this paper, we present solution for the development of a novel infrastructure, safety-as-a-service (Safe-aaS) for the road transportation industry. Safe-aaS provides safety related decisions to the registered end-users. The safety decisions are customized as per the end-user types and their requirements. Existing related research work on road safety focus on the development of the safety systems, which are able to assist the driver of the vehicle. However, none of the works serves as a common platform for providing customized decisions dynamically as per user requirements. As per our knowledge, Safe-aaS is one of the first attempts in its domain, where multiple end-users receive safety related decision dynamically. An end-user enjoys the pay-per-use service of Safe-aaS, without concerning about the back-end process. Safe-aaS is based on service oriented architecture, where different business entities such as vehicle owners, sensor owners, safety service provider, and end-users are involved. We introduce the term, decision virtualization, which enables multiple end-users to access the customized decisions remotely. We present possible cost analysis for the entities involved in the system. Analytical results show the cost and profit analysis of the different entities. We observe the profit gain by mobile sensor owner is 19.69% more as compared to static sensor owner. In the presence of 5, 10, and 15 end-users, payable rent varies between 15%-20%. Additionally, we present two case studies to depict a clear view of usage of Safe-aaS.