IoV-based Deployment and Scheduling of Charging Infrastructure in Intelligent Transportation Systems

in IEEE Sensors Journal (2020)

Internet of vehicles (IoV) is an emerging paradigm to exchange and analyze information collected from sensors using wireless technologies between vehicles and people, vehicles and infrastructure, and ... [more ▼]

Internet of vehicles (IoV) is an emerging paradigm to exchange and analyze information collected from sensors using wireless technologies between vehicles and people, vehicles and infrastructure, and vehicles-to-vehicles. With the recent increase in the number of electric vehicles (EVs), the seamless integration of IoV in EVs and charging infrastructure can offer environmentally sustainable and budget-friendly transportation. In this paper, we propose an IoV-based framework that consists of deployment and scheduling of a mobile charging infrastructure. For the deployment, we formulate an optimization problem to minimize the total cost of mobile charging infrastructure placement while considering constraints on the number of EVs that can be charged simultaneously. The formulated problem is mixedinteger programming and solved by using the branch and bound algorithm. We then propose an IoV-based scheduling scheme for EVs charging to minimize travel distance and charging costs while satisfying the constraints of charging time requirement of EVs and resources of a charging station.We consider passive road sensors and traffic sensors in the proposed IoV-based scheduling scheme to enable EV users for finding a charging station that can fulfill their requirements, as well as to enable service providers to know about the demand in the area. Simulation results illustrate the significant impact of the optimal deployment of charging infrastructure and scheduling optimization on the efficiency of EVs charging. [less ▲]

Emerging Edge Computing Technologies for Distributed IoT Systems

in IEEE Network (2019)

The ever-increasing growth of connected smart devices and Internet of Things (IoT) verticals is leading to the crucial challenges of handling the massive amount of raw data generated by distributed IoT ... [more ▼]

The ever-increasing growth of connected smart devices and Internet of Things (IoT) verticals is leading to the crucial challenges of handling the massive amount of raw data generated by distributed IoT systems and providing timely feedback to the end-users. Although existing cloud computing paradigm has an enormous amount of virtual computing power and storage capacity, it might not be able to satisfy delaysensitive applications since computing tasks are usually processed at the distant cloud-servers. To this end, edge/fog computing has recently emerged as a new computing paradigm that helps to extend cloud functionalities to the network edge. Despite several benefits of edge computing including geo-distribution, mobility support and location awareness, various communication and computing related challenges need to be addressed for future IoT systems. In this regard, this paper provides a comprehensive view on the current issues encountered in distributed IoT systems and effective solutions by classifying them into three main categories, namely, radio and computing resource management, intelligent edge-IoT systems, and flexible infrastructure management. Furthermore, an optimization framework for edge-IoT systems is proposed by considering the key performance metrics including throughput, delay, resource utilization and energy consumption. Finally, a Machine Learning (ML) based case study is presented along with some numerical results to illustrate the significance of ML in edge-IoT computing. [less ▲]