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Top1. Introduction
The integration of the information and communication technologies (ICT), cloud computing, Software defined networking (SDN) and Internet of Things (IoT) with century old transportation infrastructures lead to the evolution of Intelligent Transportation Systems (ITS) (Mohd Saqib et al., 2017). With the emergence of ITS application, the number of advanced vehicular applications is growing at ever-fast rate (M. Hussain et al., 2018). Consequently, the challenges to meet the demands from both communication and computation are becoming increasingly prominent. The disruptive IoT technology will serve as a key driver for the success of data-driven ITS realization (Zeng et al., 2011). The vehicles in ITS network when embedded in IoT technology lead to emergence of Internet of Vehicles (IoV) (Kaiwartya et al., n.d.). IoV ensures real-time connectivity, automation and tracking of on-premise smart objects. It seamlessly integrates information sensing, storage, processing, intelligence, and control capabilities into the physical ITS components, thus forming a Cyber-Physical System (CPS) (Hussain et al., n.d.). The IoT nodes intelligently capture the behaviors and actions of all the entities in the ITS supply-chain to deliver smart, sustainable, and secure mobility solutions, and ensure economical and environmentally sustainable use.
These days, due to advancements in ITS data generation and storage technologies, the connected vehicles became the key driver of IoT (Bonomi et al., 2013). Such vehicles are leveraged with on-board sensor units and build connected networks with ITS sub-systems such as roadside infrastructures (RSU), parking lots, smart vehicle charging stations (Hiraishi and Mizoguchi, 2018), Advanced Metering Infrastructures (AMI), etc. Also, by enabling the IoT technology a global standard and basis for ITS communications, new avenues will be created for maximizing the prospects for future innovations (Muhammad Baqer Mollah et al., 2017). The cyber-machineries in connected vehicular network aims at providing real-time connectivity, automation and tracking of on-premise smart devices, deployed for analysis monitoring and control of the ITS infrastructures. The IoT evolves the legacy ITS architecture into a Transportation Cyber-Physical Systems (T-CPS) (Figure 1) where the cyber half forms the basis of data-driven big data analytics, ultimately focused to manage the operation of physical sub-system (Things 2018). The IoT aided ITS turns into an integrated framework of real and virtual worlds where the attributes from the real (physical) as well as virtual (cyber) world are fed as input to the control/data centers (clouds), to generate simulation models for predicting the future mechanisms and transitions (Birman, Ganesh, and Renesse, 2011).
Figure 1. A framework for transportation cyber-physical systems (T-CPS)
Since the T-CPS comprises billions of these intelligent communicating devices (vehicles, roadside infrastructures, sensors, etc.) that generate enormous amount of data, and hence performing analysis on this data is a significant task (Zhu et al., 2018). Thus, the growing volume and velocity of IoT data exchange in T-CPS infrastructures urge for rigorous communication resources, leading to the bottleneck in terms of data processing, data latency, and traffic overhead. The data deluge created due to millions of IoT aided ITS devices embark urgent needs for big data management techniques. The IoT-ITS marriage comes with the cost of humanitarian as well as monetary expenses, incurred during storage, analytics and management of generated big data. Without robust communication and computational support, the ITS applications and services will still stay in the concept phase and cannot be set into practice in day to day life (Hou et al., 2016). The existing ITS solutions, viz. cellular networks, road-side units (RSUs) and cloud computing facilities, are far from ideal because they are highly reliant on and bear the cost of additional infrastructure deployments.