Using Vehicles as Fog Infrastructures for Transportation Cyber-Physical Systems (T-CPS): Fog Computing for Vehicular Networks

Using Vehicles as Fog Infrastructures for Transportation Cyber-Physical Systems (T-CPS): Fog Computing for Vehicular Networks

Md Muzakkir Hussain (Aligarh Muslim University, Aligarh, India) and M.M. S. Beg (Aligarh Muslim University, Aligarh, India)
DOI: 10.4018/IJSSCI.2019010104
OnDemand PDF Download:
List Price: $37.50
10% Discount:-$3.75


The advent of intelligent vehicular applications and IoT technologies gives rise to data-intensive challenges across different architectural layers of an intelligent transportation system (ITS). Without powerful communication and computational infrastructure, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in daily life. The current cloud computing and cellular set-ups are far from perfect because they are highly dependent on, and bear the cost of additional infrastructure deployment. Thus, the geo-distributed ITS components require a paradigm shift from centralized cloud-scale processing to edge centered fog computing (FC) paradigms. FC outspreads the computing facilities into the edge of a network, offering location-awareness, latency-sensitive monitoring, and intelligent control. In this article, the authors identify the mission-critical computing needs of the next generation ITS applications and highlight the scopes of FC based solutions towards addressing them. Then, the authors discuss the scenarios where the underutilized communication and computational resources available in connected vehicles can be brought in to perform the role of FC infrastructures. Then the authors present a service-oriented software architecture (SOA) for FC-based Big Data Analytics in ITS applications. The authors also provide a detailed analysis of the potential challenges of using connected vehicles as FC infrastructures along with future research directions.
Article Preview

1. 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.

Complete Article List

Search this Journal:
Volume 15: 1 Issue (2023): Forthcoming, Available for Pre-Order
Volume 14: 4 Issues (2022): 1 Released, 3 Forthcoming
Volume 13: 4 Issues (2021)
Volume 12: 4 Issues (2020)
Volume 11: 4 Issues (2019)
Volume 10: 4 Issues (2018)
Volume 9: 4 Issues (2017)
Volume 8: 4 Issues (2016)
Volume 7: 4 Issues (2015)
Volume 6: 4 Issues (2014)
Volume 5: 4 Issues (2013)
Volume 4: 4 Issues (2012)
Volume 3: 4 Issues (2011)
Volume 2: 4 Issues (2010)
Volume 1: 4 Issues (2009)
View Complete Journal Contents Listing