Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> The urban traffic congestions represent a major problem in the vast majority of world metropolis and the Intelligent Transport Systems are created to provide real time control and route guidance for the traffic participants, and to optimize the performance of traffic networks. The updated control and the adaptive control strategies are more and more frequently used due to their potential to reduce the delay in the intersections. New opportunities to control the traffic on street networks have been created due to the fast evolution of communications and computerized processing methods in the last decade. The role of urban traffic optimization is to both increase the efficiency of transport services and their capacity to respond to user requirements. This category of systems include information systems that disseminate information concerning the public transport schedule and the exact time-of-arrival of public transport assets, prices, routes, automatic trip tax collection systems, vehicle location systems for fleet management and safety systems. The paper herein provides an analysis of a group of intersections, starting from the Vasile Milea Boulevard, up to its intersection with the 13 Septembrie Road, while trying to determine the optimal maximum green time for each and every intersection, in order to reduce the waiting time at the semaphores, to improve the level of service and to avoid semaphore waiting queues. <s> BIB001 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> This paper considers the problem of vehicular traffic density estimation, utilizing the information cues present in the cumulative acoustic signal acquired from a roadside-installed single microphone. This cumulative signal comprises several noise signals such as tire noise, engine noise, engine-idling noise, occasional honks, and air turbulence noise of multiple vehicles. The occurrence and mixture weightings of these noise signals are determined by the prevalent traffic density conditions on the road segment. For instance, under a free-flowing traffic condition, the vehicles typically move with medium to high speeds and thereby produce mainly tire noise and air turbulence noise and less engine-idling noise and honks. For slow-moving congested traffic, the cumulative signal will largely be dominated by engine-idling noise and honks; air turbulence and tire noises will be inconspicuous. Furthermore, these various noise signals have spectral content that are very different from each other and, hence, can be used to discriminate between the different traffic density states that lead to them. Therefore, in this work, we extract the short-term spectral envelope features of the cumulative acoustic signals and model their class-conditional probability distributions, conditioned on one of the three broad traffic density states, i.e., Jammed (0-10 km/h), Medium-Flow (10-40 km/h), and Free-Flow (40 km/h and above) traffic. While these states are coarse measures of the average traffic speed, they nevertheless can provide useful traffic density information in the often-chaotic and nonlane-driven traffic conditions of the developing geographies, where other techniques (magnetic loop detectors) are inapplicable. Based on these learned distributions, we use a Bayes' classifier to classify the acoustic signal segments spanning a duration of 5-30 s, which results in a high classification accuracy of ~95%. Using a discriminative classifier such as a support vector machine (SVM) results in further classification accuracy gains over the Bayes' classifier. <s> BIB002 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> In this paper we present a comparative study of two approaches for road traffic density estimation. The first approach uses the microscopic parameters which are extracted using both motion detection and tracking methods from a video sequence, and the second approach uses the macroscopic parameters which are directly estimated by analyzing the global motion in the video scene. The extracted parameters are applied to three classifiers, the K Nearest Neighbor (KNN) classifier, the LVQ classifier and the SVM classifier, in order to classify the road traffic in three categories: light, medium and heavy. The methods are compared based on their robustness to the classification of different road traffic states. The goal of this study is to propose an algorithm for road traffic density estimation with a high precision. <s> BIB003 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> In Vehicular Networks, communication success usually depends on the density of vehicles, since a higher density allows having shorter and more reliable wireless links. Thus, knowing the density of vehicles in a vehicular communications environment is important, as better opportunities for wireless communication can show up. However, vehicle density is highly variable in time and space. This paper deals with the importance of predicting the density of vehicles in vehicular environments to take decisions for enhancing the dissemination of warning messages between vehicles. We propose a novel mechanism to estimate the vehicular density in urban environments. Our mechanism uses as input parameters the number of beacons received per vehicle, and the topological characteristics of the environment where the vehicles are located. Simulation results indicate that, unlike previous proposals solely based on the number of beacons received, our approach is able to accurately estimate the vehicular density, and therefore it could support more efficient dissemination protocols for vehicular environments, as well as improve previously proposed schemes. <s> BIB004 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> Road traffic is experiencing a drastic increase, and vehicular traffic congestion is becoming a major problem, especially in metropolitan environments throughout the world. Additionally, in modern Intelligent Transportation Systems (ITS) communications, the high amount of information that can be generated and processed by vehicles will significantly increase message redundancy, channel contention, and message collisions, thus reducing the efficiency of message dissemination processes. In this work, we present a V2X architecture to estimate traffic density on the road that relies on the advantages of combining V2V and V2I communications. Our proposal uses both the number of beacons received per vehicle (V2V) and per RSU (V2I), as well as the roadmap topology features to estimate the vehicle density. By using our approach, modern Intelligent Transportation Systems will be able to reduce traffic congestion and also to adopt more efficient message dissemination protocols. <s> BIB005 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> Cooperative vehicular systems are currently being investigated to design innovative ITS (Intelligent Transportation Systems) solutions for road traffic management and safety. Through the wireless exchange of information between vehicles, and between vehicles and infrastructure nodes, cooperative systems can support novel decentralized strategies for ubiquitous and more cost-attractive traffic monitoring. In this context, this paper presents and evaluates CoTEC (COperative Traffic congestion detECtion), a novel cooperative technique based on Vehicle-to-Vehicle (V2V) communications designed to detect road traffic congestion. CoTEC is evaluated under large-scale highway scenarios using iTETRIS, a unique open source simulation platform created to investigate the impact of cooperative vehicular systems. The obtained results demonstrate CoTEC’s capability to accurately detect and characterize road traffic congestion conditions under different traffic scenarios and V2V penetration rates. In particular, CoTEC results in congestion detection probabilities higher than 90%. These results are obtained without overloading the cooperative communications channel. In fact, CoTEC reduces the communications overhead needed to detect road traffic congestions compared to related techniques by 88%. <s> BIB006 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> Over the last few years, vehicular ad hoc networks (VANETs) have gained popularity for their interesting applications. To make efficient routing decisions, VANET routing protocols require road traffic density information for which they use density estimation schemes. This paper presents a distributed mechanism for road vehicular density estimation that considers multiple road factors, such as road length and junctions. Extensive simulations are carried out to analyze the effectiveness of the proposed technique. Simulation results suggested that, the proposed technique is more accurate compared to the existing technique. Moreover, it facilitate VANET routing protocols to increase packet delivery ratio and reduce end-to-end delay. <s> BIB007 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> The new era of the Internet of Things is driving the evolution of conventional Vehicle Ad-hoc Networks into the Internet of Vehicles (IoV). With the rapid development of computation and communication technologies, IoV promises huge commercial interest and research value, thereby attracting a large number of companies and researchers. This paper proposes an abstract network model of the IoV, discusses the technologies required to create the IoV, presents different applications based on certain currently existing technologies, provides several open research challenges and describes essential future research in the area of IoV. <s> BIB008 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> Abstract Automotive manufacturers are currently giving much interest to in-vehicle sensors and their relevant ITS applications. The number of in-vehicle sensors is in continuously increasing because of their proved benefits represented in avoided accidents, higher driving efficiencies, and ubiquitous sensing-based services. These benefits are not limited to only the vehicle's driver, but also to other vehicles and third parties. In this paper, we introduce Vehicle as a Mobile Sensor (VaaMS), a concept that shows how sensor-equipped vehicles can be considered a pivotal, mobile resource of sensory data and sensor-related applications/services. In addition, we present a new categorization of in-vehicle sensors along with a discussion of some representative sensors and several supported ITS applications. Besides, we elaborate on some communication technologies that support VaaMS. <s> BIB009 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> The number of vehicles in our roads is drastically increasing, especially in developing countries. In addition, these vehicles tend to be concentrated in urban areas which present a large population. Since traffic jams have important and mostly negative consequences, such as increasing travel time, fuel consumption, and air pollution, governments are making efforts to alleviate the increasing traffic pressure, being vehicular density one of the main metrics used for assessing the road traffic conditions. However, vehicle density is highly variable in time and space, making it difficult to be estimated accurately. In this paper, we present a solution to estimate the density of vehicles in urban scenarios. Our proposal, that has been specially designed for vehicular networks, allows intelligent transportation systems to continuously estimate vehicular density by accounting for the number of beacons received per road side unit (RSU), and also considering the roadmap topology where the RSUs are located. Using V2I communications, we are able to estimate the traffic density in a certain area, which represents a key parameter to perform efficient traffic redirection, thereby reducing the vehicles' travel time and avoiding traffic jams. Simulation results reveal that, unlike previous proposals, our approach accurately estimates the vehicular density in all the scenarios, presenting an average relative error is of only 3.04 %. <s> BIB010 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> Wireless technologies are making the development of new applications and services in vehicular environments possible since they enable mobile communication between vehicles (V2V), as well as communication between vehicles and infrastructure nodes (V2I). Usually, V2V communications are dedicated to the transmission of small messages mainly focused on improving traffic safety. Instead, V2I communications allow users to access the Internet and benefit from higher level applications. The combination of both V2V and V2I, known as V2X communications, can increase the benefits even further, thereby making intelligent transportation systems (ITS) a reality. In this paper, we introduce V2X-d, a novel architecture specially designed to estimate traffic density on the road. In particular, V2X-d exploits the combination of V2V and V2I communications. Our approach is based on the information gathered by sensors (i.e., vehicles and road side units (RSUs)) and the characteristics of the roadmap topology to accurately make an estimation of the instant vehicle density. The combination of both mechanisms improves the accuracy and coverage area of the data gathered, while increasing the robustness and fault tolerance of the overall approach, e.g., using the information offered by V2V communications to provide additional density information in areas where RSUs are scarce or malfunctioning. By using our collaborative sensing scheme, future ITS solutions will be able to establish adequate dissemination protocols or to apply more efficient traffic congestion reduction policies, since they will be aware of the instantaneous density of vehicles. <s> BIB011 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> Despite the broad utilization of cloud computing, some applications and services still cannot benefit from this popular computing paradigm due to inherent problems of cloud computing such as unacceptable latency, lack of mobility support and location-awareness. As a result, fog computing, has emerged as a promising infrastructure to provide elastic resources at the edge of network. In this paper, we have discussed current definitions of fog computing and similar concepts, and proposed a more comprehensive definition. We also analyzed the goals and challenges in fog computing platform, and presented platform design with several exemplar applications. We finally implemented and evaluated a prototype fog computing platform. <s> BIB012 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> Traffic congestion causes drivers' frustration and costs billions of dollars annually in lost time and fuel consumption. In order to overcome such issues, this paper presents a mechanism for Intelligent Transport Systems named FOX (Fast Offset XPath), which aims to detect and manage traffic congestion in Vehicular Ad hoc Networks. FOX is implemented in a FOG computing environment, taking advantage of the aspects inherent to this platform, such as scalability, low latency, the importance of geographical location and network conditions. The focus is to reduce the time to process, reroute and notify vehicles. Simulation results show that the proposed mechanism can reduce the average trip time, CO 2 emissions and fuel consumption. In particular, the average trip time was decreased approximately in 32%, the average fuel consumption in 14% and the stop time in 59%. <s> BIB013 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> With the emergence of ever-growing advanced vehicular applications, the challenges to meet the demands from both communication and computation are increasingly prominent. Without powerful communication and computational support, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in the daily life. Thus, solving this problem is of great importance. The existing solutions, such as cellular networks, roadside units (RSUs), and mobile cloud computing, are far from perfect because they highly depend on and bear the cost of additional infrastructure deployment. Given tremendous number of vehicles in urban areas, putting these underutilized vehicular resources into use offers great opportunity and value. Therefore, we conceive the idea of utilizing vehicles as the infrastructures for communication and computation, named vehicular fog computing (VFC), which is an architecture that utilizes a collaborative multitude of end-user clients or near-user edge devices to carry out communication and computation, based on better utilization of individual communication and computational resources of each vehicle. By aggregating abundant resources of individual vehicles, the quality of services and applications can be enhanced greatly. In particular, by discussing four types of scenarios of moving and parked vehicles as the communication and computational infrastructures, we carry on a quantitative analysis of the capacities of VFC. We unveil an interesting relationship among the communication capability, connectivity, and mobility of vehicles, and we also find out the characteristics about the pattern of parking behavior, which benefits from the understanding of utilizing the vehicular resources. Finally, we discuss the challenges and open problems in implementing the proposed VFC system as the infrastructures. Our study provides insights for this novel promising paradigm, as well as research topics about vehicular information infrastructures. <s> BIB014 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> The Safe Cooperating Cyber-Physical Systems using Wireless Communication (SafeCop) project addresses safety-related issues in cooperating cyber-physical systems. These systems, characterised by wireless communications, multiple stakeholders, and variable operating environments, are called Cooperative Open Cyber-Physical Systems (CO-CPS). CO-CPSs can successfully address several societal challenges -- cooperative vehicles have been shown to reduce fuel consumption as well as the number of accidents. A vehicle-to-infrastructure (V2I) cooperation for the traffic management scenario is therefore considered as a key use cases of SafeCop. In this paper, we outline the V2I traffic management scenario, assess the research goals that arise from it, and provide an overview of the architecture of the demonstrator, as well as a roadmap for its development and evaluation. <s> BIB015 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named “Fog computing” has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features. We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research. <s> BIB016 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> An increase of vehicles in a city without an efficient infrastructure of traffic management can cause damages not only financial but also environmental and social. In order to support urban traffic system to relieve the traffic congestion and the damage caused by congestion of vehicles, in this paper, we propose a mechanism for Intelligent Transport Systems named FOg RoutE VEhiculaR (FOREVER), in order to assist the traffic management in Vehicular Networks (VANET). For achieving this, FOREVER will detect and recommend an alternative route for the vehicles to avoid previous congestion. FOREVER is based on FOG computing paradigm that aims of to compute and modify the route of the vehicle to avoid the formation of congestion. Thus, the results show that FOREVER had a reduction about 7.9% of the CO 2 emissions, 8.3% the stop time and 7.6% of the trip time. <s> BIB017 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> A novel framework is developed in this paper, to increase the real-time roadway traffic condition assessment accuracy, which integrates connected vehicle technology (CVT) with artificial intelligence (AI) paradigm forming a CVT-AI method. Traffic density is a major indicator of traffic conditions. In this paper, the traffic operational condition is assessed based on traffic density. A simulated network of Interstate 26 in South Carolina is developed to investigate the effectiveness of the method. The assumption is that the vehicle onboard units will forward the CV generated data to the edge devices (e.g., roadside units) for further processing. CV generated distance headway and number of stops, and speed data are used to estimate traffic density. This paper reveals that, with 20% and greater CV penetration levels, the accuracy of the density information with the AI-aided CVT is a minimum of 85%. Moreover, this paper demonstrates that the integrated CVT-AI method yields a higher accuracy with the increase of CV penetration levels. Level of service (LOS) is the indicator of traffic congestion level on highways and is described with traffic density in terms of passenger car/mile/lane for a specific free flow speed. LOS estimated using the CVT-AI density estimation method is compared with the density estimation algorithm used by the Caltrans Performance Measurement System (PeMS), which relies on the occupancy and flow data collected by the freeway inductive loop detectors. With a 10% or more CV penetration, higher accuracy is achieved using the CVT-AI algorithm compared with the PeMS density estimation algorithm. <s> BIB018 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> Some of the mobility challenges in the cities can be approached with the Internet of Things, Cloud Computing, and Fog Computing. In this work we consider the city as a system of systems and focus on the interaction of those entities with the mobility systems. A key element that allows this interaction is a vehicle on-board unit. In this work we address the interconnection, enabling technologies, interoperability, scalability, geo-distribution and time constrains aspects in the architectural design of mobility systems. For this we propose the On-Board Unit architecture and cover three different levels of technification in information and communication technologies. Based on this differentiation we are able to include Cloud and Fog Computing into our proposed designs. <s> BIB019 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> Vehicular fog computing extends the fog computing paradigm to conventional vehicular networks. This allows us to support more ubiquitous vehicles, achieve better communication efficiency, and address limitations in conventional vehicular networks in terms of latency, location awareness, and real-time response (typically required in smart traffic control, driving safety applications, entertainment services, and other applications). Such requirements are particularly important in adversarial environments (e.g., urban warfare and battlefields in the Internet of Battlefield Things involving military vehicles). However, there is no one widely accepted definition for vehicular fog computing and use cases. Thus, in this article, we formalize the vehicular fog computing architecture and present a typical use case in vehicular fog computing. Then we discuss several key security and forensic challenges and potential solutions. <s> BIB020 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> Road safety, traffic congestion and efficiency of the transport sector are major global concerns. Improving this is the primary objective of intelligent transport systems (ITS). Having Internet of things (IoT) based solutions for ITS would enable motorists to obtain prior contextual guidance to reduce congestion and avoid potential hazards. IoT based solutions enabling collection of data from client nodes in a wireless sensor network in the transport environment implementing ITS goals is studied. The parameters to be monitored, type of sensors and communication related design parameters are identified to develop an effective IoT based solution. Road safety techniques studied include distance sensing, improper driving detection and accident prevention, weather related events and negligent driving detection and accident avoidance. Vehicle to vehicle communication and vehicle to infrastructure based channels are studied. Wireless communication technologies suitable for the channels are studied. Additional benefits and services that can be added to a system with the IoT approach are also studied. The effectiveness of such a system is studied with the use of validation framework. Multiple case studies of current and future IoT based ITS along with the challenges in the application is discussed. <s> BIB021 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> Smart grid is the process of applying ICT in order to optimize energy consumption and decrease energy loses. This paper presents a three tier Internet of Thing based hierarchical framework for the smart home, as a reflection to the present lack of intelligent solutions that do not fully use the advantages of Internet of Thing technologies. Our framework aims to extend the smart home to microgrid level, in order to integrate all renewable distributed energy sources from the microgrid and to achieve better energy optimization. As an extension to the traditional data processing, we define fog computing approach for smart home. Through simulation on real smart meter dataset, we showed that fog computing based on predictive filters can reduce the number of transmissions and minimize smart home network traffic. <s> BIB022 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> I. INTRODUCTION <s> Abstract Intelligent transportation systems are designed to provide innovative applications and services relating to traffic management, as well as to facilitate the access to information for other systems and users. The compelling motivation for employing underutilized onboard resources for transportation systems and the advancements in management technology for Cloud computing resources has promoted the concept of Vehicular Clouds. This work gathers and describes the most recent approaches and solutions for Vehicular Clouds, featuring applications, services, and traffic models that can enable Vehicular Cloud in a more dynamic environment. We have considered a large number of applications and services that showed relevance in the scope of the transportation system, benefiting its management, drivers, passengers, and pedestrians. Nevertheless, the high traffic mobility imposes as a significant challenge in implementing a Vehicular Cloud on continually changing physical resources. The dynamics of the environment bring fundamental issues and increase the complexity of building this new type of Cloud. By analyzing the existing traffic models, we found that Vehicular Cloud computing is technologically feasible not just in the static environment, like a parking lot or garage where vehicles are stationary, but also the dynamic scenarios, such as highways or streets where vehicles move. <s> BIB023
EHICUALR congestion is a global phenomenon. It is impacting the efficiency of the transport sector by introducing delays which cause increases in travel time and leads to increased fuel consumption and associated vehicular emissions BIB010 , BIB013 , BIB017 . Vehicular congestion causes loss in productivity and the monetary loss is estimated to be as high as 160 billion dollars per year in the U.S alone BIB017 . Of the total cost, 85 % of the economic cost of congestion is due to loss of work hours caused by increased travel time, 13 % of the loss is attributed to fuel wastage and increased vehicular emissions is responsible for the remaining 2 % of the loss BIB013 . Vehicular congestion is expected to severely worsen in magnitude due to rapid increase in the number of vehicles. In a study conducted by IBM, it was estimated that current global vehicular population is over a billion and is expected to T his work was supported in part by the National Research Foundation, South Africa under Grant IFR160118156967 and RDYR160404161474. Arnav Thakur is with the Department of Electrical, Electronic and Computer Engineering, University of Pretoria, South Africa. Reza Malekian is with the Department of Electrical, Electronic and Computer Engineering, University of Pretoria, South Africa. Email: reza.malekian@ieee.org double by the year 2020 BIB017 . To enhance the efficiency of the transportation sector and reduce the associated economic and environmental consequences, an accurate real-time vehicular traffic congestion identification system is required to be used in effective traffic management schemes that improve traffic flow and utilization of road infrastructure. Vehicular congestion is quantified by using the traffic density estimate metric which is the number of vehicles per unit area  . The traffic density estimation techniques use parameters that include road occupancy rates, difference of incoming and outgoing traffic in an observation area, traffic flow estimation and motion detection and tracking to identify congestion BIB003 . Vehicular congestion detection using traffic density estimation is performed by using contact and non-contact based sensing techniques  , BIB003 , BIB004 , BIB011 . In contact based techniques, the number of vehicles passing the sensory node are detected by loop detectors and pressure pads BIB018 , BIB001 . Contact based methods are not reliable as accuracy is dependent on maintenance of sensing apparatus and results are valid for a small coverage area BIB003 , BIB018 . Thus, contact based techniques are not suitable for estimating traffic density in a large observation area as it would incur high deployment and maintenance costs of sensory infrastructure. Traffic density estimation for detecting vehicular congestion using non-contact based sensing techniques deploy surveillance camera, microphones and connected vehicle technology (CVT)  , BIB003 , BIB011 , BIB018 . Surveillance cameras based non-contact techniques capture the traffic stream. Segmentation and motion tracking algorithms are applied on vehicle objects in the video sequence to extract motion related parameters which are processed by classifiers to determine congestion levels BIB007 . In microphone based sensing technique, cumulative acoustic signals are acquired that contain components of engine noise, idle engine noise and tire noise BIB002 . Traffic congestion is determined based on the proportion of the above mentioned components in the input signal BIB002 . Non-contact traffic density estimation for congestion detection based on connected vehicle technology use the Internet of Vehicles (IoV) approach. IoV are Internet of Things (IoT) based solutions for vehicular clients designed to achieve the road safety and traffic management goals of intelligent transportation systems (ITS) BIB008 , BIB023 , BIB019 . A wireless sensor network (WSN) is used in the CVT based BIB019 , BIB014 , BIB020 . The WSN uses vehicle to vehicle (V2V) communication for exchanging data between vehicular sensory interfaces and vehicle to infrastructure communication (V2I) for exchange of data between vehicular nodes and RSUs BIB023 , BIB021 . Parameters collected from the vehicular sensory nodes include vehicle position, direction and route, frequency of braking events, surface area of vehicles and average velocity and acceleration of vehicle node BIB013 , BIB017 , BIB009 . The collected parameters are processed by the traffic density estimation algorithms for identifying congestion on the processing node which varies across IoV architectures being used  , BIB004 , BIB011 , BIB005 . Results from non-contact based techniques such as microphone and surveillance cameras are not reliable as the results are dependent on environmental conditions  , BIB002 . Moreover, results from surveillance cameras are not in realtime due to computationally intensive nature of the image processing algorithms used for segmentation and tracking of traffic stream BIB007 . Results obtained from CVT based technique are not affected by weather and environmental conditions. Additionally, the sensory data is ready for processing in real time. Mobile nature of the sensory nodes used in CVT based approach enables this technique to be used for large deployment areas with low costs and higher reliability of the inputs  , BIB006 . Thus, CVT based techniques for congestion identification are reliable and are suitable for usage in an effective traffic management system BIB013 , BIB005 , BIB015 . Vehicular networks require collection and processing of large amounts of data in real-time. The information being exchanged is time-sensitive due to high mobility of the nodes and the communication links have limited bandwidth. The network is also required to be context location aware BIB012 . By the year 2020, it is estimated that there will be up to 152 million active connected vehicles that will generate more than 30 TB of data every day BIB020 . Thus, the communication system has to be robust for a practically feasible solution that meets Quality of Service (QoS) and Quality of Performance (QoP) requirements BIB014 , BIB020 . Distributed cloud computing based approaches of Fog Computing are used in conjunction with cloud computing to optimize communication in the WSN by reducing the number of data exchange events BIB016 . Fog computing uses localized computing and lightweight algorithms to process sensor data at edge devices which optimizes the capacity of the communication and computational components of the system BIB022 . In this study, Fog Computing for IoV systems are studied for developing a practically feasible solution for detecting vehicular congestion. Remainder of this paper is organized as follows: In section II, multiple cloud architectures suitable for WSNs are studied and compared. In section III, vehicular Fog based cloud computing architecture for IoV applications is studied. In section IV vehicular congestion detection using CVT based WSN and Fog Computing based principles are studied. Results from the studied literature is discussed in section V and a conclusion is drawn in section VI.