Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> III. VEHICULAR CLOUD AND FOG COMPUTING <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> BIB001 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> III. VEHICULAR CLOUD AND FOG COMPUTING <s> Modern infrastructure, such as dense urban areas and underground tunnels, can effectively block all GPS signals, which implies that effective position triangulation will not be achieved. The main problem that is addressed in this project is the design and implementation of an accurate vehicle location system using radio-frequency identification (RFID) technology in combination with GPS and the Global system for Mobile communication (GSM) technology, in order to provide a solution to the limitation discussed above. In essence, autonomous vehicle tracking will be facilitated with the use of RFID technology where GPS signals are non-existent. The design of the system and the results are reflected in this paper. An extensive literature study was done on the field known as the Internet of Things, as well as various topics that covered the integration of independent technology in order to address a specific challenge. The proposed system is then designed and implemented. An RFID transponder was successfully designed and a read range of approximately 31 cm was obtained in the low frequency communication range (125 kHz to 134 kHz). The proposed system was designed, implemented, and field tested and it was found that a vehicle could be accurately located and tracked. It is also found that the antenna size of both the RFID reader unit and RFID transponder plays a critical role in the maximum communication range that can be achieved. <s> BIB002 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> III. VEHICULAR CLOUD AND FOG COMPUTING <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> BIB003 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> III. VEHICULAR CLOUD AND FOG COMPUTING <s> Fog computing is a promising paradigm in terms of extending cloud computing to an edge network. In a broad sense, fog computing in Internet-of-Vehicles(IoV) provides low-latency services since fog nodes are closely located with moving cars and are locally distributed. In this paper, we propose a fog computing architecture based on a publish/subscribe model. After that, we describe a traffic congestion control scenario using a smart traffic light system which operates on top of the proposed architecture. Furthermore, we propose an upper-level domain ontology in order to enhance the expressivity of knowledge and describe a variety of semantic properties which interlink spatial information in IoV. Finally, we present an active rule where supports the exchange of event-driven messages between publishing and subscribing fog nodes. <s> BIB004 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> III. VEHICULAR CLOUD AND FOG COMPUTING <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> BIB005 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> III. VEHICULAR CLOUD AND FOG COMPUTING <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> BIB006 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> III. VEHICULAR CLOUD AND FOG COMPUTING <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> BIB007 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> III. VEHICULAR CLOUD AND FOG COMPUTING <s> In cities, where the number of vehicles continuously increases faster than the available traffic infrastructure to support them, congestion is a difficult issue to deal with and it becomes even worse in case of car accidents. This problem affects many aspects of the modern society, including economic development, traffic accidents, increase in greenhouse emissions, time spent, and health damages. In this context, modern societies can rely on traffic management system to minimize traffic congestion and its negative effects. Traffic management systems are composed of a set of application and management tools to improve the overall traffic efficiency and safety of the transportation systems. Furthermore, to overcome such issue, traffic management system gathers information from heterogeneous sources, exploits such information to identify hazards that may potentially degrade the traffic efficiency, and then provides services to control them. With this question in mind, this article presents a classification, ... <s> BIB008 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> III. VEHICULAR CLOUD AND FOG COMPUTING <s> This paper describes the work that has been done in the design and development of a wireless OBD II fleet management system. The system aims to measure speed, distance and fuel consumption of vehicles for tracking and analysis purposes. An OBD II reader is designed to measure speed and mass air flow, from which distance and fuel consumption are also computed. This data is then transmitted via WiFi to a remote server. The system also implements GPS tracking to determine the location of the vehicle. A database management system is implemented at the remote server for the storage and management of transmitted data and a graphical user interface (GUI) is developed for analysing the transmitted data . Various qualification tests are conducted to verify the functionality of the system. The results demonstrate that the system is capable of reading the various parameters, and can successfully process, transmit and display the readings. <s> BIB009 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> III. VEHICULAR CLOUD AND FOG COMPUTING <s> This paper presents ThinGs In a Fog (TGIF)- a system designed to support interdisciplinary research that fall under the broad context of the Internet of Things. The framework is based on an Edge Computing system design that distributes application processing to system compute nodes leveraging geographic compute location diversity of a Cloud-to-the-edge to support machine-to- machine interactions that potentially have real- time constraints. To provide further insight, we focus on Connected Vehicle as an exemplar application domain. This paper provides a summary of work-to-date, including results from a small prototype of the system deployed at Clemson University. We illustrate the system be presenting work-to-date on the design, implementation and evaluation of a Queue Warning which is an application that has been studied thoroughly by the transportation community. This particular application is nicely suited for illustrating the additional benefits and complexities associated with implementing well understood applications in emerging distributed computing environments expected to be supported by the IoT. <s> BIB010 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> III. VEHICULAR CLOUD AND FOG COMPUTING <s> This paper proposes a combined longitudinal and lateral control approach for an intelligent vehicle system based on energy dissipation. The vehicle system dynamics resembles a series of mass/spring/damper systems that are dissipative, i.e., the energy of the system decays to zero eventually. Thus, the nonlinear-optimal longitudinal and lateral coupling control problem of the intelligent vehicle system is transformed into a dissipative control design based on an energy storage function. To satisfy the g-performance, with respect to the quadratic supply rate, the storage function is developed by using a back-stepping based Lyapunov method and a step-by-step improvement of performance bounds. A dissipative feedback control law is formulated by successive approximation based on the stepby-step reduction of the value of g. The results of the adaptive vehicle control simulations and test-bed experiments are provided and verified by the respective comparison of energy consumption on different values of g and speed adaption under different road geometries. <s> BIB011
The system in a connected vehicle technology based approach with Fog computing contains three layers which are the data generation, fog and cloud layer BIB006 . The edge devices part of the data generation layer gather sensory data and consume IoV services are the OBUs placed in the vehicles BIB007 BIB001 . Sensors used in the mobile probes at the network edge include speed sensors, distance sensors, radars, accelerometer, gyroscope, GPS, tilt sensors and image and audio sensors for surface analysis  , BIB002 , BIB008 , BIB011 . Sensory information from the vehicle's ECU is also acquired from the OBD II interface BIB009 . Sensory data is preprocessed to make decisions applicable for the vehicle that gathers the sensory information. The Fog nodes in the IoV system are the stationary waypoints contained in the RSU and act like a bridge between the data generation layer contained in the vehicle sensor probes and the cloud. Data is exchanged using V2V and V2I communication which use long and short range wireless communication technologies such as ZigBee, Wi-Fi, Dedicated Short Range Communication (DSRC) and Wireless Access for Vehicular Environments (WAVE) BIB010 , BIB008 . Data flow in the entire environment is real time or event driven which influences the availability of resources and services in the system BIB003 . A publish/subscribe based model using Fog Computing for IoV systems is proposed in BIB004 . Fog nodes collaborate using techniques that include cluster based on the homogeneity of the nodes, master-slave and peer to peer (P2P) BIB003 . The Fog nodes process the data sent by the vehicles to make local area based decisions and report processed data to cloud servers. BIB006 . The cloud based servers perform computationally intensive operations on the information sent by the RSUs to make holistic decisions such as managing and controlling of road infrastructure for a large area and provide centralized control from a remote location BIB006 . The architecture used in Fog Computing for vehicular networks is summarized in Fig 3.   Fig. 3 . Three layer architecture used in Fog Computing for vehicular networks (From BIB006 ). In BIB005 , a traffic management system using Fog Computing principles is developed where the entire city is divided into regions and independent RSU units with storage, processing and wireless communication capabilities functioning as Fog nodes are responsible for detecting and controlling congestion in its area of interest in the region. The size of the area of interest is limited by the range of the radio in the RSU and vehicles exchange sensory information with the nearest RSU BIB005 . A similar architecture is used in BIB007 for a route management mechanism based traffic control system.