Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> II. VEHICULAR COMPUTING MODELS <s> Mobile Cloud Computing is a new field of research that aims to study mobile agents (people, vehicles, robots) as they interact and collaborate to sense the environment, process the data, propagate the results and more generally share resources. Mobile agents collectively operate as Mobile Clouds that enable environment modeling, content discovery, data collection and dissemination and other mobile applications in a way not possible, or not efficient, with conventional Internet Cloud models and mobile computing approaches. In this paper, we discuss design principles and research issues in mobile cloud computing. We then focus on the Mobile Vehicular Cloud and review cloud applications ranging from urban sensing to intelligent transportation. <s> BIB001 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> II. VEHICULAR COMPUTING MODELS <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> BIB002 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> II. VEHICULAR COMPUTING MODELS <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> BIB003 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> II. VEHICULAR COMPUTING MODELS <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> BIB004 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> II. VEHICULAR COMPUTING MODELS <s> As a meaningful and typical application scenario of Internet of Things (IoT), Internet of Vehicles (IoV) has attracted a lot of attentions to solve the increasingly severe problem of traffic congestion and safety issues in smart city. Information-centric networks (ICN) is a main stream of next generation network because of its content based forwarding strategy and in-network caching properties. Many existing works have been done to introduce ICN in IoV because of IP-based network architecture's maladjustment of such extremely mobile and dynamic IoV environment. In contrast, ICN is able to sustain packet delivery in unreliable and extreme environment. However, the frequent mobility of the vehicles will consume ICN's network resources to incessantly update the Forward Information Base (FIB) which will further affect the aggregation processing. For example, geographic location based name schema aggregation will be affected dramatically by mobility problem. On the other hand, fog computing is an edge computing technology usually integrated with IoT by bringing computation and storage capacity near the underlying networks to provide low-latency and time sensitive services. In this paper, we integrate fog computing into information-centric IoV to provide mobility support by allocating different schema taking account of the data characteristic (e.g., user-shareable data, communication data). Moreover, we use the computation, storage and location-aware capabilities of fog to design a mobility support mechanism for data exchange and communication considering the feature of IoV service (e.g., alarm danger in local, updating traffic information, V2V communication etc.). We evaluate related performances of the proposed mechanism in high mobility environment, compared with original information-centric IoV. The result shows the advantages of a fog computing based information-centric IoV. <s> BIB005 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> II. VEHICULAR COMPUTING MODELS <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> BIB006 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> II. VEHICULAR COMPUTING MODELS <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> BIB007 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> II. VEHICULAR COMPUTING MODELS <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> BIB008 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> II. VEHICULAR COMPUTING MODELS <s> Cloud Computing in VANETs (CC-V) has been investigated into two major themes of research including Vehicular Cloud Computing (VCC) and Vehicle using Cloud (VuC). VCC is the realization of autonomous cloud among vehicles to share their abundant resources. VuC is the efficient usage of conventional cloud by on-road vehicles via a reliable Internet connection. Recently, number of advancements have been made to address the issues and challenges in VCC and VuC. This paper qualitatively reviews CC-V with the emphasis on layered architecture, network component, taxonomy, and future challenges. Specifically, a four-layered architecture for CC-V is proposed including perception, co-ordination, artificial intelligence and smart application layers. Three network component of CC-V namely, vehicle, connection and computation are explored with their cooperative roles. A taxonomy for CC-V is presented considering major themes of research in the area including design of architecture, data dissemination, security, and applications. Related literature on each theme are critically investigated with comparative assessment of recent advances. Finally, some open research challenges are identified as future issues. The challenges are the outcome of the critical and qualitative assessment of literature on CC-V. <s> BIB009 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> II. VEHICULAR COMPUTING MODELS <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> BIB010
The wireless sensor network in an IoV system acquire physical world data from the underutilized onboard sensors of the vehicles which function as mobile probes for applications such as Advanced Vehicle Control and Safety Systems (AVCSS), Advanced Traffic Management Systems (ATMS), Advanced Traveler Information Systems (ATIS), Advanced Public Transportation. Systems (APTS) ,Commercial Vehicle Operation Systems (CVOS), urban surveillance and environmental monitoring BIB004 , BIB002 , BIB005 , BIB001 , BIB006 , BIB003 . The WSN shares storage, computational and communication resources to maximize utilization and effectively perform the processing of the large voluminous sensory information for the desired application BIB009 . Components of the vehicular WSN are summarized in Vehicles which function as the mobile probes contain sensors in a package referred as the onboard unit (OBU) BIB010 . The OBU contains the communication apparatus. Communication interfaces include inter vehicle communication channel for V2V communication and V2I communication for connectivity between the vehicle and RSU with remote handler BIB007 . The sensors included in the OBU include safety related distance and night vision sensors, kinetics related sensors for tracking motion related parameters such as speed and acceleration and positioning apparatus such as GPS for monitoring applications BIB002 . The OBU also contains storage and computing resources to process sensory information locally BIB004 . The WSN network architecture aims to optimally utilize the communication and computational resources by collaborative sharing and the cloud computing services types realized in vehicular networks include Network as a service (NaaS), Cooperation as a service (Coaas), Computation as a service (COaaS), storage as a service (STaaS), sensing as a service (SEaaS) BIB010 , BIB009 . NaaS is used to facilitate communication links between the nodes and the remote handler, SEaaS is used for real time monitoring of physical world related parameters, COaaS and STaaS is used for optimally exploiting the underutilized computational capability of the vehicular probes and CaaS is used for mutual collaboration between vehicular nodes BIB010 , BIB009 . Cloud based services for multiple applications in the IoV based system are handled through web sockets, Restful and Java APIs and protocols that include Constrained Application Protocol (CoAP), Message Queue Telemetry Transport (MQTT) and Advanced Message Queuing protocol (AMQP) BIB010 , BIB008 . Architecture of the vehicular cloud in IoV based approach is summarized in Fig 2 . Multiple computing paradigms exist to realize vehicular sensor networks. This includes vehicular cloud computing (VCC), vehicles using cloud (VuC), mobile cloud computing (MCC) and vehicular Fog Computing (VFC). In the following, computing paradigms are studied for application in vehicular networks for IoV based solutions and a comparison is summarized in table I.