Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> D. Fog Computing (FC) <s> 802.11p, also known as WAVE, is a standard protocol intended for future traffic systems in order to support safety and commercial non-safety applications for vehicular communication. 802.11p is modified from 802.11a, and both are based on OFDM. The main difference between 802.11a and 802.11p is that the latter is proposed to use 10 MHz frequency bandwidth (half of bandwidth of 802.11a) in order to make the signal more robust against fading and increase the tolerance for multipath propagation effects of signals in a vehicular environment. In this paper, we investigate the performance difference between 802.11a and 802.11p for Vehicle-to-Infrastructure communication through real-world experiments. We measure contact duration and losses of 802.11p and 802.11a in both LOS and NLOS environments. In addition, we investigate their throughput with different modulations over various distances between OBU and RSU to evaluate the feasibility of using rate adaptation for non-safety V-to-I applications. <s> BIB001 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> D. Fog Computing (FC) <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> BIB002 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> D. Fog Computing (FC) <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> BIB003 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> D. Fog Computing (FC) <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> BIB004 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> D. Fog Computing (FC) <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> BIB005 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> D. Fog Computing (FC) <s> The increasing average age of the population in most industrialized countries imposes a necessity for developing advanced and practical services using state-of-the-art technologies, dedicated to personal living spaces. In this paper, we introduce a hierarchical distributed approach for home care systems based on a new paradigm known as Internet of Things (IoT). The proposed generic framework is supported by a three-level data management model composed of dew computing, fog computing, and cloud computing for efficient data flow in IoT based home care systems. We examine the proposed model through a real case scenario of an early fire detection system using a distributed fuzzy logic approach. The obtained results prove that such implementation of dew and fog computing provides high accuracy in fire detection IoT systems, while achieving minimum data latency. <s> BIB006 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> D. Fog Computing (FC) <s> Fog computing (FC) and Internet of Everything (IoE) are two emerging technological paradigms that, to date, have been considered standing-alone. However, because of their complementary features, we expect that their integration can foster a number of computing and network-intensive pervasive applications under the incoming realm of the future Internet. Motivated by this consideration, the goal of this position paper is fivefold. First, we review the technological attributes and platforms proposed in the current literature for the standing-alone FC and IoE paradigms. Second, by leveraging some use cases as illustrative examples, we point out that the integration of the FC and IoE paradigms may give rise to opportunities for new applications in the realms of the IoE, Smart City, Industry 4.0, and Big Data Streaming, while introducing new open issues. Third, we propose a novel technological paradigm, the Fog of Everything (FoE) paradigm, that integrates FC and IoE and then we detail the main building blocks and services of the corresponding technological platform and protocol stack. Fourth, as a proof-of-concept, we present the simulated energy-delay performance of a small-scale FoE prototype, namely, the V-FoE prototype. Afterward, we compare the obtained performance with the corresponding one of a benchmark technological platform, e.g., the V-D2D one. It exploits only device-to-device links to establish inter-thing “ad hoc” communication. Last, we point out the position of the proposed FoE paradigm over a spectrum of seemingly related recent research projects. <s> BIB007
Edge sensory devices lack adequate storage, bandwidth, computational capability and are powered by a battery based power source. IoT based systems depend heavily on powerful server infrastructure contained in the cloud to provide large elastic resources BIB002 . The solution introduces large round trip times for communication between the sensory nodes and the BIB003 , BIB002 . The centralized approach is unsuitable for systems with large geo distribution of sensory nodes and high mobility BIB004 . Thus, a scalable solution is required that does not negatively influence the responsiveness and reliability of the sensory nodes that operate on the network end points. Fog computing based approach is evolving with the increase in the number of IoT devices and services and is used in conjunction with traditional cloud computing which was first proposed by Cisco in 2012 BIB004 . Fog computing is a distributed computing based approach where the Fog layer is an intermediate layer in the three-layer framework. The first layer in a Fog system contains edge devices equipped with sensors and raw processing capability of the collected data BIB005 , BIB006 . The Fog computing level nodes process and store data to make neighborhood level decisions and the cloud computing level is used for wide area level advanced decision making BIB006 ,  . The Fog layer is used in-between the IoT sensors and cloud servers which functions as a local cloud for nodes at the edge of the network BIB002 , BIB004 . This is done to bring elastic resources such as computations and storage provided by the traditional cloud closer to the edge devices distributed over a large coverage area with mobility support BIB002 . Having a cloud like infrastructure closer to the edge devices helps to minimize network traffic and amount of information being processed on the central cloud is reduced  . Thus, IoT services can be offered to a large number of devices over a wide coverage area with low latency and high bandwidth with high QoS and QoP BIB004 . Nodes at the Fog layer have limited resources and this directly impacts the fault tolerance capability of the Fog layer nodes BIB007 . Additionally short range communication technologies such as Wi-Fi and ultra-wideband (UWB) are used for exchanging data between the Fog Nodes and edge devices which are prone to interference BIB007 . This challenge can be addressed for vehicular applications by using Dedicated Short Range Communication (DSRC) based wireless communication technology which is less prone to interference BIB001 . The decentralized approach of the distributed Fog computing approach makes it suitable for a wide range of applications ranging from healthcare to smart grid, augmented reality and vehicular networks  .Various computing models studied in this section are compared in table I. 