Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> V. DISCUSSION <s> Intelligent Transportation Systems (ITS) have been considered important technologies to mitigate urban traffic congestion. Accurate traffic prediction is one of the critical steps in the operation of an ITS. While techniques for traffic prediction have existed for many years, the research effort has mainly been focused on highway networks. Due to the fundamental difference between the traffic flow pattern on highways and that on city roads, much of the existing models cannot be effectively applied to city traffic prediction. In this paper, we propose two city traffic prediction models using different modeling approaches. Model-1 is based on the traffic flow propagation in the network, while Model-2 is based on the time-varied spare flow capacity on the concerned road link. The proposed models are implemented to predict the traffic volume in Cologne in Germany, and the real data are collected through simulations in the traffic simulator SUMO. The results show that both of the proposed models reduce the prediction error up to 52% and 30% in the best cases compared to the existing Shift Model. In addition, we found that Model-1 is suitable for short prediction interval that is in the same magnitude as the link travel time, while Model-2 demonstrates superiority when the prediction interval is larger than one minute. <s> BIB001 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> V. DISCUSSION <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> BIB002 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> V. DISCUSSION <s> Abstract Radio Frequency Identification (RFID) technology is widely used to achieve indoor object tracking and positioning. Currently, many methods need to deploy a large number of reference tags beforehand and some are limited by antennas’ spacing. Further, the signal propagation along Non-Line of Sight introduces multipath effects which will challenge the accuracy of RFID localization system. In this work, we propose a method based on measured phase to track mobile RFID tags with millimeter level (mm-level) accuracy. We first partition the surveillance region into square grids at mm-level and suppose that there is a virtual tag as the same as the tracked one in each grid. On this basis, for the case where the tags move along a known track with constant speed, we only need to locate the tag's initial position. We leverage phase periodicity to obtain some candidates and then eliminate position ambiguity by double difference true phase. And for the case where the tag's moving track is unknown to the system, we adopt a first-order Taylor series expansion to calculate the relative displacements of the tracked tag and then locate the initial position as the same process as tracking the known trajectory. In our experiment, our solution can achieve a mean error distance of 0.26 cm and 0.55 cm for known and unknown movement tracks respectively. <s> BIB003 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> V. DISCUSSION <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> BIB004 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> V. DISCUSSION <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> V. DISCUSSION <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> BIB006 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> V. DISCUSSION <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> BIB007 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> V. DISCUSSION <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> BIB008 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> V. DISCUSSION <s> This paper proposes a variable structure control approach for vehicles platooning based on a hierarchical fuzzy logic. The leader-follower vehicle dynamics with model uncertainties is discussed from the viewpoint of a consensus problem. A practical two-layer fuzzy control for the platooning is designed by employing two common spacing policies to ensure system robustness in different scenarios. The two policies, i.e., constant distance and constant time headway, utilize the predecessor-successor information flow from the immediate predecessor and follower other than controlled vehicles. The first layer of the fuzzy system combines spacing control with velocity-acceleration control to achieve a rapid tracking for the desired control commands, and the second layer combines the sliding mode control to adaptively compensate for reducing the state errors caused by parameter uncertainties and disturbances. Shift between different controller parameters is based on performance boundaries to guarantee the stability of individual vehicle and platooning for arbitrary initial spacing and velocity errors. These performance boundaries can be determined by using a Lyapunov method with exponential stability. Simulation of a ten-vehicle large platooning with two spacing policies shows that the control performance of the newly proposed method is effective and promising. <s> BIB009
Vehicular congestion detection techniques involve contact based techniques which have an accuracy dependent on maintenance of the sensory infrastructure and non-contact methods. Performance of noncontact based traffic density estimation techniques using surveillance cameras and microphones are influenced by environmental factors and results are not in real-time as they rely on computationally intensive image processing and acoustic processing. It was found that contact based traffic density estimation using embedded loop detectors has an accuracy of 80% and the accuracy obtained with 20% of the traffic stream equipped with connected vehicle technology was 85% BIB004 , BIB001 . IoV based systems using CVT are best suited for vehicular congestion detection applications that are time sensitive and require high accuracy of sensory data. Accuracy levels of traffic density estimation suing V2V and V2I based techniques are in acceptable limits. The accuracy levels vary for different scenarios depending on the topographic features of the area being monitored. The error level in estimation of traffic density of -4.86% is achieved using V2V based technique with an estimation polynomial of order 3 BIB002 . Whereas the error in traffic density estimation by using V2I based methods with a Taylor series density approximation function is 8.34% BIB002 . The coverage area of the more accurate V2V based techniques is narrow which makes it unsuitable for congestion handling. Whereas V2I based techniques have wide coverage areas and are suitable for congestion handling  . Higher accuracy levels of 98 % and wide coverage areas can be achieved by combining V2V and V2I based traffic density estimation techniques BIB002 . Various computing paradigms exist that can be deployed in the IoV system for congestion detection using CVT. The requirements for IoV systems for congestion detection where large amounts of data are collected for processing include low response time, low communication latency, support for mobility and location awareness. The above requirements are fulfilled by VCC and VFC based approaches. However, VFC based approach uses lightweight algorithms and optimized communication with the servers to improve response time of the system and support a larger number of devices. A three tier Fog Computing based approach was used for a smart home application in BIB005 and it was found that network traffic can be reduced by 95%. by using Fog Computing principles. Additionally communication latencies in the Fog Computing based systems is limited to a few tens of milliseconds BIB006 . This makes vehicular fog computing most suitable for deployment in traffic density estimation applications for congestion detection where data is required to be collected periodically from a large number of vehicular sensory probes to offer a wider coverage area. Extremely low communication latencies with support for mobility and distributed architecture offered by Fog Computing based approach can be beneficial for applications that require tracking and controlling of vehicles with high accuracy. Deployment of Fog Computing based principles in systems such as vehicular platooning and autonomous control of intelligent vehicles can be investigated in future works BIB008 , BIB003 , BIB009 . Research on security risks and mitigation of threats in vehicular fog computing is at its initial stages which makes security a challenge as most security mechanisms can only handle threats posed by passive attacks BIB007 .