Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> B. Estimation using V2V Communication <s> Vehicular ad hoc networks have several characteristics that distinguish them from other ad hoc networks. Among those is the rapid change in topology due to traffic jams, which also disturbs the homogeneous distribution of vehicles on the road. For this reason, a dynamic transmission range is more effective in maintaining the connectivity while minimizing the adverse effects of a high transmission power. This paper proposes a scheme that allows vehicles to estimate the local density and distinguish between the free-flow and the congested traffic phases. The density estimate is used to develop a dynamic transmission-range-assignment (DTRA) algorithm that sets a vehicle transmission range dynamically according to the local traffic conditions. Simulations of several road configurations validate the quality of the local density estimation and show that the DTRA algorithm is successful in maintaining the connectivity in highly dynamic networks. <s> BIB001 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> B. Estimation using V2V Communication <s> Vehicular traffic congestion is a well-known economic and social problem generating significant costs and safety challenges, and increasing pollution in the cities. Current intelligent transport systems and vehicular networking technologies rely heavily on the supporting network infrastructure which is still not widely available. This paper contributes towards the development of distributed and cooperative vehicular traffic congestion detection by proposing a new vehicle-to-vehicle (V2V) congestion detection algorithm based on the IEEE 802.11p standard. The new algorithm allows vehicles to be self-aware of the traffic in the street, performing congestion detection based on speed monitoring and cooperation with the surrounding vehicles. Cooperation is achieved using adaptive single-hop broadcasting which depends on the level of congestion. The paper presents the congestion detection algorithm and the cooperative communication in detail, and presents performance evaluation using large-scale simulation in Veins framework based on OMNeT++ network simulator and SUMO vehicular mobility simulator. Results show that precise congestion detection and quantification can be achieved using a significantly decreased number of exchanged packets. <s> BIB002 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> B. Estimation using V2V Communication <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> BIB003
Neighbor discovery based approach is used in V2V based traffic density estimation technique to mutually exchange sensory information between peers and identify local traffic densities  . This limits the coverage area of V2V based traffic density estimation technique to one-hop neighbor BIB003 . In  , a three phase approach is developed for estimating traffic density by using V2V communication and one-hop neighbor discovery. The algorithm computes the local traffic density estimate by using one-hop density and average speed of the neighbors which is propagated to the following vehicles in the traffic stream through one-hop broadcasting. In BIB002 , a similar approach is adopted with adaptive broadcasting. The broadcasting event only occurs if the traffic density estimate of the vehicles is higher than its peer which optimizes the number of packets being exchanged over the V2V communication channel. In BIB001 , traffic density estimation is based on stopping times of vehicles and mobility patterns. The local traffic density is computed by taking the relationship between the number of neighboring vehicles and distance in the front and rear of the vehicle. Accuracy of the estimation by this algorithm is heavily dependent on determining the distance on the front and rear ends of the vehicles.