Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> C. Estimation using V2I Communication <s> In Vehicular Networks, communication success usually depends on the density of vehicles, since a higher density allows having shorter and more reliable wireless links. Thus, knowing the density of vehicles in a vehicular communications environment is important, as better opportunities for wireless communication can show up. However, vehicle density is highly variable in time and space. This paper deals with the importance of predicting the density of vehicles in vehicular environments to take decisions for enhancing the dissemination of warning messages between vehicles. We propose a novel mechanism to estimate the vehicular density in urban environments. Our mechanism uses as input parameters the number of beacons received per vehicle, and the topological characteristics of the environment where the vehicles are located. Simulation results indicate that, unlike previous proposals solely based on the number of beacons received, our approach is able to accurately estimate the vehicular density, and therefore it could support more efficient dissemination protocols for vehicular environments, as well as improve previously proposed schemes. <s> BIB001 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> C. Estimation using V2I Communication <s> The number of vehicles in our roads is drastically increasing, especially in developing countries. In addition, these vehicles tend to be concentrated in urban areas which present a large population. Since traffic jams have important and mostly negative consequences, such as increasing travel time, fuel consumption, and air pollution, governments are making efforts to alleviate the increasing traffic pressure, being vehicular density one of the main metrics used for assessing the road traffic conditions. However, vehicle density is highly variable in time and space, making it difficult to be estimated accurately. In this paper, we present a solution to estimate the density of vehicles in urban scenarios. Our proposal, that has been specially designed for vehicular networks, allows intelligent transportation systems to continuously estimate vehicular density by accounting for the number of beacons received per road side unit (RSU), and also considering the roadmap topology where the RSUs are located. Using V2I communications, we are able to estimate the traffic density in a certain area, which represents a key parameter to perform efficient traffic redirection, thereby reducing the vehicles' travel time and avoiding traffic jams. Simulation results reveal that, unlike previous proposals, our approach accurately estimates the vehicular density in all the scenarios, presenting an average relative error is of only 3.04 %. <s> BIB002 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> C. Estimation using V2I Communication <s> In urban areas, traffic congestion has become a serious phenomenon resulting in a great waste of time and fuel. How to detect road condition and disseminate traffic information effectively has become a significant challenge. Recently, the technology of vehicular ad-hoc networks (VANETs) has been utilized to deal with this problem, and become an important component of the intelligent transportation system (ITS). As two basic paradigms, both vehicle-to-vehicle (V2V) and vehicle to-RSU (V2R) communications have their own advantages and disadvantages. In this paper, we propose two novel approaches based on V2V and V2R communications respectively to explore a more suitable candidate for the traffic management. The travel time of vehicles is selected as the common indicator to evaluate the traffic state of route. On the basis of it, a routing algorithm is designed to provide a reasonable route for drivers. Finally, our two approaches are validated in a realistic scenario by Veins. The simulation results show that the performance of V2R scheme is restricted by the limited transmission range of RSU, while the V2V scheme is more appropriate for traffic information dissemination if the broadcast storm is well solved. <s> BIB003
Traffic density estimation techniques using V2I based methods collect periodic sensory data by using V2I communication interfaces from multiple probes which are the OBUs mounded on vehicles. Centralized processing is performed on the collected data to estimate the traffic density of the road segment. This gives V2I based techniques much larger coverage area when compared to V2V based techniques  . A traffic density function is used in V2I based methods. The function is a polynomial where the coefficients used are derived from topographic feature of the city where the system is to be deployed. The topographical features of the city that are studied to compute the coefficients of the polynomial include average lanes/street, street lengths and street/junction ratio BIB002 , BIB001 . Vehicles send beacon messages periodically to the RSUs which are handled using queues BIB003 . The average beacons received from every vehicle is used by the traffic density estimation polynomial BIB002 . Accuracy of V2I based estimation technique is improved by including additional information in the beacon messages such as speed change and lane change events  .