Abstract

This paper proposes VehILux, a new vehicular mobility model based on real traffic counting data. It relies on two freely available sources of real information for the country of Luxembourg. The first source is traffic data collected by counting devices located on the Luxembourgian road network, while the second is geographical information about different types of areas: residential, industrial, commercial and other services. VehILux models vehicles commuting around the city of Luxembourg by considering two types of traffic, outer traffic with vehicles entering in the defined geographical area and inner traffic starting from residential zones located inside the geographical area. One part of the collected traffic data is used as input traffic, while another part is used to control the produced traffic and to fine-tune the model. VehILux is coupled with the microscopic road traffic simulator SUMO to produce realistic vehicular traces.

Abstract

Wireless vehicular ad-hoc networks comprised solely of city taxis are investigated for their ability to deliver data across an urban environment. Openly available taxi trace datasets for Rome (Italy) and San Francisco (USA) are combined with respective building footprint and road network topology data from OpenStreetMap, to generate a realistic systems level model of a taxi V2V network. Analysis of LOS and NOLOS constraints on wireless transmission range suggests a minimum threshold of 50m is applicable to ensure LOS in over 90% of cases. Variations in taxi location sampling frequency and filtering techniques for the taxi trace datasets are also investigated. Overall vehicular network performance is computed for an all-to-one transmission scenario for both cities with varying taxi fleet size. Results suggest a non-linear relationship between increases in taxi fleet sizes and the reduction of end-to-end delay; doubling taxi fleet size (using a randomised data folding technique) reduces end-to-end delay by a factor of 0.6–0.7. However, doubling the fleet does not increase the fraction of delivered source messages, which saturates at 0.67–0.71 in most simulations. Finally it appears that taxi networks for delivering messages across urban environments are limited more by their routing than by the number of possible V2V exchanges. In a simulated one-to-all continuous V2V broadcast scenario, over 90% of the taxis within the fleet receive the source message within one hour of the original taxi passing the source node.

Abstract

International audience; As mobile applications deliver increasingly complex functionalities, the demands for even more intensive computation would quickly transcend energy capability of mobile devices. On one hand and in an attempt to address such issues, fog computing paradigm is introduced to mitigate the limited energy and computation resources available within constrained mobile devices, by moving computation resources closer to their users at the edge of the access network. On another hand, most of electric vehicles (EVs), with increasing computation, storage and energy capabilities, spend more than 90% of time on parking lots. In this paper, we conceive the basic idea of using the underutilized computation resources of parked EVs as fog nodes in order to provide on-demand computation at the vicinity of the access network. The proposed Vehicular Fog Computing (VFC) architecture aggregates the abundant unused resources of parked vehicles, and uses it to serve mobile users' demands. The resource allocation problem is formulated as a Markov Decision Process (MDP) and dynamic programming is used to solve the underling decision problem. Extensive simulation results show the effectiveness of the proposed approach by improving the global reward value by 51% and scoring an energy gain of 66% compared to two other models.

Abstract

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Enhanced position accuracy is key for modern navigation systems, location based services and applications based on Inter-Vehicle Communication (IVC). Position data are the foundation for deriving vehicle trajectories used for assessing a situation's criticality in vehicle safety. Thus, especially Advanced Driver Assistance Systems (ADASs) and integral safety applications bene t from nearby vehicles spreading their positions periodically with high accuracy. Positioning based on Global Navigation Satellite System (GNSS) measurements can be enhanced by established Cooperative Positioning (CP) methods like Real-Time Kinematic (RTK) and Di fferential GNSS (DGNSS). Conventional CP relies on positioning correction data from a third party, whereas this paper introduces a self-su fficient CP system based on Precise Point Positioning (PPP) and Vehicular Ad-Hoc Network (VANET) technology requiring no infrastructure. Furthermore, the data dissemination process and achievable coverage are analysed by a simulation study for a rural area in Bavaria, Germany. For this purpose, the simulation employs the European IVC protocol stack ITS-G5. While the general feasibility of this CP approach could be assured, some remaining issues regarding employed network protocols were discovered as well.