With the emergence of self-driving technology and the ever-increasing demand of bandwidth-hungry applications, providing the required latency, security and computational capabilities is becoming a challenging task. Although being evolving, traditional vehicular radio access technologies, namely WLAN/IEEE 802.11p and cellular networks cannot meet all the requirements of future Cooperative, Connected and Automated Mobility (CCAM). In addition, current vehicular architectures are not sufficiently flexible to support the highly heterogeneous landscape of emerging communication technologies, such as mmWave, Cellular Vehicle-to-Everything (C-V2X), and Visible Light Communication (VLC). To this aim, Multi-access Edge Computing (MEC) has been recently proposed to enhance the quality of passengers experience in delay-sensitive applications. In this paper, we discuss the in-premises features of MEC and the need of supporting technologies, such as Software Defined Networking (SDN) and Network Function Virtualization (NFV), to fulfil the requirements in terms of responsiveness, reliability and resiliency. The latter is of paramount importance for automated services, which are supposed to be always-on and always-available. We outline possible solutions for mobilityaware computation offloading, dynamic spectrum sharing, and interference mitigation. Also, by revealing MEC-inherent security vulnerabilities, we argue for the need of adequate security and privacy-preserving schemes in MEC-enabled vehicular architectures.
Beaconing is a basic communication process taking place in Vehicular Ad Hoc Networks (VANETs) to achieve cooperative awareness among vehicles on the road. It is actually a paradigm of information spreading among peer-agents, where each node of a networks sends periodically broadcast messages containing information collected by the node itself. A trade-off arises between the update frequency of the broadcast information and the congestion induced in the wireless shared channel used to send the messages, which is based on the IEEE 802.11p standard in case of a VANET. For periodic updates, the primary metric is the Age-of-Information (AoI), i.e., the age of the latest update received by neighboring nodes. We define an analytical model to evaluate the AoI of a VANET, given the connectivity graph of the vehicles. Analytical results are compared to simulation to assess the accuracy of the model. The model provides a handy tool to optimize the AoI trade-off.
Vehicular traffic monitoring is a major enabler for a whole range of Intelligent Transportation System services. Real time, high spatial and temporal resolution vehicular traffic monitoring is becoming a reality thanks to the variety of communication platforms that are being deployed. Dedicated Short Range Communications (DSRC) and cellular communications like Long Term Evolution (LTE) are the major technologies. The former is specifically tailored for Vehicular Ad-hoc Network, the second one is pervasive. We propose a fully distributed Floating Car Data (FCD) collection protocol that exploits the heterogeneous network provided by DSRC and LTE. The proposed approach adapts automatically to the penetration degree of DSRC, achieving the maximum possible LTE offloading, given the VANET connectivity achieved via DSRC. Extensive simulations in real urban scenarios are used to evaluate the protocol performance and LTE offloading, as compared to baseline and literature approaches
Data dissemination and data collection to/from vehicles traveling on city roads are key features to fully enable the advent of Intelligent Transport Systems and Autonomous vehicles. Both Road Side Units and On Board Units need to disseminate different kind of data to vehicles or to collect data sensed by the vehicles themselves and transfer them to road monitoring and control centers. In this work we propose a protocol, named DISCOVER, that disseminates and collects the data of interest in a quite large city area efficiently and timely by using a single network structure, i.e., a multi-hop backbone made up only of vehicles nodes. DISCOVER is distributed and adaptive to the different traffic conditions, i.e., to the different levels of vehicular traffic density. Several numerical results show that it attains very good performance in different type of city maps (New York, Paris, Madrid and Rome) when compared with baseline approaches as well as when compared with a theoretical bound
Periodical collection of data from vehicles inside a target area is of interest for many applications in the context of Intelligent Transportation Systems (ITS). Long Term Evolution (LTE) has been identified as a good candidate technology for supporting such type of applications — particularly for the non-safety domain. However, a high number of vehicles intermittently reporting their information via LTE can introduce a very high load on the LTE access network. In this context, the use of heterogeneous networking technologies can yield significant offloading of LTE — here, WLAN and Dedicated Short-Range Communication (DSRC) technology can support local data aggregation. In this paper, we propose an on-the-fly distributed clustering algorithm that uses both LTE and DSRC networks in the forwarder selection process. Our results clearly indicate that it is crucial to consider parameters drawn from both networking platforms for selecting the right forwarders. In particular, we show for the first time that relying on the Channel Quality Indicator (CQI) has a substantial impact. We demonstrate that our solution is able to significantly reduce the LTE channel utilization with respect to other state of the art approaches
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