Vehicular Knowledge Networking, when integrated withNamed Data Networking (NDN), unfold an emerging paradigm to deliverKnowledge—instead of raw data to the consumer. The mechanism improves the learning process and improves the decision-making capability of autonomous vehicles. However, in constantly evolving environment, adhering to the vanilla NDN forwarding mechanism fails to retain a reliable communication path between communicating vehicles. This results in frequent path disruptions, Knowledge retransmissions, resources utilization, and intolerable delays, ultimately hindering the timely delivery of Knowledge. To address these challenges, this article envisions MAKS—an efficientmobility-aware Knowledge sharingframework for next-generation Internet of autonomous Vehicles. MAKS incorporates vehicular dynamics and trajectory information to develop aMobility-aware Forwarding Information Base (MaFIB), which ensures stable reverse paths and reliable Knowledge dissemination under varying network conditions. Additionally, MAKS presents efficient path recovery mechanisms that enables vehicles to carry-on Knowledge sharing in the event of potential forwarder failures, thus maintaining service continuity. Simulation results demonstrate that MAKS achieves an impressive Knowledge delivery ratio exceeding 90%, reduces path partitioning by more than 35%, and decreases the redundant retransmissions by approximately 40% compared to benchmark scheme.

Vehicular Knowledge Networking (VKN) is a paradigm where vehicles exchange knowledge instead of data. Named Data Networking (NDN) provides a resilient architecture for data sharing between vehicles without requiring details of the hosting vehicle. However, NDN as well as state-of-art NDN simulation environments lack knowledge representation and reasoning (KRR) support to process, understand and optimize knowledge exchange between vehicles. In this paper, we present an open-source co-simulation framework tailored to VKN, integrating NDN, traffic, perception, and control simulators with an AI-as-a-Service (AIaaS) platform for KRR. This paper describes the APIs between various simulators and tools and demonstrates on a proof-of-concept the benefits of KRR for NDN by dividing NDN response time by a factor of five, while reducing NDN storage size by a factor of two.

Effective vehicular platooning requires the Ultra-Reliable, Low-Latency Communication (URLLC) capabilities provided by 5G New Radio (NR) sidelink (SL). These advanced communication features also impose constraints on platoon size and organizational structure. This paper introduces a framework that integrates platoon management with 5G Proximity Services (ProSe), defined by the 3rd Generation Partnership Project (3GPP) Release.18, to accommodate these constraints. We propose a 5G ProSe-based distributed service for platoon management, incorporating join and leave functions for platoons. The system features a combined slice design supporting both general vehicular and platooning communication, along with admission control tailored to URLLC SL slice requirements. Incorporating both a deterministic scheduler and a high 5G NR numerology, our described 5G ProSe-based platoon service supports strict URLLC requirements.

With decentralized Machine Learning (ML) strategies and modern edge Tensor Processing Unit (TPUs), smart devices are no longer only consumers but also producers of Artificial Intelligence (AI), transforming an Internet of Things (IoT) into a global and decentralized Artificial Intelligence of Things (AIoT). With the availability of a large amount of AI actors comes not only the challenge to discover and network with them, but also the potential to use their AI capabilities as a service. However, the heterogeneity of the AI actors, their AI capabilities, AI contextual environment, mobility or even the AI characteristic available or sought requires not only a robust IoT architecture but also flexible AI semantics. In this paper, we present an AI-as-a-Service platform assisting AI consumers to identify existing AI tailored to their needs among the AIoT. We describe the architecture, the APIs, the message flows and AI semantics to identify the most appropriate AI workers when and where needed to efficiently generate AI models from distributed vehicles. As a proof-of-concept, we select an application scenario demonstrating the trainability/changeability of AI models between vehicles according to their context using the CARLA driving simulator.

Platooning is a popular vehicular application for autonomous driving on which the Platoon Leader (PL) manages all maneuvers using context information from Vehicle-to-Vehicle (V2V) messages. However, redundant context information from nearby vehicles in the platoon can increase computational costs for the PL. To solve this issue, vehicular micro-clouds can be formed to enable collective data processing and aggregation, thus reducing the PL’s perception workload. The proposed solution, called Platoon Local Dynamic Map (P-LDM), creates a single database of context information, distributing the data aggregation load among all members of the platoon. Simulation results evaluate the effectiveness of the proposed solution and compare it to typical Cooperative Perception mechanisms.

It is estimated that the data volume between connected vehicles and edge/cloud server(s) will be about 100 petabytes per month by 2025. The networking framework we have, on the other hand, is the existing cellular network in which the most connected vehicles function today. However, such a network suffers from several issues and may not work under this predicted data demand. To address such a dilemma, a new paradigm, Vehicular Knowledge Networking (VKN), is recently introduced. In VKN, the data is transformed into knowledge and it is distributed with various lifetimes/relevance. To benefit from the knowledge, on the other hand, it should be placed intelligently such that a high number of vehicles can access and consume it. In this paper, we tackle this issue and propose mobility-aware smart knowledge placement. In the proposed method, vehicle mobility is analyzed to measure the centrality degree of a region. The computed centrality degrees are then further analyzed to identify the most central zones. The knowledge is placed on these zones to increase availability. We demonstrate the benefits of the proposed method through a simulation. Our preliminary result has shown that the mobility-aware smart knowledge placement makes knowledge accessible from vehicles over short range communication. Through such short-range availability of knowledge, vehicles can use the free spectrum to download it which decreases the cellular communication cost significantly.

Delivering ultra-reliable low-latency communication (URLLC) services on 5G New Radio (NR) is critical for extended Vehicle-to-everything (V2X) applications. Early studies focused either on infrastructure V2X communications, or exclusively evaluating performance on mm Wave band, but never so far for V2X Sidelink communications. In this paper, we evaluate the feasibility of achieving URLLC critical requirements for 5G NR V2X Sidelink communication at the V2X 5.9 GHz band. We propose a higher 5G-NR numerology completed by a deterministic scheduler and propose an analytical V2X SL admission control algorithm to reach tight URLLC requirements.

Leveraging Modulation and Coding Schemes (MCS) in 5G New Radio (NR) Sidelink represents one key strategy to provide sufficient capacity required by future 5G for Vehicle-to-Everything (V2X) services for intelligent vehicles. Early studies either directly adopt the previously optimised QPSK 1/2 by 802.11p/C-V2X or suggest an optimal MCS value under a particular context. In this paper, we identify a MCS value optimal under any context, by evaluating the impact of MCS on V2X broadcast communication considering multiple varying parameters (e.g. variable packet size, transmit rate or density) representative of different 5G V2X services.

Wireless congestion control and resource allocation for 802.11p based V2X safety communication have been widely investigated for a single Cooperative Awareness service, considering homogeneous resource requirement per vehicle. Future cooperative connected vehicles, will have heterogeneous capabilities and communication needs, which existing congestion control mechanisms have not fully addressed. In this paper, we analyze issues with the channel congestion control protocol standardized in Europe by ETSI, regarding distributed resource allocation for heterogeneous number of services and message types per vehicle. We present a cooperative congestion control mechanism to orchestrate channel resource among a mixed distribution of vehicles with diverse resource requirements under channel congestion. Simulation based evaluation using standardized safety messages show the application performance improvement rendered by our proposed mechanism, compared to the standardized protocol.

Vehicles are expected to generate and consume an increasing amount of data, but how to perform risk reasoning over relevant data is still not yet solved. Location, time of day and driver behavior change the risk dynamically and make risk assessment challenging. This paper introduces a new paradigm, transferring information from raw sensed data to knowledge and explores the knowledge of risk reasoning through vehicular maneuver conflicts. In particular, we conduct a simulation study to analyze the driving data and extract the knowledge of risky road users and risky locations. We use knowledge to facilitate reduced volume and share it through a Vehicular Knowledge Network (VKN) for better traffic planning and safer driving.

ETSI Decentralized Congestion Control (DCC) for vehicular communication is an essential mechanism for limiting wireless channel congestion and resource allocation of ad-hoc V2X communications. Standardized channel congestion control protocols have been designed considering mostly a single message for cooperative awareness such as CAM/BSM, while future automated vehicles will exchange additional messages, including sensor data, control information, HD-maps etc.In this paper we evaluate and improve the performance of the state-machine based DCC standardized in Europe by ETSI. We highlight the channel capacity under-utilization and the communication quality degradation, and propose three design improvements to enhance its performance. Simulation based evaluation, considering multiple standardized safety messages on a single channel, proves the performance improvement due to our proposed modifications, almost doubling the reception throughput for dense V2X communication scenarios.

The fifth generation (5G) wireless communication networks are expected to support communications with high mobility, e.g., with a speed up to 500 km/h. Hence 5G communications will have numerous applications in high mobility scenarios, such as high speed railways (HSRs), vehicular ad hoc networks, and unmanned aerial vehicles (UAVs) communications [1]-[3]. The 5G systems will provide advanced communication platforms enabling reliable transmission for the Wireless Train Backbone (WLTB) or Wireless Train Control & Management System (WTCMS) [4], [5]. They will also enable new services or enhancements for vehicular communications in Intelligent Transportation System (ITS) [6]-[9]. The coordination and swarming control for UAVs will also benefit from 5G capabilities, as UAV-based 5G infrastructure modeling and improvement have begun receiving attention [10]. In general, high mobility communication is not only about how large is the maximum speed, it is more about the challenges caused by mobility. In high mobility scenarios, a wireless channel is rapidly time varying, Doppler shifts and spreads can be much larger than those in cellular communications, and if modeled statistically, the channel will be non-wide-sense stationary (non-WSS) over a short time period. In addition, network topology can change quickly, and switching among base stations (BSs) and/or peer nodes can be more frequent, not forgetting 5G challenges in cross-border mobility [11].

Transmit Rate Control (TRC) for V2X networks is critical for distributed channel resource allocation, limiting the channel usage per vehicle while preventing channel saturation. Similarly, efficiently distributing the limited transmit opportunities among multiple services of a vehicle is necessary, but has not been sufficiently studied. In this paper, we present a multi-V2X service resource orchestrator composed of two complementary mechanisms: (i) a multi-factor prioritization function, (ii) a budgetary scheduler allowing smooth resource earning/spending. Simulation-based evaluations showed improved access time for various V2X services under restricted resources and enhanced control on resource balancing between V2X services.

Autonomous systems are an important part of todays and future solutions for the automotive and industrial sector. The research and development activities to enable high/full automated driving and industry 4.0 have to deal with a lot of new requirements (e.g. fail operational, cyber security), technologies (connectivity over 5G, neuronal networks, future computing platforms) and topics (data analytics, artificial intelligence). Furthermore processes, methods und tools lack behind and need to speed up to cope with all the consequences in validation and verification. The short paper will give an overview over these challenges and the actual state of research and the development in the field of digital autonomous systems.

At early market penetration, automated vehicles will share the road with legacy vehicles. For a safe transportation system, automated vehicle controllers therefore need to estimate the behavior of the legacy vehicles. However, mismatches between the estimated and real human behaviors can lead to inefficient control inputs, and even collisions in the worst case. In this paper, we propose a framework for evaluating the impact of model mismatch by interfacing a controller under test with a driving simulator. As a proof- of-concept, an algorithm based on Model Predictive Control (MPC) is evaluated in a braking scenario. We show how model mismatch between estimated and real human behavior can lead to a decrease in avoided collisions by almost 46%, and an increase in discomfort by almost 91%. Model mismatch is therefore non-negligible and the proposed framework is a unique method to evaluate them.

Decentralized Congestion Control (DCC) for 802.11p based inter-vehicular communication (V2X) is a critical mechanism for distributed wireless resource allocation of future connected intelligent vehicles. Studies so far mostly focused on optimizing resources for a single Cooperative Awareness service, whereas future connected intelligent vehicles will be based on multiple heterogeneous new V2X services. In this paper, we present a Facilities-layer DCC, currently being standardized in Europe, capable of handling heterogeneous V2X services and evaluate its integration impact with legacy DCC mechanisms. We first emphasize significant wireless resource under-utilizations and application performance degradations stemming from conflicting decisions between legacy and Facilities-layer DCC. We then show the capability of the DCC mechanism purely based at service layer and illustrate its flexibility for agile wireless resource allocations between V2X services for intelligent vehicles.

Decentralized Congestion Control (DCC) for 802.11p based V2X communication has been widely investigated for a single Cooperative Awareness service. As future connected intelligent vehicles will be based on multiple V2X services with a variety of application traffic pattern, DCC mechanisms should be capable of limiting channel congestion while satisfying the channel resource requirement of a range of heterogeneous applications in each node. In this paper, we analyze the application layer rate control approach of Facilities DCC, currently being standardized in Europe, and propose a flexible packet generation control, which gives each node more flexibility and agility for resource management among its applications. Our simulation based results show that a flexible allocation can more rapidly serve application packet generation requests while keeping the channel load within the desired level even with high node density. More importantly, it solves the starvation issue, commonly associated with the strict priority based traffic shaping approach of DCC.

A centralized controller computes controls and transmits them to automated vehicles. In case of a communication failure, control values will be missing and vehicles might take incorrect driving decisions. Although modern controllers may resist spurious communication errors, burst errors are expected to be more challenging. This paper introduces a centralized controller robust to burst errors. We propose a model predictive controller (MPC) computing intended future controls and sharing them with vehicles to be stored in a buffer. Through such buffer, vehicles may still retrieve control values even under burst errors. The proposed concept is evaluated in a mixed multi vehicle braking scenario and its superiority over other approaches in terms of distance and acceleration errors compared is shown.

This positioning paper provides an overview on an envisioned platform, intended as a set of technologies, protocols and algorithms, to achieve highly accurate localization for cooperative-intelligent transport systems. This is the result of a three years investigation conducted within the scope of the EU H2020 HIGHTS project and offers an insight on the envisioned hybrid service platform to enable vehicular positioning services for highly automated driving (HAD) scenarios. This paper reviews the main components of such a platform, drafting the guidelines for the seamless integration (i.e. hybridization) and field validation of multiple localization solutions to support robust HAD functionalities.

The Internet of Things (IoT) has accelerated the innovations in the connected car domain. Together with Intelligent Transportation Systems (ITS), the IoT technologies envision unleashing efficient and more sustainable transportation systems. A connected car is already considered as an IoT resource whose capabilities can be exposed through an IoT platform, but few testbeds provide full-stack, cloud-based environment for connected car services experimentation. This paper formulates several research and engineering challenges for developing such testbed. Our proposed testbed exploits the IoT and microservices to provide advanced functionalities such as (i) the integration of heterogeneous sensors and vehicular communication technologies in a complete end-to-end stack and (ii) an Edge Server for vehicular data annotation, local processing with actuation. The testbed exposes the vehicular resources securely through a Cloud based framework and open APIs. They are supported by The Web of Things (WoT) based interoperable descriptions of vehicular devices and interactions among relevant actors. Finally, the Cloud system deploys the connected car services using microservices philosophy. In addition to that, we present a functional IoT architecture of the testbed, describe its operational phases, novel aspects, prototyping and experimentation details.

This paper addresses Particle Filter (PF)-based hybrid Cooperative Localization (CLoc) strategies consisting of fusing absolute position information from embedded Global Navigation Satellite System (GNSS) with relative distance-dependent estimates using Impulse Radio - Ultra WideBand (IR-UWB) technology. Such hybrid GNSS/IR-UWB CLoc yet cannot benefit from the high precision estimates from the IR-UWB due to the disparity between GNSS position and IR-UWB V2V ranging noises, leading to a divergence in CLoc accuracy. This paper first investigates the source of such counter-intuitive effect, and second proposes a novel adaptive Bayesian dithering technique to improve the efficiency of GNSS/IR-UWB fusion-based CLoc. This strategy increases the probability to reach a 20 cm accuracy from 50% (conventional IR-UWB and WiFi PF) to 90%.

In an effort to improve positioning accuracy in Vehicular Ad hoc NETworks (VANETs), Cooperative Localization (CLoc) has been proposed to fuse relative observations from Vehicle-to-Vehicle (V2V) communication devices with absolute observations from on-board resources such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Units (IMU), and Wheel Speed Sensors (WSSs). In challenging but common tunnel environments, prolonged GNSS outages and unsustainable error accumulation of inertial sensors over time (e.g., gyroscopes) lead to the fast divergence of position estimates. In this paper, we aim at resolving this problem by relying on additional Vehicle-to-Infrastructure (V2I) measurements, making use of RoadSide Units (RSUs) internally to the tunnel. In particular, we explore practical trade-offs between V2I technology (i.e., Impulse Radio - Ultra WideBand (IR-UWB) or ITS-G5) and RSUs deployment costs (i.e., in terms of density and geometric configuration), while improving CLoc absolute accuracy. We also consider combining this solution with lane detection capabilities (e.g., camera-based) and associated road information, while comparing it with a more conventional approach based on GNSS repeaters.

International audience

To support the increasing need for WiFi gigabit links, the IEEE 802.11ac technology introduced 80MHz and 160MHz channels, which led to an extended WiFi channelization at 5GHz, spanning over the ISM band up to 5.9GHz. However, 5.9GHz also accommodates the 70MHz spectrum reserved for vehicular traffic safety-related applications using IEEE 802.11p technology, forcing both technologies to coexist. In this paper, we highlight the inherent differences between these two technologies and formulate the coexistence challenge on a basic urban scenario. We present and evaluate two coexistence protocols proposed by the WiFi community. Lastly, we illustrate WiFi potential `harmful' interferences to safety-related ITS applications and propose improvements for a better coexistence.

Precise location services are seen as key enablers to future Intelligent Transport Systems (ITSs). Relying on Vehicle-to-Vehicle (V2V) communication links, one promising solution consists in performing distributed Cooperative Positioning (CP). More specifically, Cooperative Awareness Message (CAM) broadcasts from neighboring vehicles (seen as “virtual anchors”) are used to exchange positional information and to measure V2V radiolocation metrics such as the Received Signal Strength Indicator (RSSI). For the sake of fusing these nonlinear hybrid data, Particle Filters (PFs) represent the required positional information by a set of particles with associated weights. However, in a jointly cooperative and distributed context, the transmission of explicit particle clouds (required by receiving neighbors to update their own location estimates) is hardly affordable under limited V2V channel capacity with typical numbers of particles. In this paper we thus combine and compare several solutions in terms of message representation and adaptive transmission policy so as to reduce simultaneously CAM overhead, channel congestion and computational complexity. Proposals are made at both signal processing level (parametric density approximation) and protocol level (jointly adaptive transmission payload, power and rate), showing no impact on channel load in congested scenarios and negligible CP accuracy degradation in comparison with standard CAM transmission at critical rates.

Software-Defined Networking (SDN) is transforming the networking ecosystem. SDN allows network operators to easily and quickly introduce new services and flexibly adapt to their requirements, while simplifying the network management to reduce the cost of operation, maintenance and deployment. On the other hand, mobility is a key aspect for the future mobile networks. In this context, Distributed Mobility Management (DMM) has been recently introduced as a new trend to overcome the limitations of the today's mobility management protocols which are highly centralized and hierarchical. Driven from the fact that DMM and SDN share the same principle in which the data and control plane are decoupled, we propose a DMM solution based on SDN architecture called S-DMM. This solution offers a lot of advantages including no need to deploy any mobility-related component at the access router, independence of the underlying technologies, and per-flow mobility support. On one hand, the numerical results prove that S-DMM is more scalable than the legacy DMM. On the other hand, the experiment results from a real implementation show that S-DMM comes at no performance penalty (in terms of handover latency and end-to-end delay) compared to legacy DMM, yet at a slightly better management cost, which makes S-DMM a promising candidate for a mobility management solution in the context of 5G networks.

Vehicles are becoming the next frontiers for Internet of Things (IoT) based platforms and services. Connected vehicles, Intelligent Transportation Systems (ITS) together with IoT technologies have the potential of unleashing efficient and more sustainable transportation system which is fast becoming an important societal challenge. This paper formulates several main research and engineering challenges for integrating connected vehicles into IoT ecosystems. The challenges include - (i) a suitable alternative of cloud platform to support real time connected vehicular scenarios, (ii) uniform description and data collection mechanisms from vehicular sensors, (iii) integrating smart devices into transport systems, (iv) uniform mechanism for data fusion and analytics and (v) integrating all heterogeneous elements into a standard IoT architecture for connected vehicles. To mitigate these challenges, we propose a novel IoT framework. The solutions, operational phases of the framework, software elements & their implementations and advantages are described in details. The building blocks of the framework are integrated into an oneM2M standard architecture. Finally, the paper concludes with best practice recommendations and lessons learnt from the prototyping.

Cooperative safety applications require dedicated short-range communications (DSRC) to provide position-awareness of neighboring vehicles at a specific level of reliability, i.e., awareness-quality, up to a given distance, i.e., awareness-range. However, heavy communication loads negatively impact such awareness requirements due to communication impairments, ranging from strict capacity limitations of DSRC channels to correlated packet collisions due to periodic communication patterns. Transmission control strategies may adapt power or rate to control such impairments but risk missing the requirements of cooperative safety applications. In this paper, we design a new awareness control strategy by implementing a spatial awareness framework. Specifically, we adapt the distribution of the awareness-quality as a function of the awareness-range. Therefore, we first propose random transmit power control (RTPC), which manages to provide different levels of awareness-quality at different ranges, while mitigating correlated packet collisions by randomizing them in space. As RTPC is able to reduce the channel load, we secondly propose to combine RTPC with transmit rate control (TRC) and to benefit from the gained channel resources by subsequently increasing the update-rate and by implication, the quality of position-awareness. The spatial awareness control capability of RTPC+TRC has been evaluated through simulations. We discuss the influence of RTPC+TRC on cooperative safety applications exemplarily for the forward collision warning (FCW) application.

Distributed Floating Car Data (DFCD) corresponds to vehicles directly exchanging their floating data and cooperatively estimating the local traffic density instead of letting this task to the cloud. With a fully distributed approach, DFCD exhibits salient advantages in particular related to low latency and high reactiveness for smart mobility applications. This work proposes DissFlow, a DFCD solution relying on the knowledge of data dissemination delay of Vehicular-to-Everything (V2X) communications to estimate the underlying vehicular traffic density. DissFlow is first analytically formulated and analyzed, before being evaluated by simulation means on the iTETRIS platform. Considering a 1-D road network modeled by SUMO, simulation results show that the knowledge of the V2X dissemination delay allows to closely estimate and follow the evolutionary trend of vehicular traffic density.

It is widely acknowledged that the key limitation to a raising market deployment of Electric Vehicles (EV) is correlated to the anxiety related to electric vehicle charging services (EVCS). From a user perspective, the electricity service should provide widely available and easily reachable charging stations with transparent payment options. From electricity operator perspective, charging vehicles should be well scheduled in time and space to avoid sudden burst of requests. Such EVCS should be conducted before reaching a charging station as well as ubiquitous and transparent to the mobility of EVs. An efficient heterogeneous communication system is then required. The centralized nature of the EVCS makes a network-based IP mobility such as PMIPv6 a good choice, first to make heterogeneous technologies transparent to the EVCS, but also hides the mobility of the EVs to the service. In this paper, we first present the mapping between the charging type and use cases of the EVCS. We then describe the required extensions to PMIPv6 to be integrated to the EVCS, and finally via a near-to-real testbed show that the EVCS satisfies the data delivery time required by the IEEE 1646.

Urban traffic congestions have become a financial and societal burden in many cities. Efficient traffic management solutions mitigating such congestions require a reliable modeling and estimation of traffic jams. In urban traffic, the modeling challenges are related to flow collisions and gridlocks created at intersections. In this paper, we propose a traffic light extended Cell Transmission Model (CTM), where the influence of flow collisions and gridlocks are modeled by a single CK parameter. Our approach only requires adapting CK for each intersection type/geometry instead of a complex mathematical formulae proposed in related works. We formalize the description of our urban CTM, and evaluate its capability to model traffic volumes and jams against the microscopic traffic simulator SUMO (Simulation of Urban MObility). Results show that the CK parameter is able to closely reproduce the impact of collisions and gridlocks on traffic jam, making the proposed urban CTM suitable to predict traffic congestions in urban environments.

Future safety-related vehicular applications require reliable information exchange provided by Dedicated Short Range Communication (DSRC). As the wireless vehicular environment is very challenging, it is crucial to test and evaluate the performance of DSRC communications in real conditions through experimental tests. Yet, DSRC communications have been evaluated independently from vehicles, and precise knowledge related to vehicular integration effects (i.e. cable/connector loss, antenna placement, vehicle geometry, etc) on DSRC communications still lacks. In this paper, we measure aspects of vehicular integration influencing DSRC communication in field experimental tests. Considering a global link budget, we illustrate the non-negligible impacts of cable, connectors, hardware, or roof types and antenna design on the communication range. We also measure their influence on the wireless channel and show that a reasonable distance between vehicles and radio obstacles, or metallic side-reflector improve communication range. Applying these results to a Road Hazard Warning application, we illustrate that, while vehicular integration affects its performance, wireless channel effects, in particular side reflectors, partially compensate them.

We see today urban traffic growing faster than the capacity of road infrastructures and posing a challenge to traffic safety and to the sustainability of our mobility. In this context, cooperative mobility strategies are expected to become a key enabler for a better usage of the available road infrastructures. Considering the estimation by the American Automotive Association of the yearly cost of traffic safety to 160 billion USD, or the yearly cost of traffic congestion to 50 billion EUR by the European Commission, local road authorities are facing a strategic challenge to develop and propose cooperative ITS applications to the public.

In this paper, we propose a framework for user/operator utility-based infrastructure deployment in vehicular networks. We extend the well known maximum coverage problem to consider the following four complementary aspects: (i) a non-circular polygon-based coverage representing measured heterogeneous any-directional communication conditions (ii) a generic intensity-based utility function reflecting variations of the intensity of a desired metric over the polygonal area (iii) comparable metrics either by normalizing the utility over the coverage, or by an intensity threshold maintaining a minimal utility regardless of coverage, (iv) a joint user/operator utility optimization minimizing the infrastructure deployment. We finally illustrate the benefits of our framework over related coverage-based approaches in an exemplary study.

The wide scale deployment of cooperative vehicular ad-hoc networks will require the design of efficient congestion control policies that guarantee stable and reliable communications between vehicles and with infrastructure nodes. These policies should reduce the load on the communications channel, while satisfying the strict application's reliability requirements. To this aim, this letter proposes and evaluates a contextual cooperative congestion control policy that exploits the traffic context information of each vehicle to reduce the channel load, while satisfying the vehicular applications requirements.

In this paper we propose a simulation-as-a-service approach to evaluate ITS applications. We segment a simulation process in two building blocks: a web-interface/server front-end used by users to configure the remote simulations of their ITS application, and a back-end consisting of a controller and a HCC platform to conduct the remote simulations. The controller is in charge of configuring the HCC platform as well as distributing and scheduling the simulations on it. HCC platforms usually provide users with only a restricted control on the simulation environment and a limited set of available libraries. To mitigate these drawbacks, we make use of the virtualization support of recent HCC platforms and employ Kernel-based Virtual Machines.

Position-based routing has proven to be well suited for highly dynamic environment such as Vehicular Ad Hoc Networks (VANET) due to its simplicity. Greedy Perimeter Stateless Routing (GPSR) and Greedy Perimeter Coordinator Routing (GPCR) both use greedy algorithms to forward packets by selecting relays with the best progress towards the destination or use a recovery mode in case such solutions fail. These protocols could forward packets efficiently given that the underlying network is fully connected. However, the dynamic nature of vehicular network, such as vehicle density, traffic pattern, and radio obstacles could create unconnected networks partitions. To this end, we propose GeoDTN+Nav, a hybrid geographic routing solution enhancing the standard greedy and recovery modes exploiting the vehicular mobility and on-board vehicular navigation systems to efficiently deliver packets even in partitioned networks. GeoDTN+Nav outperforms standard geographic routing protocols such as GPSR and GPCR because it is able to estimate network partitions and then improves partitions reachability by using a store-carry-forward procedure when necessary. We propose a virtual navigation interface (VNI) to provide generalized route information to optimize such forwarding procedure. We finally evaluate the benefit of our approach first analytically and then with simulations. By using delay tolerant forwarding in sparse networks, GeoDTN+Nav greatly increases the packet delivery ratio of geographic routing protocols and provides comparable routing delay to benchmark DTN algorithms.

Mobility is the distinguishing feature of vehicular networks, affecting the evolution of network connectivity over space and time in a unique way. Connectivity dynamics, in turn, determine the performance of networking protocols, when they are employed in vehicle-based, large-scale communication systems. Thus, a key question in vehicular networking is: which effects does nodes mobility generate on the topology of a network built over vehicles? Surprisingly, such a question has been quite overlooked by the networking research community. In this paper, we present an in-depth analysis of the topological properties of a vehicular network, unveiling the physical reasons behind the peculiar connectivity dynamics generated by a number of mobility models. Results make one think about the validity of studies conducted under unrealistic car mobility and stimulate interesting considerations on how network protocols could take advantage of vehicular mobility to improve their performance.

In this paper, we introduce a routing solution called "landmark overlays for urban vehicular routing environments" (LOUVRE), an approach that efficiently builds a landmark overlay network on top of an urban topology. We define urban junctions as overlay nodes and create an overlay link if and only if the traffic density of the underlying network guarantees the multi-hop vehicular routing between the two overlay nodes. LOUVRE contains a distributed traffic density estimation scheme which is used to evaluate the existence of an overlay link. Then, efficient routing is performed on the overlay network, guaranteeing a correct delivery of each packet. We evaluate LOUVRE against the benchmark routing protocols of GPSR and GPCR and show that LOUVRE performs higher in packet delivery and achieves lower hop count.

Within the extent of research activity in the field of vehicular networking, notwithstanding the quantity of simulative studies carried out on protocols performance, the impact of vehicular mobility characterization has been often overlooked. Realistic mobility models are seldom employed, and when used, no comment is usually provided on the different effects that such models have on the results with respect to simpler mobility descriptions. In this paper, we address this issue, and analyze the impact that various levels of details in vehicular mobility modeling have on the simulation of networking protocols.

Geo-localization information is a key component for providing location-based services for Intelligent Transportation Systems (ITS). Generally obtained by GPS devices and transmitted by Wireless Vehicular Communications, geo-localization in ITS represents a promising approach to reach objectives such as an increased road safety, transport efficiency, or on-the-road services. Despite its popularity, the issue of the overhead generated by the transmission of geo-localization data has not been addressed by the ITS community. In this paper, we first discuss the format of the information provided by GPS devices and propose a flexible message structure for exchanging geo-localization data. Then, we illustrate the significant overhead generated by the transmission of such information and propose a compression method achieving up to 70% overhead reduction without loss of precision. We finally test our method on the OLSR routing protocol.

While most topology control protocols only address limited network mobility, we propose in this paper a quasi-localized topology control algorithm that considers mobility predictions in order to construct and maintain a power efficient topology without relying on periodic beacons. Indeed, a node is capable of extracting linear trajectories of its neighboring nodes based on their positions and velocities. Based on such information, a node obtains a local prediction of neighborhood evolution and can thereafter proactively adapt the topology without relying on periodic beacons. Maintenance is driven on a per-event basis. It is therefore only when a node changes course that messages are exchanged in order to adapt the structure. Our approach is able to create and keep a stable kinetic backbone at a linear message and time complexity. It also improves concurrent communications by providing a significant reduction on local power assignments, therefore reducing interferences, increasing battery life and improving the overall network lifespan.