Traditionally, the vehicle has been the extension of the man's ambulatory system, docile to the driver's commands. Recent advances in communications, controls and embedded systems have changed this model, paving the way to the Intelligent Vehicle Grid. The car is now a formidable sensor platform, absorbing information from the environment (and from other cars) and feeding it to drivers and infrastructure to assist in safe navigation, pollution control and traffic management. The next step in this evolution is just around the corner: the Internet of Autonomous Vehicles. Pioneered by the Google car, the Internet of Vehicles will be a distributed transport fabric capable to make its own decisions about driving customers to their destinations. Like other important instantiations of the Internet of Things (e.g., the smart building), the Internet of Vehicles will have communications, storage, intelligence, and learning capabilities to anticipate the customers' intentions. The concept that will help transition to the Internet of Vehicles is the Vehicular Cloud, the equivalent of Internet cloud for vehicles, providing all the services required by the autonomous vehicles. In this article, we discuss the evolution from Intelligent Vehicle Grid to Autonomous, Internet-connected Vehicles, and Vehicular Cloud.

Increasing need for people to be "connected";

Abstract-In this paper we apply the Named Data Networking [1], a newly proposed Internet architecture, to networking vehicles on the run. Our initial design, dubbed V-NDN, illustrates NDN's promising potential in providing a unifying architecture that enables networking among all computing devices independent from whether they are connected through wired infrastructure, ad hoc, or intermittent DTN. This paper describes a prototype implementation of V-NDN and its preliminary performance assessment, and identifies remaining challenges.

Allergic rhinitis (AR) is impacted by allergens and air pollution but interactions between air pollution, sleep and allergic diseases are insufficiently understood. POLLAR (Impact of air POLLution on sleep, Asthma and Rhinitis) is a project of the European Institute of Innovation and Technology (EIT Health). It will use a freely-existing application for AR monitoring that has been tested in 23 countries (the Allergy Diary, iOS and Android, 17,000 users, TLR8). The Allergy Diary will be combined with a new tool allowing queries on allergen, pollen (TLR2), sleep quality and disorders (TRL2) as well as existing longitudinal and geolocalized pollution data. Machine learning will be used to assess the relationship between air pollution, sleep and AR comparing polluted and non-polluted areas in 6 EU countries. Data generated in 2018 will be confirmed in 2019 and extended by the individual prospective assessment of pollution (portable sensor, TLR7) in AR. Sleep apnea patients will be used as a demonstrator of sleep disorder that can be modulated in terms of symptoms and severity by air pollution and AR. The geographic information system GIS will map the results. Consequences on quality of life (EQ-5D), asthma, school, work and sleep will be monitored and disseminated towards the population. The impacts of POLLAR will be (1) to propose novel care pathways integrating pollution, sleep and patients’ literacy, (2) to study sleep consequences of pollution and its impact on frequent chronic diseases, (3) to improve work productivity, (4) to propose the basis for a sentinel network at the EU level for pollution and allergy, (5) to assess the societal implications of the interaction. MASK paper N°32.

Abstract-As the technology available on cars is increasing, a wide range of applications, from safety to entertainment, are becoming factually accessible to passengers. Many of these applications involves a one-to-many transmission model where a single car broadcasts a message that has to be forwarded, even with multiple hops, in a very short time to all the other cars located within a range of few kilometers from the source. Since the high mobility and density of a car network scenario, specific solutions need to be devised to choreograph a fast-delivery multihop broadcast. To this aim, we developed a practical and efficient technique that allows cars to estimate their communication range with the help of a very limited message exchange and exploit this information to reduce the number of transmissions, as well as the hops to be traversed, and hence the time, required by a broadcasted message to reach all the cars following the sender within a certain distance.

Abstract-Road congestion results in a huge waste of time and productivity for millions of people. A possible way to deal with this problem is to have transportation authorities distribute traffic information to drivers, which in turn can decide (or be aided by a navigator) to route around congested areas. Such traffic information can be gathered by relying on static sensors placed at specific road locations (e.g., induction loops, video cameras) or by having single vehicles report their location, speed and travel time. While the former approach has been widely exploited, the latter has seen birth only more recently, and, consequently, its potential is less understood. For this reason, in this paper we study a realistic test case that allows to evaluate the effectiveness of such a solution. As part of this process: a) we designed a system that allows vehicles to crowd-source traffic information in an Ad-Hoc manner, allowing them to dynamically reroute based on individually collected traffic information, b) we implemented a realistic network-mobility simulator that allowed us to evaluate such a model, and c) the main focus of this paper: we performed a case study that evaluates whether such a decentralized system can help drivers to minimize trip times. This study is based on traffic survey data from Portland, Oregon and our results indicate that such navigation systems can indeed greatly improve traffic flow. Finally, to test the feasibility of our approach we implemented our system and run some real experiments at UCLA's C-Vet test-bed.

Recent advances in communications, controls, and embedded systems have changed the perception of a car. A vehicle has been the extension of the man's ambulatory system, docile to the driver's commands. It is now a formidable sensor platform, absorbing information from the environment (and from other cars) and feeding it to drivers and infrastructure to assist in safe navigation, pollution control, and traffic management. The next step in this evolution is just around the corner: the Internet of Autonomous Vehicles. Pioneered by the Google car, the Internet of Vehicles will be a distributed transport fabric capable of making its own decisions about driving customers to their destinations. Like other important instantiations of the Internet of Things (e.g. the smart building), the Internet of Vehicles will have communications, storage, intelligence, and learning capabilities to anticipate the customers' intentions. The concept that will help transition to the Internet of Vehicles is the vehicular fog, the equivalent of instantaneous Internet cloud for vehicles, providing all the services required by the autonomous vehicles. In this article, we discuss the evolution from intelligent vehicle grid to autonomous, Internet-connected vehicles, and vehicular fog.

Abstract-This paper proposes Navigo 1 , a location based packet forwarding mechanism for vehicular Named Data Networking (NDN). Navigo takes a radically new approach to address the challenges of frequent connectivity disruptions and sudden network changes in a vehicle network. Instead of forwarding packets to a specific moving car, Navigo aims to fetch specific pieces of data from multiple potential carriers of the data. The design provides (1) a mechanism to bind NDN data names to the producers' geographic area(s); (2) an algorithm to guide Interests towards data producers using a specialized shortest path over the road topology; and (3) an adaptive discovery and selection mechanism that can identify the best data source across multiple geographic areas, as well as quickly react to changes in the V2X network.