Giovanni Pau

Internet of Vehicles: From intelligent grid to autonomous cars and vehicular fogs

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.

Navigo: Interest forwarding by geolocations in vehicular Named Data Networking

This paper proposes Navigo, 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.

Content distribution in VANETs

Advances in vehicular communications technology are making content distribution to vehicles more effective and increasingly more popular. This paper presents state of the art technologies and protocols for content distribution in VANETs. Major challenges are Internet access spectrum scarcity, mobility, connectivity intermittence and scalability. Aspects covered in this paper include: coexistence of WiFi and LTE; application of network coding; protection from pollution attacks; incentive design for cooperation enforcement; QoS support for video streaming applications. Simulation and testbed results are presented to support the findings. Critical issues that will determine future directions in this area are identified and discussed.

Anchor-Less Producer Mobility in ICN

Mobility has become a basic premise of almost any network communication, thereby requiring a native integration into next generation 5G networks. Despite the numerous efforts to propose and to standardize effective mobility management models for IP, the result is a very complex, poorly flexible set of mechanisms not suitable for the design of a radio-agnostic 5G mobile core. The natural support for mobility, security and storage offered by ICN (Information-Centric Networking) architecture, makes it a good candidate to define a radically new solution relieving limitations of traditional approaches. If consumer mobility is supported in ICN by design in virtue of its connectionless pull-based communication model, producer mobility still appears to be an open challenge. In this work we describe an initial proposal for an anchor-less approach to manage producer mobility via Interest Updates/Notifications in the data plane, even in presence of latency-sensitive applications. We detail the different operations triggered by producer movements and position our contribution in the context of existing alternatives, by discussing either user performance and network metrics.

Using geosocial search for urban air pollution monitoring

Abstract While Twitter and other Online Social Networks (OSNs) or microblogs are considered as a source of information for breaking news or uproarious and unexpected events, they could also be exploited as a dense worldwide sensors network for physical measurements. The corpus of geotagged posts from OSNs includes people’s feedbacks about a wide range of topics, with precise temporal and geographical metadata, that can be used as a support or an improvement to hardware sensors. For instance, if collocated people, independently and at the same time, write posts complaining about high temperatures, it could effectively denote a raise of heat in that place. In this paper, we explore the feasibility to use a geographical search on social networks, that is, a geosocial search, about air pollution related posts, as effective air impureness measurements. We evaluate our assumption in large cities over three continents of the planet, where a minimum increment about the number of air pollution related posts in an area, indeed corresponds to a raise of minimum pollution values in such area. Such a correlation can be exploited to integrate and extend existing air pollution monitoring networks. At the end of the manuscript we propose to further employ the time series of posts returned by the geosocial search to predict next pollution values.

Social Network Based Crowd Sensing for Intelligent Transportation and Climate Applications

In recent years, the growing prevalence of social networks makes it possible to utilize human users as sensors to inspect city environment and human activities. Consequently, valuable insights can be gained by applying data mining techniques to the data generated through social networks. In this work, a practical approach to combine data mining techniques with statistical analysis is proposed to implement crowd sensing in a smart city. A case study to analyze the relationship between weather conditions and traffic congestion in Beijing based on tweets posted on Sina Weibo platform is presented to demonstrate the proposed approach. Following the steps of data pre-processing and topic determination, we applied Granger Causality Test to study the causal relationships between weather conditions, traffic congestion and human outdoor activity. The mediation analysis is also implemented to verify human outdoor activity as a mediator variable significantly carrying the influence of good weather to traffic congestion. The result demonstrates that outdoor activity serves as a mediator transmitting the effect of good weather on traffic congestion. In addition, the causes of negative emotion are also studied.

Sensing Pollution on Online Social Networks: A Transportation Perspective

Transportation policy and planning strategies, as well as Intelligent Transportation Systems (ITS), can all play important roles in decreasing pollution levels and their negative effects. Interestingly, limited effort has been devoted to exploring the potential of social network analysis in such context. Social networks provide direct feedback from people and, hence, potentially valuable information. A post telling how a person feels about pollution at a given time at a given location, could be useful to policy-makers, planners or environmentally-aware ITS designers. This work verifies the feasibility of sensing air pollution from social networks and of integrating such information with real sensors feeds, unveiling how people advertise such phenomenon, acting themselves as smart objects, and how online posts relate to true pollution levels. This work explores a new dimension in pollution sensing for the benefit of environmental and transportation research in future smart cities, confronting over 1,500,000 posts and pollution readings obtained from governmental on-the-field sensors over a one-year span.

Enabling street-level pollution and exposure measures: a human-centric approach

In the last several years the awareness about pollution levels and other natural hazards has risen significantly. Millions of citizens globally pay daily attention to air quality and other factors such as for instance UV index and pollens. This new need has been so far met by a plethora of smartphone applications that leverage data collected by the city-operated stations or, in some cases, community-based stations located at the user premises. In this work we exploit recent advances in micro-controllers and sensing technologies to design and implement a personal pollution awareness system tiny enough to be embedded into user accessories. In particular, we will introduce the concept and the architecture of our prototype and show the preliminary results we collected using this prototype.

Drop dead data

In this paper we conduct a performance evaluation of privacy protocols for Information Centric Networking (ICN). Our contribution is three-fold: Firstly, we define a simple but complete performance framework for comparing current and future solutions. Secondly, we conjecture and prove the existence of unsafe replicas, namely cached content that remains available to users whose access has been revoked. Thirdly, we propose a performant protocol that solves the problem of unsafe replicas without tampering with the caching functionality of ICN.

Poster: ParkMaster: Leveraging Edge Computing in Visual Analytics

In this work we propose ParkMaster, a low-cost crowdsourcing architecture which exploits machine learning techniques and vision algorithms to evaluate parking availability in cities. While the user is normally driving ParkMaster enables off the shelf smartphones to collect information about the presence of parked vehicles by running image recognition techniques on the phones camera video streaming. The paper describes the design of ParkMasters architecture and shows the feasibility of deploying such mobile sensor system in nowadays smartphones, in particular focusing on the practicability of running vision algorithms on phones.