Advertisement Delivery and Display in Vehicular Networks

The role of vehicles has been rapidly expanding to become a different kind of utility, no longer just vehicles but nodes of the future Internet. The car producers and the research community are investing considerable time and resources in the design of new protocols and applications that meet customer demand, or that foster new forms of interaction between the moving customers and the rest of the world. Among the variety of new applications and business models, the spreading of advertisements is expected to play a crucial role. Indeed, advertising is already a significant source of revenue and it is currently used over many communication channels, such as the Internet and television. In this paper, we address the targeting of advertisements in vehicular networks, where advertisements are broadcasted by Access Points and then displayed to interested users. In particular, we describe the advertisement dissemination process by means of an optimization model aiming at maximizing the number of advertisements that are displayed to users within the advertisement target area and target time period. We then solve the optimization problem on an urban area, using realistic vehicular traffic traces. Our results highlight the importance of predicting vehicles mobility and the impact of the user interest distribution on the revenue that can be obtained from the advertisement service

A-VIP: Anonymous Verification and Inference of Positions in Vehicular Networks

MiniconferenceInternational audienceKnowledge of the location of vehicles and tracking of the routes they follow are a requirement for a number of applications, including e-tolling and liability attribution in case of accidents. However, public disclosure of the identity and position of drivers jeopardizes user privacy, and securing the tracking through asymmetric cryptography may have an exceedingly high computational cost. Additionally, there is currently no way an authority can verify the correctness of the position information provided by a potentially misbehaving car. In this paper, we address all of the issues above by introducing A-VIP, a lightweight framework for privacy preserving and tracking of vehicles. A-VIP leverages anonymous position beacons from vehicles, and the cooperation of nearby cars collecting and reporting the beacons they hear. Such information allows an authority to verify the locations announced by vehicles, or to infer the actual ones if needed. We assess the effectiveness of A-VIP through both realistic simulation and testbed implementation results, analyzing also its resilience to adversarial attacks

Efficient area formation for LTE broadcasting

An effective way to provide popular content in LTE networks is through broadcast and multicast services (a.k.a. eMBMS). This requires to aggregate cells into areas where transmissions are synchronized in time so that each area broadcasts the same set of content items, on the same radio resources. We look at an aspect of LTE broadcasting that has been scarcely addressed so far: how to form broadcasting areas and assign content to them so that radio resources are efficiently exploited and user requests satisfied. Due to its high complexity, we solve the problem through an original clustering heuristics, named Single-Content Fusion (SCF), that initially aggregates cells into single-content areas by maximizing cell similarity in content interests. Such areas are then merged into multiple-content areas leveraging similarity in spatial coverage. The validity of our solution is shown by the excellent match with the optimum in a toy scenario and by the remarkable advantages SCF provides in large-scale, real-world scenarios, in comparison to other heuristic approaches

LookUp: Enabling Pedestrian Safety Services via Shoe Sensing

Motivated by safety challenges resulting from distracted pedestrians, this paper presents a sensing technology for fine-grained location classification in an urban environment. It seeks to detect the transitions from sidewalk locations to in-street locations, to enable applications such as alerting texting pedestrians when they step into the street. In this work, we use shoe-mounted inertial sensors for location classification based on surface gradient profile and step patterns. This approach is different from existing shoe sensing solutions that focus on dead reckoning and inertial navigation. The shoe sensors relay inertial sensor measurements to a smartphone, which extracts the step pattern and the inclination of the ground a pedestrian is walking on. This allows detecting transitions such as stepping over a curb or walking down sidewalk ramps that lead into the street. We carried out walking trials in metropolitan environments in United States (Manhattan) and Europe (Turin). The results from these experiments show that we can accurately determine transitions between sidewalk and street locations to identify pedestrian risk