Intervehicle communication (IVC) is emerging in research prominence for the interest that it is generating in all major car manufacturers and for the benefits that its inception will produce. The specific features of IVC will allow the deployment of a wide set of possible applications, which span from road safety to entertainment. Even if, on the one hand, these applications share the common need for fast multihop message propagation, on the other hand, they possess distinct characteristics in terms of generated network traffic. The state of the art of current research only proposes solutions specifically designed for a single application (or class) that is not directly extendable to a general IVC context. Instead, we claim that a privileged architecture exists, which is able to support the whole spectrum of application classes. To this aim, we propose a novel IVC architecture that adapts its functionalities to efficiently serve applications by quickly propagating their messages over a vehicular network. We conducted an extensive set of experiments that demonstrate the efficacy of our approach. As representative case studies, we considered two application classes that, for their network traffic characteristics, are at the opposite boundaries of the application spectrum: safety and entertainment.

Flying ad-hoc networks are becoming a promising solution for different application scenarios involving unmanned aerial vehicles, like urban surveillance or search and rescue missions. However, such networks present various and very specific communication issues. As a consequence, there are several research studies focused on analyzing their performance via simulation. Correctly modeling mobility is crucial in this context and although many mobility models are already available to reproduce the behavior of mobile nodes in an ad-hoc network, most of these models cannot be used to reliably simulate the motion of unmanned aerial vehicles. In this article, we list the existing mobility models and provide guidance to understand whether they could be actually adopted depending on the specific flying ad-hoc network application scenarios, while discussing their advantages and disadvantages.

Scalable routing for wireless communication systems is a compelling and challenging task. To this aim, routing algorithms exploiting geographic information have been proposed. These algorithms refer to nodes by their location, rather than their address, and use those coordinates to route greedily towards a destination. With the advent of unmanned airborne vehicle (UAV) technology, a lot of research effort has been devoted to extend position-based packet routing proposals to three dimensional environments. In this context, Flying Ad-hoc Networks (FANETs), comprised of autonomous flying vehicles, pose several issues. This work focuses on the state-of-the-art, stateless geographic packet routing protocols conceived or adapted for three-dimensional network scenarios. Proposals are evaluated over a common scenario through a comprehensive comparative analysis.

FANETs are a promising solution for application scenarios involving UAVs. There are many mobility models that can be used to reproduce the behavior of the mobile nodes in an ad-hoc network, but some of these cannot simulate the realistic motion of UAVs. In this paper we list the available mobility models and try to understand which ones should be adopted for different FANET application scenarios, discussing their pros and cons.

Sensor-enabled smartphone's have become a mainstream platform for researchers due to their ability to collect and process large quantities of data, hence creating new opportunities for innovative applications. Yet, the limits in employing sensors to opportunistically detect human behaviors are not clear and deserve investigation. To this purpose, in this article, we discuss movement pattern recognition in day-by-day urban street behavior. As a case study, we restrict at recognizing situations when a pedestrian stops, crosses a street ruled by a traffic light; to do so we only use data coming from the accelerometer of the pedestrian's smartphone.