Ricerc@Sapienza

In today’s connected world, users migrate within a complex set of networks, including, but not limited to, 3G and 4G (LTE) services provided by mobile operators, Wi-Fi hotspots in private and public places, as well as wireless and/or wired LAN access in business and home environments. Following the widely expanding Bring Your Own Device (BYOD) approach, many public and educational institutions have begun to encourage customers and students to use their own devices at all times.

The analysis of the shape and geometry of the human hand has long represented an at- tractive field of research to address the needs of digital image forensics. Over recent years, it has also turned out to be effective in biometrics, where several innovative research lines are pursued. Given the widespread diffusion of mobile and portable devices, the possibil- ity of checking the owner identity and controlling the access to the device by the hand image looks particularly attractive and less intrusive than other biometric traits.

The interdependency of multiple networks makes today's infrastructures more vulnerable to failures. Prior works mainly focused on robust network design and recovery strategies after failures, given complete knowledge of failure location. Nevertheless, in real-world scenarios, the location of failures might be unknown or only partially known. In this work, we focus on cascading failures involving the power grid and its communication network with imprecision in failure assessment.

In this article, we address the problem of autonomously deploying mobile sensors in an unknown complex environment. In such a scenario, mobile sensors may encounter obstacles or environmental sources of noise, so that movement and sensing capabilities can be significantly altered and become anisotropic. Any reduction of device capabilities cannot be known prior to their actual deployment, nor can it be predicted.We propose a new algorithm for autonomous sensor movements and positioning, called DOMINO (DeplOyment of MobIle Networks with Obstacles).

The computation of electrical flows is a crucial primitive for many recently proposed optimization algorithms on weighted networks. While typically implemented as a centralized subroutine, the ability to perform this task in a fully decentralized way is implicit in a number of biological systems. Thus, a natural question is whether this task can provably be accomplished in an efficient way by a network of agents executing a simple protocol.

The internal inspection of large pipeline infrastructures, such as sewers and waterworks, is a fundamental task for the prevention of possible failures. In particular, visual inspection is typically performed by human operators on the basis of video sequences either acquired on-line or recorded for further off-line analysis. In this work, we propose a vision-based software approach to assist the human operator by conveniently showing the acquired data and by automatically detecting and highlighting the pipeline sections where relevant anomalies could occur.

The paper deals with an innovative Predictive Maintenance (PdM) system to assess the quality of the engine oil for buses, tested in Ravenna within the European Bus System of the Future - EBSF_2 project, funded by the European Union. The system relies on a PdM software linked to oil sensors and filters, installed on a test fleet, and on an IT architecture, specifically designed.