Ricerc@Sapienza

Detailed and accurate vehicle-oriented sensor data is considered fundamental for efficient vehicle-to-everything V2X communication applications, especially in the upcoming highly heterogeneous, brisk and agile 5G networking era. Information retrieval, transfer and manipulation in real-time offers a small margin for erratic behavior, regardless of its root cause.

Shielding and absorbing screens made of tunable graphene/ dielectric laminate (GL) doped by an electrostatic field bias are designed applying simple modelling procedures in the low-gigahertz frequency range. The adaptive response of both types of screens is achieved through the control of the effective sheet resistance of the GL, consisting of a proper number of doped graphene layers separated by thin films of polyethylene terephthalate (PET).

This paper presents a selection of research topics related to nano-electromagnetic compatibility (nano-EMC) issues in emerging carbon nanoelectronics. Carbon-based nano-interconnect modeling and analysis are first introduced. Then, the key techniques of carbon nanotube-filled through-silicon vias and carbon- based passive devices are discussed.

In high voltage transmission lines, shield wires are periodically grounded and can be periodically sectionalized for several purposes. These two practices have important effects on multiconductor transmission lines, since both affect the wave propagation along the line. In this work, the attention is focused on the frequency behavior of the characteristic impedance. Moreover, the effect of wires transposition on the characteristic impedance is also investigated.

We study maximal identifiability, a measure recently introduced in Boolean Network Tomography to characterize networks' capability to localize failure nodes in end-to-end path measurements. Under standard assumptions on topologies and on monitors placement, we prove tight upper and lower bounds on the maximal identifiability of failure nodes for specific classes of network topologies, such as trees, bounded-degree graphs, d-dimensional grids, in both directed and undirected cases.

Containers are increasingly adopted, because they simplify the deployment and management of applications. Moreover, the ever increasing presence of IoT devices and Fog computing resources calls for the development of new approaches for decentralizing the application execution, so to improve the application performance. Although several solutions for orchestrating containers exist, the most of them does not efficiently exploit the characteristics of the emerging computing environment.

Network recovery after large-scale failures has tremendous cost implications. While numerous approaches have been proposed to restore critical services after large-scale failures, they mostly assume having full knowledge of failure location, which cannot be achieved in real failure scenarios. Making restoration decisions under uncertainty is often further complicated in a large-scale failure. This paper addresses progressive network recovery under the uncertain knowledge of damages. We formulate the problem as a mixed integer linear programming (MILP) and show that it is NP-Hard.

Software Defined Networking (SDN) provides a framework to dynamically adjust and re-program the data plane with the use of flow rules. The realization of highly adaptive SDNs with the ability to respond to changing demands or recover after a network failure in a short period of time, hinges on efficient updates of flow rules. We model the time to deploy a set of flow rules by the update time at the bottleneck switch, and formulate the problem of selecting paths to minimize the deployment time under feasibility constraints as a mixed integer linear program (MILP).

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.

Here we discuss a framework (OpenAnswer) providing support to the teacher's activity of grading answers to open ended questions. OpenAnswer implements a teacher mediated peer-evaluation approach: the marking results obtained from peer assessments are tuned by the grades explicitly assigned by the teacher, the teacher grades only a subset of the answers, suggested by the system. When a termination criterion is met, for the process managing the amount of teacher grading work, the remaining answers are automatically graded.