Control-Based Resource Management Procedures for Satellite Networks

This paper describes the resource management of a DVBRCS geostationary satellite network. The functional modules of the access layer aim at efficiently exploiting the link resources while assuring the contracted Quality of Service (QoS) to the traffic entering the satellite network. The main novelty is the integration between the Connection Admission Control and the Congestion Control procedures. Both them exploit the estimation of the traffic load, performed by a Kalman filter. The proposed solution has been analysed via computer simulations, which confirmed their effectiveness

Robust Adaptive Congestion Control for Next Generation Networks

This paper deals with the problem of congestion control in a next-generation heterogeneous network scenario. The algorithm runs in the 'edge' routers (the routers collecting the traffic between two different networks) with the aim of avoiding congestion in both the network and the edge routers. The proposed algorithm extends congestion control algorithms based on the Smith's principle: i) the controller, by exploiting on-line estimates via probe packets, adapts to the delay and rate variations; ii) the controller assures robust stability in the presence of time-varying delays

A security metric for assessing the security level of critical infrastructures

The deep integration between the cyber and physical domains in complex systems make very challenging the security evaluation process, as security itself is more of a concept (i.e. a subjective property) than a quantifiable characteristic. Traditional security assessing mostly relies on the personal skills of security experts, often based on best practices and personal experience. The present work is aimed at defining a security metric allowing evaluators to assess the security level of complex Cyber-Physical Systems (CPSs), as Critical Infrastructures, in a holistic, consistent and repeatable way. To achieve this result, the mathematical framework provided by the Open Source Security Testing Methodology Manual (OSSTMM) is used as the backbone of the new security metric, since it allows to provide security indicators capturing, in a non-biased way, the security level of a system. Several concepts, as component Lifecycle, Vulnerability criticality and Damage Potential – Effort Ratio are embedded in the new security metric framework, developed in the scope of the H2020 project ATENA

Efficient and Risk-Aware Control of Electricity Distribution Grids

This article presents an economic model predictive control (EMPC) algorithm for reducing losses and increasing the resilience of medium-voltage electricity distribution grids characterized by high penetration of renewable energy sources and possibly subject to natural or malicious adverse events. The proposed control system optimizes grid operations through network reconfiguration, control of distributed energy storage systems (ESSs), and on-load tap changers. The core of the EMPC algorithm is a nonconvex optimization problem integrating the ESSs dynamics, the topological and power technical constraints of the grid, and the modeling of the cascading effects of potential adverse events. An equivalent (i.e., having the same optimal solution) proxy of the nonconvex problem is proposed to make the solution more tractable. Simulations performed on a 16-bus test distribution network validate the proposed control strategy

Automatic Transportation Mode Recognition on Smartphone Data Based on Deep Neural Networks

In the last few years, with the exponential diffusion of smartphones, services for turn-by-turn navigation have seen a surge in popularity. Current solutions available in the market allow the user to select via an interface the desired transportation mode, for which an optimal route is then computed. Automatically recognizing the transportation system that the user is travelling by allows to dynamically control, and consequently update, the route proposed to the user. Such a dynamic approach is an enabling technology for multi-modal transportation planners, in which the optimal path and its associated transportation solutions are updated in real-time based on data coming from (i) distributed sensors (e.g., smart traffic lights, road congestion sensors, etc.); (ii) service providers (e.g., car-sharing availability, bus waiting time, etc.); and (iii) the user’s own device, in compliance with the development of smart cities envisaged by the 5G architecture. In this paper, we present a series of Machine Learning approaches for real-time Transportation Mode Recognition and we report their performance difference in our field tests. Several Machine Learning-based classifiers, including Deep Neural Networks, built on both statistical feature extraction and raw data analysis are presented and compared in this paper; the result analysis also highlights which features are proven to be the most informative ones for the classification

Traffic Steering and Network Selection in 5G Networks based on Reinforcement Learning

This paper presents a controller for the problem of Network Selection in 5G Networks, based on Reinforcement Learning. The problem of Network Selection and Traffic Steering is modeled as a Markov Decision Process and a Q- Learning based control solution is designed to meet 5G requirements, such as Quality of Experience (QoE) maximization, Quality of Service (QoS) assurance and load balancing. Numerical simulations preliminarily validate the proposed approach on a simulated scenario considered in the European project H2020 5G-ALLSTAR