RiskNet: neural risk assessment in networks of unreliable resources

We propose a graph neural network (GNN)-based method to predict the distribution of penalties induced by outages in communication networks, where connections are protected by resources shared between working and backup paths. The GNN-based algorithm is trained only with random graphs generated on the basis of the Barabási–Albert model. However, the results obtained show that we can accurately model the penalties in a wide range of existing topologies. We show that GNNs eliminate the need to simulate complex outage scenarios for the network topologies under study—in practice, the entire time of path placement evaluation based on the prediction is no longer than 4 ms on modern hardware. In this way, we gain up to 12 000 times in speed improvement compared to calculations based on simulations.This work was supported by the Polish Ministry of Science and Higher Education with the subvention funds of the Faculty of Computer Science, Electronics and Telecommunications of AGH University of Science and Technology (P.B., P.C.) and by the PL-Grid Infrastructure (K.R.).Peer ReviewedPostprint (published version

Determination of the influence of specific building regulations in smart buildings

The automation of domestic services began to be implemented in buildings since the late nineteenth century, and today we are used to terms like ‘intelligent buildings’, ‘digital home’ or ‘domotic buildings’. These concepts tell us about constructions which integrate new technologies in order to improve comfort, optimize energy consumption or enhance the security of users. In conjunction, building regulations have been updated to suit the needs of society and to regulate these new facilities in such structures. However, we are not always sure about how far, from the quantitative or qualitative point of view, legislation should regulate certain aspects of the building activity. Consequently, content analysis is adopted in this research to determine the influence of building regulations in the implementation of new technologies in the construction process. This study includes the analysis of different European regulations, the collection and documentation of such guidelines that have been established and a study of the impact that all of these have had in the way we start thinking an architectural project. The achievements of the research could be explained in terms of the regulatory requirements that must be taken into account in order to achieve a successful implementation of a home automation system, and the key finding has been the confirmation of how the design of smart buildings may be promoted through specific regulatory requirements while other factors, such as the global economic situation, do not seem to affect directly the rate of penetration of home automation in construction

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

On the Study of Vehicle Density in Intelligent Transportation Systems

Vehicular ad hoc networks (VANETs) are wireless communication networks which support cooperative driving among vehicles on the road. The specific characteristics of VANETs favor the development of attractive and challenging services and applications which rely on message exchanging among vehicles. These communication capabilities depend directly on the existence of nearby vehicles able to exchange information. Therefore, higher vehicle densities favor the communication among vehicles. However, vehicular communications are also strongly affected by the topology of the map (i.e., wireless signal could be attenuated due to the distance between the sender and receiver, and obstacles usually block signal transmission). In this paper, we study the influence of the roadmap topology and the number of vehicles when accounting for the vehicular communications capabilities, especially in urban scenarios. Additionally, we consider the use of two parameters: the SJ Ratio (SJR) and the Total Distance (TD), as the topology-related factors that better correlate with communications performance. Finally, we propose the use of a new density metric based on the number of vehicles, the complexity of the roadmap, and its maximum capacity. Hence, researchers will be able to accurately characterize the different urban scenarios and better validate their proposals related to cooperative Intelligent Transportation Systems based on vehicular communications

Network Latency and Packet Delay Variation in Cyber-physical Systems

The problem addressed in this paper is the limitation imposed by network elements, especially Ethernet elements, on the real-time performance of time-critical systems. Most current network elements are concerned only with data integrity, connection, and throughput with no mechanism for enforcing temporal semantics. Existing safety-critical applications and other applications in industry require varying degrees of control over system-wide temporal semantics. In addition, there are emerging commercial applications that require or will benefit from tighter enforcement of temporal semantics in network elements than is currently possible. This paper examines these applications and requirements and suggests possible approaches to imposing temporal semantics on networks. Model-based design and simulation is used to evaluate the effects of network limitations on time-critical systems

Context-Independent Centrality Measures Underestimate the Vulnerability of Power Grids

Power grids vulnerability is a key issue in society. A component failure may trigger cascades of failures across the grid and lead to a large blackout. Complex network approaches have shown a direction to study some of the problems faced by power grids. Within Complex Network Analysis structural vulnerabilities of power grids have been studied mostly using purely topological approaches, which assumes that flow of power is dictated by shortest paths. However, this fails to capture the real flow characteristics of power grids. We have proposed a flow redistribution mechanism that closely mimics the flow in power grids using the PTDF. With this mechanism we enhance existing cascading failure models to study the vulnerability of power grids. We apply the model to the European high-voltage grid to carry out a comparative study for a number of centrality measures. `Centrality' gives an indication of the criticality of network components. Our model offers a way to find those centrality measures that give the best indication of node vulnerability in the context of power grids, by considering not only the network topology but also the power flowing through the network. In addition, we use the model to determine the spare capacity that is needed to make the grid robust to targeted attacks. We also show a brief comparison of the end results with other power grid systems to generalise the result.Comment: Pre-Proceedings of CRITIS '1