Distributed Inference over Linear Models using Alternating Gaussian Belief Propagation

We consider the problem of maximum likelihood estimation in linear models represented by factor graphs and solved via the Gaussian belief propagation algorithm. Motivated by massive internet of things (IoT) networks and edge computing, we set the above problem in a clustered scenario, where the factor graph is divided into clusters and assigned for processing in a distributed fashion across a number of edge computing nodes. For these scenarios, we show that an alternating Gaussian belief propagation (AGBP) algorithm that alternates between inter- and intra-cluster iterations, demonstrates superior performance in terms of convergence properties compared to the existing solutions in the literature. We present a comprehensive framework and introduce appropriate metrics to analyse AGBP algorithm across a wide range of linear models characterised by symmetric and non-symmetric, square, and rectangular matrices. We extend the analysis to the case of dynamic linear models by introducing dynamic arrival of new data over time. Using a combination of analytical and extensive numerical results, we show the efficiency and scalability of AGBP algorithm, making it a suitable solution for large-scale inference in massive IoT networks.Comment: 14 pages, 18 figure

Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks

Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for Machine Learning (ML)/Artificial Intelligence (AI) functions, 5G networks are expected to enable novel data-centric Smart Grid (SG) services. In this paper, we explore how data-driven SG services could be integrated with ML/AI-enabled 5G networks in a symbiotic relationship. We focus on the State Estimation (SE) function as a key element of the energy management system and focus on two main questions. Firstly, in a tutorial fashion, we present an overview on how distributed SE can be integrated with the elements of the 5G core network and radio access network architecture. Secondly, we present and compare two powerful distributed SE methods based on: i) graphical models and belief propagation, and ii) graph neural networks. We discuss their performance and capability to support a near real-time distributed SE via 5G network, taking into account communication delays

Simulation of the route network and ferry traffic intensity based on the process of discretization and circos plot intensity diagram

Today, one of the main important tasks is to analyze the states of achievement related to the required levels for marine passenger terminals and their route networks, depending on the influence of the external environment (based on the discretization of processes). This proposal is relevant both to increase the passenger traffic and to change the route network of ferry lines. There is an uneven congestion of individual directions of ferry lines and passenger terminals, which determines the need to select a finite number of states for the efficient operation of the ≪passenger terminal-ferry line≫ system. For the research of changes and assessments, it is proposed to use the process discretization methodology and the formation of a new circos plot intensity diagram. This study focuses on the passenger terminals in the Adriatic Sea and the existing route network in this region. As a result of the analysis, a set of points in time are selected that are characterized by various intensities and passenger traffic. For the selected set of values, a set of new intensity circos plot diagrams are constructed. On their basis, it is possible to analyze the mutual influence of the passenger terminals on each other to analyze the ferry transportation market and the number of shipping companies onit. New scientific approach can improve the quality of research and decision-making process for research of the ≪passenger terminal-ferry line≫ system. The practical results we can see in the form of circos plot diagrams for sea passenger transportation in the Adriatic Sea region and the proposed research methodology for research and operation analysis of the ≪ferry line - marine passenger terminal≫ system based on proces discretization

Forecasting of the route network of ferry and cruise lines based on simulation and intelligent transport systems

According to statistics, the marine passenger transportation sectors (both cruise lines and ferry lines) show a significant increase of passenger traffic and the intensity of ship routes. But new features of the conditions for passenger traffic growth require the development of new methodological transport models for cruise and ferry networks and new practical forecasting methods. Changes are observed in the fleet composition, mostly in the direction of increased. New approach for forecasting has to be based on the interaction of such systems as ≪city‒sea passenger port‒cruise and ferry lines. This condition now determines new need to describe the principles and forms of organization of maritime ferry networks and changes under the influence of the external environment. The object of the research is the Baltic Sea region and the existing route networks of cruise and ferry lines. Exploring this system, the usage of new mathematical apparatus based on correspondence matrices and agent-based simulation was justified for estimating the workload on transport infrastructure around the passenger port and for the existing ferry or cruise route network. The practical results of new simulation model, on the one hand, justify the need for a comprehensive study of the conditions for the formation of ferry and cruise route networks in changing conditions. On the other hand, these new results could improve the quality of decision-making process in forecasting the route network on the basis of the research of passenger traffic between systems city‒sea terminal-cruise line or ferry line