Message Flow Research Articles

Analytical method for analyzing message transmission processes in FDDI networks for digital substations

Objectives. To develop analytical approaches for the evaluation of probability-time characteristics and fiber distributed data interface (FDDI) network performance with the marker access method, thus enabling communication processes for digital electrical substations to be automated.Methods. The authors used theory reliability methods, random process theory, mass maintenance theory, the Laplace–Stieltjes transformation for inferring functional equations and the probability-time characteristics calculation for the information transfer processes with the occurring failures.Results. We conducted a numerical study of packet transfer processes between central processing stations in the FDDI network. We considered the processes of discrete information exchange between electronic devices in the system of electrical digital substations. These included the main technological operations and electrical digital substations operator performed when preparing reports. We described the different modes of operation, both for the individual electrical digital substation and for the system. The authors calculated node loading dependencies, FDDI network performance and temporal characteristics of the packet transfer processes on the incoming message flow intensity and the transmission medium reliability. We conducted a functional analysis of the FDDI networks on two fiber-optic rings which form the main and redundant path of data transfer between the network nodes, significantly increasing network resiliency. The objective of the study was to analyze the information transfer processes in FDDI networks with an accent on ensuring the transmission medium reliability.Conclusions. We were able to establish the existence of the critical operating network region, which leads to a sharp increase in node load and temporal characteristics, while performance reaches its maximum value and then sharply decreases. We propose the exchange of discrete messages to reflect the electronic devices state and information messages of operator between various remotely spaced electrical digital substation with the FDDI fiber-optic network.

Performance analysis of a blockchain-based messaging system implementation for air cargo supply chains

ABSTRACT The air cargo supply chain’s goal is to achieve fast shipment movement through multistage coordination among multiple air cargo stakeholders. The lack of transparency in the end-to-end supply chain and complex digital connectivity is what limits the traditional peer-to-peer communication flow in the fragmented air cargo industry. Blockchain technology can enhance communication transparency by streamlining multistage message flows via smart contracts and by acting as an intermediary to simplify digital connections among air cargo stakeholders. Despite the benefits of applying blockchain for air cargo messaging, as blockchain is an emerging technology, technical concerns in the areas of performance, privacy, and interoperability can hinder the practical adoption of the blockchain system. To address practical implementation concerns, a blockchain-based messaging system using the Algorand public blockchain is developed in our work to demonstrate the practicality of blockchain. Transaction data privacy can be protected by the encryption key exchange algorithm, which supports forward secrecy. Our experimental results provide practical performance insights into the Algorand blockchain for streamlined messaging from various aspects related to transaction submission, confirmation, and retrieval. Our results show that by leveraging the Algorand blockchain, our proposed system can offer scalable, efficient, reliable, and cost-effective communication channels for air cargo messaging.

The evolution of exceptions and investigations messaging and flows

This paper aims to provide the reader with information concerning the migration of exceptions and investigations (E&I) messages from the FIN MT format to the ISO 20022 format. In addition to this format migration, there are improvements that Swift will implement as part of their Case Manager product, such as smart routing of E&I messaging, automated confirmation of receipt messages and automated reminder generation. These enhancements will bring many benefits for financial institutions, such as cost reductions, due to a decrease in the number of cases being raised (ie no need to raise a case when playing the role of an intermediary for some investigation types), increased automation due to the new, structured elements of the ISO messaging format and clear investigation types, speed of resolution due to the smart routing logic and reduction of messaging legs required and fewer processes for operators as generation of the confirmation of receipt and the reminder/chaser messages are initiated by the Case Manager rather than requiring an operator to initiate these. This paper provides an overview of the ‘As is’ and ‘To be’ message formats and flows to enable the reader to fully understand the benefits that this migration will bring and the changes that will be mandated within the industry in the E&I space.

FlowX: Towards Explainable Graph Neural Networks via Message Flows.

We investigate the explainability of graph neural networks (GNNs) as a step toward elucidating their working mechanisms. While most current methods focus on explaining graph nodes, edges, or features, we argue that, as the inherent functional mechanism of GNNs, message flows are more natural for performing explainability. To this end, we propose a novel method here, known as FlowX, to explain GNNs by identifying important message flows. To quantify the importance of flows, we propose to follow the philosophy of Shapley values from cooperative game theory. To tackle the complexity of computing all coalitions' marginal contributions, we propose a flow sampling scheme to compute Shapley value approximations as initial assessments of further training. We then propose an information-controlled learning algorithm to train flow scores toward diverse explanation targets: necessary or sufficient explanations. Experimental studies on both synthetic and real-world datasets demonstrate that our proposed FlowX and its variants lead to improved explainability of GNNs.

An architecture to improve performance of software-defined optical networks

The software-defined optical network (SDON) is a revolutionary approach in the field of optical networks. The separation of the control plane and data plane in software-defined networking (SDN) provides enhanced security and simplified network administration. Nevertheless, performance and control plane scalability are significant issues in SDN. SDN performance can be evaluated using parameters such as burst loss, delay, channel occupancy, packet loss, throughput, and average response time. The number of messages exchanged between the data plane and the control plane is used as a metric to determine controller scalability. As the network load increases, the controller experiences a higher flow of messages. It causes delay and burst loss in transmitting the burst. Occasionally, bursts exceed the capacity of the fixed-sized burstifier and are discarded because it takes a long time to identify a suitable route for the burst. Hence, it is essential to minimize the volume of messages exchanged between the control plane and the data plane to improve performance and controller scalability. In this paper, we propose a scalable SDN optical network architecture that minimizes the number of messages exchanged between the data plane and the control plane. We proposed mechanisms like channel reservation, transmission cycles, and guard time between cycles to enhance both the speed and the quality of burst transmission. Prior to transmission, resources or channels are allocated to bursts to minimize the possibility of burst collision and loss. The data plane comprises an optical burst switching (OBS) network, and the flow table entries are periodically updated to minimize inter-plane communication. We perform simulations to evaluate and compare the performance of the proposed architecture with the existing state-of-the-art architecture reported in the literature. The proposed architecture performs better than the existing state-of-the-art in terms of metrics including burst loss, delay, channel occupancy, packet loss, throughput, average response time, and reduction in the number of messages exchanged between the data plane and the control plane. Experimental results indicate a 41% reduction in mean burst loss probability and a 40.5% reduction in mean burst sending delay compared to existing architectures. Additionally, 42.1% fewer messages are exchanged between the control plane and the data plane compared to the number of exchanged messages in existing architectures.

APPLICATION OF FUZZY MODELS TO CONTROL THE STREAM OF MESSAGES IN DATA TRANSMISSION INFRASTRUCTURES

The report presents one of the problems that arises in distributed data transmission infrastructures - node overload in data transmission when there is unstable communication between nodes. As a solution, a mechanism for controlling the message flow intensity based on a fuzzy model is proposed. The developed fuzzy control model has demonstrated its effectiveness in the network for collecting and transmitting quotes of financial instruments from various trading platforms to the information-analytical system of a financial company. Message queues at the sources have significantly decreased, while minimizing losses of quotes on unstable communication channels. Additionally, the possibility of more flexible adjustment of fuzzy control by expanding the number of metrics of input parameters of the fuzzy model has been explored.

Classification of Cyber Risks for Sea Vessel’s Voyage Cycle

Modern digital transformation in the shipping industry Shipping 4.0 is accompanied by increased automation and autonomy of the ship. This means strengthening the digitization of the ship's navigation systems (cyber systems). Therefore, the safe operation and navigation of modern ships depends on the adequate operation of cyber-physical systems, which are created on the basis of information and operational technologies. Such interconnections of ship information systems inevitably increase the vulnerability of the ship's digital navigation infrastructure to cyber-attacks. The article examines methods of controlling cyber risks of various origins, which determine the priority of flows, their internal interconnection and relations with the sources of messages that form them. The formalization of the qualitative and quantitative properties of information flows of the voyage cycle using matrix and graph-analytical modeling methods has been carried out. A decomposition of information was built and a cluster of information security of ship maneuvering control was defined, with its access closed for cyber-attacks. In the event of cyber-attacks, it is very important for the navigator to determine the moment of it’s appearance and the state of navigational devices of the bridge. Usually, the working state of the device is determined by the presence of electric current in it. However, only the presence of current in the device during cyber-attacks is not enough to assess its working condition. Inspection and verification of 11 navigation devices on the bridge takes a lot of time, which the navigator does not have in the event of a cyber-attack. Therefore, we propose to introduce an additional function of controlling the working status of each navigational device, and to transmit the results of all devices to a separate navigation cluster Mk of the navigation bridge via shielded cable lines of the vessel without other methods. This will make it possible to gather information about all the devices in one place on the bridge, protect it from cyberattacks, and quickly determine the operating status of all the navigation devices on the bridge. Descriptive modeling of the meaningful categories of the information space of maritime routes during cyberattacks was performed. The proposed classification of cyber risks has a logical structure based on the grouping of message flows, using the cluster connection coefficient between them. This makes it possible to assess the criticality of the impact of cyber-attacks on the ship and to choose ways to reduce their impact on the maneuvering of the ship in the voyage cycle.

Analysis of Pharmaceutical Companies' Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public.

The COVID-19 pandemic, a period of great turmoil, was coupled with the emergence of an "infodemic", a state when the public was bombarded with vast amounts of unverified information from dubious sources that led to a chaotic information landscape. The excessive flow of messages to citizens, combined with the justified fear and uncertainty imposed by the unknown virus, cast a shadow on the credibility of even well-intentioned sources and affected the emotional state of the public. Several studies highlighted the mental toll this environment took on citizens by analyzing their discourse on online social networks (OSNs). In this study, we focus on the activity of prominent pharmaceutical companies on Twitter, currently known as X, as well as the public's response during the COVID-19 pandemic. Communication between companies and users is examined and compared in two discrete channels, the COVID-19 and the non-COVID-19 channel, based on the content of the posts circulated in them in the period between March 2020 and September 2022, while the emotional profile of the content is outlined through a state-of-the-art emotion analysis model. Our findings indicate significantly increased activity in the COVID-19 channel compared to the non-COVID-19 channel while the predominant emotion in both channels is joy. However, the COVID-19 channel exhibited an upward trend in the circulation of fear by the public. The quotes and replies produced by the users, with a stark presence of negative charge and diffusion indicators, reveal the public's preference for promoting tweets conveying an emotional charge, such as fear, surprise, and joy. The findings of this research study can inform the development of communication strategies based on emotion-aware messages in future crises.

ИСПОЛЬЗОВАНИЕ АЛГОРИТМОВ ОБРАБОТКИ ЕСТЕСТВЕННОГО ЯЗЫКА И МАШИННОГО ОБУЧЕНИЯ В СОВЕРШЕНСТВОВАНИИ УПРАВЛЕНЧЕСКИХ РЕШЕНИЙ

In the current conditions of growth and development of digital technologies, the latest tools and methods of supporting management decision-making are becoming increasingly important. In this paper, we will consider natural language processing algorithms that help analyze large amounts of unstructured text data and machine learning algorithms that analyze data and, based on them, perform forecasting, optimization and automation tasks. The paper analyzes the main tasks that can be solved when using machine learning and natural language processing methods together, namely: achieving high forecasting accuracy, processing and analyzing big data, automating routine tasks, managing risks, optimizing resource allocation, improving customer interaction. We analyzed the main software tools for the implementation of these tasks, the features of their application, settings and work parameters. Built a communication diagram based on the flow of messages between these tasks. Separately, the main software tools that are used to solve natural language processing and machine learning problems are analyzed in detail, their strengths and the possibilities of mutual integration or complement are described. A diagram of the IT infrastructure deployment was built based on the main software tools for natural language processing and machine learning methods.

Analysis of information security analysis of a distributed system based on message flow

SIEM systems are considered and a comparative analysis of existing correlation mechanisms is carried out. A new mechanism for unique fields is proposed. The necessity of using the unique fields mechanism to improve the performance of the system as a whole is substantiated. Keywords information security, security events, correlation, unique fields

Improving Arabic Spam classification in social media using hyperparameters tuning and Particle Swarm Optimization

Online social networks continue to evolve, serving a variety of purposes, such as sharing educational content, chatting, making friends and followers, sharing news, and playing online games. However, the widespread flow of unwanted messages poses significant problems, including reducing online user interaction time, extremist views, reducing the quality of information, especially in the educational field. The use of coordinated automated accounts or robots on social networking sites is a common tactic for spreading unwanted messages, rumors, fake news, and false testimonies for mass communication or targeted users. Since users (especially in the educational field) receive many messages through social media, they often fail to recognize the content of unwanted messages, which may contain harmful links, malicious programs, fake accounts, false reports, and misleading opinions. Therefore, it is vital to regulate and classify disturbing texts to enhance the security of social media. This study focuses on building an Arabic disturbing message dataset extracted from Twitter, which consists of 14,250 tweets. Our proposed methodology includes applying new tag identification technology to collected tweets. Then, we use prevailing machine learning algorithms to build a model for classifying disturbing messages in Arabic, using effective parameter tuning methods to obtain the most suitable parameters for each algorithm. In addition, we use particle swarm optimization to identify the most relevant features to improve the classification performance. The results indicate a clear improvement in the classification performance from 0.9822 to 0.98875, with a 50% reduction in the feature set. Our study focuses on Arabic spam messages, classifying spam messages, tuning effective parameters, and selecting features as key areas of investigation.

SKAFS: Symmetric Key Authentication Protocol With Forward Secrecy for Edge Computing

The IoT-edge-cloud paradigm enables resource-constrained IoT devices to offload their computation, thereby meeting the required quality-of-service for real-time applications. However, the deployment of IoT devices in public places such as smart cities exposes them to various security threats, including physical attacks. To address these security concerns, we propose a Physical Unclonable Function (PUF) based IoT-edge-cloud Symmetric Key Authentication protocol with Forward Secrecy (), which ensures the anonymity of transacting IoT devices, resilience to desynchronization-based denial-of-service attacks, and PUF modeling attacks. To evaluate the security of our protocol, we conduct a formal security analysis using the automated AVISPA tool. In addition, based on the indistinguishability property of the PUF, we formally prove that is secure under the CK-adversary model. Moreover, we implement the protocol using socket programming between a Raspberry Pi 1 as an IoT device, a Raspberry Pi 4 as an IoT gateway, and an 11th Gen Intel Core i7-11800H laptop as the CA to simulate the message flow between the protocol entities in a real-time experiment and calculate its end-to-end latency. Finally, we compare with other PUF-based protocols in terms of computation time, communication cost, and storage requirements.

Controller placement with critical switch aware in software-defined network (CPCSA).

Software-defined networking (SDN) is a networking architecture with improved efficiency achieved by moving networking decisions from the data plane to provide them critically at the control plane. In a traditional SDN, typically, a single controller is used. However, the complexity of modern networks due to their size and high traffic volume with varied quality of service requirements have introduced high control message communications overhead on the controller. Similarly, the solution found using multiple distributed controllers brings forth the 'controller placement problem' (CPP). Incorporating switch roles in the CPP modelling during network partitioning for controller placement has not been adequately considered by any existing CPP techniques. This article proposes the controller placement algorithm with network partition based on critical switch awareness (CPCSA). CPCSA identifies critical switch in the software defined wide area network (SDWAN) and then partition the network based on the criticality. Subsequently, a controller is assigned to each partition to improve control messages communication overhead, loss, throughput, and flow setup delay. The CPSCSA experimented with real network topologies obtained from the Internet Topology Zoo. Results show that CPCSA has achieved an aggregate reduction in the controller's overhead by 73%, loss by 51%, and latency by 16% while improving throughput by 16% compared to the benchmark algorithms.

Determination of probabilistic-time characteristics when studying load properties in computer networks

The article is devoted to the determination of probabilistic-time characteristics when studying the properties of load in computer networks. The proposed approach to determining these characteristics is based on consideration of the probabilistic-time characteristics of a single-line queuing system with a geometric input flow and an exponential distribution of service time with a limited queue (g/M/1/N). The work takes into account that the flow of messages at the input of a subscriber station, hub or net-work controller serving a multipoint network with a “ring” or “tree” structure differs from Poisson. The proposed analysis method (G/M/1/N type system), called the parameter method, is illustrated with examples of various types of queuing systems.

Obywatele w środowisku informacji politycznej: obszary badań i wyzwania metodologiczne

Citizens in Political Information Environment: Areas of Study and Methodological The aim of this paper is to offer a theoretical background for studies on political communication that focus on the role of the audience in the flow of political messages. We start with normative models of democracy and their demands upon citizens and the concept of political information environment. By employing these two concepts, we are able to focus on two dimensions of citizens’ roles in political communication, that is expectations towards media users who are perceived here as citizens, as well as citizens’ expectations towards news media disseminating political information. A combination of the aforementioned concepts provides us with a theoretical background for studies on actual news media consumption and related phenomena, such as news avoidance and selective media exposure.

Robust Design of Interdependent Networks Considering Intra-Network Support Flow

Cyber-physical systems comprise multiple interdependent networks supporting each other. This paper considers a combination of power grid and computer network as a typical instance of the interdependent networks. A node in an interdependent network fails if it cannot receive the required volume of support from the nodes in the other interdependent network. Meanwhile, each node supporting the other network is required to receive a sufficient volume of intra-network flow named “support flow”, which corresponds to the flow of electric power in the power grid and the flow of control messages in the computer network. Each sink node of the support flow must terminate a sufficient volume of support flow generated at source nodes in the same network in order to achieve the support of the other network. This means that the support flow is one of the essential elements to make the interdependent networks operable. The conditions related to the support flow increases the possibility of a small initial node failure invoking a cascading failure with a wide range. This paper proposes a robust design method for the interdependent networks to maximize the robustness against the cascading failures while assuring satisfaction of the constraints on the support flow. The method can derive the interdependency to minimize the impact of the cascading failures induced from a given set of possible initial node failures when the configuration of the respective interdependent networks is specified beforehand. The robust design problem is formulated using a mixed integer linear programming model and two kinds of metaheuristic methods are proposed to solve the problem efficiently even if a great number of initial node failures are specified. Several characteristics of the risk of the cascading failures are revealed from the results of the robust design. Simulation experiments demonstrate the effectiveness of the proposed metaheuristic methods.

Conflict Mitigation Framework and Conflict Detection in O-RAN Near-RT RIC

The steady evolution of the Open RAN concept sheds light on xApps and their potential use cases in O-RANcompliant deployments. There are several areas where xApps can be used that are being widely investigated, but the issue of mitigating conflicts between xApp decisions requires further in-depth investigation. This article defines a conflict mitigation framework (CMF) built into the existing O-RAN architecture; it enables the Conflict Mitigation component in O-RAN's Near- Real-Time RAN Intelligent Controller (Near-RT RIC) to detect and resolve all conflict types defined in the O-RAN Alliance's technical specifications. Methods for detecting each type of conflict are defined, including message flows between Near-RT RIC components. The suitability of the proposed CMF is proven with a simulation of an O-RAN network. Results of the simulation show that enabling the CMF allows balancing the network control capabilities of conflicting xApps to significantly improve network performance, with a small negative impact on its reliability. It is concluded that defining a unified CMF in Near-RT RIC is the first step towards providing a standardized method of conflict detection and resolution in O-RAN environments.

CoGNN: An Algorithm-Hardware Co-Design Approach to Accelerate GNN Inference With Minibatch Sampling

As a new algorithm of graph embedding, Graph Neural Networks (GNNs) have been widely used in many fields. However, GNN computing has the characteristics of both sparse graph processing and dense neural network, which make it difficult to be deployed efficiently on the existing graph processing accelerators or neural network accelerators. Recently, some GNN accelerators have been proposed, but the following challenges have not been fully solved: 1) The mini-batch GNN inference scenario has the potential of software and hardware co-design, which can bring 30% computation amount reduction, and this is not well utilized. Besides, the cost of Message Flow Graph construction is large and may account for more than 50% of the total delay. 2) The feature aggregation has a large amount of data access and relatively small amount of computation, which leads to low on-chip data reuse, only 10% of dense computing. 3) Without the optimization of sparse computing units, simple memory bank and cross bar architecture can easily lead to bank access conflict and load imbalance, reducing the utilization of computing units to less than 60%. In order to solve the above problems, we propose a algorithm-hardware co-design scheme to accelerate GNN inference, which includes three technologies: 1) A reuse-aware sampling method is proposed for mini-batch inference scenarios, which reduces 30% of the calculation and improves the on-chip reusability of local data. 2) Through the node-wise parallelism-aware quantization, the features and weights are quantized to integers with eight or four bits, which reduces the amount of memory access by at least four times. 3) An accelerator supporting the above technologies is designed and evaluated, and different operations are supported by the sampling-inference integration architecture. The multi-bank on-chip memory pool is designed to support data reuse, and edge stream reordering is used to reduce data access conflicts, improving the utilization of computing units by 1.5x. Combined with the above technologies, the experiments show that our design achieves 9.2× speedup and 29× energy efficiency improvement compared with Deep Graph Library framework running on servers equiped with CPU and GPU.

Gene based message passing for drug repurposing

The medicinal effect of a drug acts through a series of genes, and the pathological mechanism of a disease is also related to genes with certain biological functions. However, the complex information between drug or disease and a series of genes is neglected by traditional message passing methods. In this study, we proposed a new framework using two different strategies for gene-drug/disease and drug-disease networks, respectively. We employ long short-term memory (LSTM) network to extract the flow of message from series of genes (gene path) to drug/disease. Incorporating the resulting information of gene paths into drug-disease network, we utilize graph convolutional network (GCN) to predict drug-disease associations. Experimental results showed that our method GeneDR (gene-based drug repurposing) makes better use of the information in gene paths, and performs better in predicting drug-disease associations.

A comparison of the performance of the ad hoc on-demand distance vector protocol in the urban and highway environment

In recent years, the vehicular ad hoc network (VANET) has received great attention, as it is involved in the design of the intelligent transportation system (ITS). The VANET network includes message flows from vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) where the network is propped by wireless communication technology, such as IEEE 1609 WAVE and IEEE 802.11P. The VANET network implementation faces challenges, and one of these challenges is the design of routing protocols that transfer reliable and efficient packets from vehicle to vehicle. In VANET, steering is a challenging task in the highway and urban environment. Therefore, this paper presents an assessment of ad hoc on-demand distance vector protocol (AODV) performance in the highway and urban environment and to study the effect of vehicle density on protocol performance. The AODV protocol was simulated by MATLAB. In this study, the performance of AODV protocol was evaluated through four measures, namely, packet delivery ratio (PDR), overhead, end-to-end (E2E) delay, and dropped packets. The study in our paper showed that the best performance of the AODV protocol is in an environment where vehicle speed and vehicle density are low.