Network Situation Research Articles

Применение дифференциальных трансформаторов тока утечки в автоматизированной системе предотвращения пожаров в электросети промышленных объектов

Introduction. The occurrence of severe consequences of accidents in the electrical networks of industrial facilities depends on how correctly the automated systems for protection, detection and extinguishing of fire are selected. One of the reasons for the occurrence of a fire hazardous situation in electrical networks is the violation of the insulation and sheath of the electrical cable due to its aging or mechanical damage. Timely detection of the pre-fire state of electrical equipment is ensured by using an automated control system for electrical equipment using a differential leakage current transformer (DLCT). The automated control system for electrical equipment using a differential leakage current transformer is designed for early detection of the location of current leakage in the protected power and (or) lighting group (the electric motor and the power cable supplying it and other parts of the electrical equipment) and the issuance of information to the post with permanent duty personnel. Targets and goals. Improving fire safety of industrial facilities by using an automated control system for electrical equipment using a differential leakage current transformer. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used: analysis, synthesis, generalization, which were based on the provisions of the theory of probability and the theory of reliability. Research results. It is proposed to use DLCT for the protection of high-current electrical receivers in various sections of networks, taking into account selectivity as part of an automated fire prevention system. Conclusions. The use of an automated control system for electrical equipment upon detection of leakage currents makes it possible to: - timely detection of the place of occurrence of leakage currents, detection of the pre-fire condition of electrical cables by leakage currents; - transmission of information about the state of electrical cables to a higher level of the automated control system for electrical equipment; - disconnection of the cable line from power sources and power consumers on command from a programmable controller; - preventing the spread of fire along the cable route and along adjacent structural elements, enclosing structures and technological units; - activation of executive devices by the operator in an automated mode. Key words: accident, analysis, fire, explosion, risk, statistics, reliability, electrical equipment.

The Current Situation of Multiple Markets Network in the Tokyo Metropolitan Central Wholesale Market System

The Current Situation of Multiple Markets Network in the Tokyo Metropolitan Central Wholesale Market System

Research on Vehicle Network Security Situation Prediction Based on Improved CLPSO-RBF

In order to improve the accuracy and efficiency of the vehicle network security situation prediction, a prediction algorithm based on improved CLPSO-RBF is proposed. Firstly, for speeding up the optimization efficiency of CLPSO, a reasonable speed monitoring variable has been introduced. Secondly, we use the improved CLPSO algorithm to optimize the clustering radius of the RBF neural network, so that the optimal RBF network structure can be determined. Finally, we use the optimal RBF network to predict the security situation of the vehicle network. Simulation experiments have proved that the improved algorithm has higher accuracy and faster convergence rate in situation prediction, and has better prediction effects.

Network Situation Assessment of Host Node Based on Improved D-S Evidence Theory

In recent years, network security has become a hot spot of social concern. In order to avoid dependence on network training, improved D-S evidence theory is applied to the situation assessment of host. The fuzzy function is adopted to generate the BPA of the corresponding feature, and the weighting matrix is added to handle conflict evidences in the fusion process. Finally, the situation level of the host node is obtained. The effectiveness and feasibility of the method is proved by analyses of examples.

Analysis of the impact of vehicles on air pollution in Ulan-Ude

In recent years, urban ecology has become one of the key indicators for evaluating the citizens’ quality of life. The article studies the road network situation in Ulan-Ude, as well as the impact of vehicle emissions on the quality characteristics of the city’s atmospheric air from 2010 to 2019. The features of the application of various methods for assessing the level of pollutants from vehicles on the example of Ulan-Ude are described. The dynamics of changes in the main pollutants including sulfur dioxide, carbon monoxide, nitrogen monoxides, and other impurities, is revealed. Ways to reduce the air pollution from vehicles in Ulan-Ude are proposed.

Development of Self-Synchronized Drones’ Network Using Cluster-Based Swarm Intelligence Approach

Timing synchronization has a vital role in swarm drones' network (SDN) or a swarm of unmanned aerial vehicle (UAV) network. Current timing synchronization methods focus on enhancing single-hop skews which remarkably improve timing synchronization precision at this level. The improper clock of the drone system can cause interference, affect spectrum precision and interrupt the operation of the transceiver. In the drones' network, master drones' (MD) neighbor drone's timing synchronization approaches like Reference Broadcast System (RBS) realize a good performance. However, the requirement of one super drone with a large number of broadcasts for RBS makes it unrealistic to use in some situations like SDN network situation. Appropriate study and adjustments are needed to have real timing synchronization by eliminating the clocks drift and enhancing the timing synchronization precision. Therefore, a new self-timing synchronization approach is proposed in this paper where several MD drones can autonomously generate swarm clusters. The cluster head (CH) instigates a timing synchronization procedure starting with intra-Swarm cluster timing synchronization. The intermediate drones (ID) are elected between two swarm clusters to synchronize all drones in line with the inter-swarm cluster timing synchronization approach. The proposed approach is distributed and flexible to achieve high timing synchronization precision. The paper proposes a novel self-timing synchronization approach for in large scale semi-flat SND network architecture. Self-timing synchronization is swarm cluster-based and applicable for a huge number of master drones in SDN. One is the intra-Swarm cluster where the timing synchronization procedure starts with the CH to synchronize all CM. Secondly, in the inter-swarm cluster timing synchronization, two clusters are synchronized via intermediate drone (ID). However, the simulations demonstrated that in many cases all CHs are synchronized by the synchronized CHs from intra-swarm cluster timing synchronizations; this increased the system throughput and synchronization delay to about 75% compared to what we planned to achieve. Moreover, the simulation results also proved that the achieved synchronization precision can be used for position estimation and prediction with high accuracy.

Bilevel Multi-Objective Gray Wolf Algorithm Based on Packet Transport Network Optimization

Packet transport network (PTN), as an efficient transmission network technology in mobile communications in the big data era, is used by more and more communication operators. The existing PTN resource utilization rate is low, the network security is poor, so the existing PTN needs to be optimized in all aspects. For the optimization of the PTN, it is necessary to consider the decision of both the operator user and the service product supplier. Therefore, this paper proposes a bilevel multi-objective gray wolf algorithm based on PTN optimization problem. The operator user is the upper-level decision maker, and the objective function is to pay the product supplier the lowest cost. The product supplier is the lower-level decision maker, it mainly includes two major objective functions. The first objective function is to maximize the Label switching path overlap rate(LSPOR) evaluation score to solve the abnormal Label Switching Path (LSP) problem in the network, and the second is to maximize the committed bandwidth with utilizing rate(CBWUR) evaluation score to solve the problem of excessive Committed Information Rate(CIR) bandwidth usage in the network. According to the three scale network situation in Hubei, China, the improved multi-objective gray wolf algorithm is used to solve the PTN bilevel programming problem. The experimental results show that the model increases the utilization rate of network resources, and reduces the cost to be paid by the upper-level decision makers.

The Analysis of Higher Education Reform Strategy Based on the New Situation of Network

In the process of the continuous development of China's modern education, as an important part of the current education system, the quality of high-level education has a very important impact on the development of students and social economy. With the deepening of education reform, people pay more and more attention to the reform of higher education. In the process of the continuous development of modern information technology, promoting the effective integration of high-level education reform and modern information technology has become an important way for the development of modern higher education. Based on this, this paper deeply explores the reform strategy of Higher Education under the new situation of network, in order to improve the quality of higher education reform, and provide some valuable reference for promoting the further development of modern education.

СУЧАСНИЙ СТАН ІХТІОЦЕНОЗУ, ВИДОВОГО СКЛАДУ ТА ПОПУЛЯЦІЙ РИБ У БАСЕЙНАХ МАЛИХ РІЧОК ПРИП’ЯТСЬКОГО ПОЛІССЯ УКРАЇНИ — ШЛЯХИ РЕАБІЛІТАЦІЇ ТА ВІДНОВЛЕННЯ

Purpose. To study ichthyocenosis, species composition and current state of fish populations in small river basins from Pripyatske Polissiya of Ukraine, to propose the ways of their rehabilitation and restoration. Methodology. The study analysed data for last 60 years on the results of monitoring catches of fish wintering grounds of the first-order tributaries of the Pripyat River — right-bank rivers (rivers Slovechna, Sluch, Tina), as well as more than 300 flood plain lakes of the Desna River. Fish were caught by expeditionary methods using boats, seines and gill nets before ice formation in November-December. Methods generally accepted in ichthyology and ecology as well as surveys of anglers were used. Findings. The study showed the crisis state of the Upper Dnipro region, which was confirmed by the data on a significant decrease in fish productivity. The reasons for this are: non-observance of the basin approach in nature management, ineffective nature conservation measures, lack of measures for the rehabilitation of "natural localities" for the reproduction of native ichthyofauna. It should be noted that the creation of National Reserves and NaturalParks (Shatskyi, Pripyat-Stokhid, Rovenskyi, Polesskyi, Menskyi, etc.) did not significantly affect the general state of the problem, and rather has a declarative meaning. Since, although they still have some places with good reproduction conditions for native flora, fauna and valuable natural landscapes, however, this does not affect the general ecological situation in the river network. This is due to the fact that the surface runoff from territories without destructive human impact, which form the hydrochemical regime, is a fraction of a percent of the total amount. Accordingly, the following is observed: shallowing of river channels, smoothing of the bottom, siltation of wintering grounds, decrease in breeding stock number, degradation of spawning grounds and a lack of artificial stocking measures. This leads to a 10-fold decrease in fish catches in small rivers from Pripyatske Polissiya of Ukraine over the past 20 years. Originality. The study examines the problem of the relationship between the accessary river network and the fish productivity of rivers, including the Kуiv (receiving the flow of the DniprоRiver) and the upper reaches of the Kaniv (receiving the flow of the DesnaRiver) reservoirs. It outlines their role in the functioning of the trilateral natural complex located on the territory of Ukraine, Belarus and Poland. The possibility of creating the ichthyo-ecological reserve "Verhniy Dnipro on the territory of Ukraine on the base of existing protected areas within the Pripyat and Desnyansky hydroecological corridors is analysed. Practical Value. It has been proved that the creation of the "Verhniy Dnipro" will allow not only creating a stable base for the natural reproduction of native ichthyofauna in the upper reaches of the Dnipro reservoir cascade but providing high-quality water to the population of Kyiv and the Kyiv region. The concept of rehabilitation of reproduction and conservation of biodiversity of native ichthyofauna in the river network is proposed to maintain the stability of the biosphere, the implementation of which requires little funds. Keywords: ichthyofauna, ichthyocenosis, ichthyoecosystem, restoration of spawning grounds, PripyatRiver, DniproRiver, DesnaRiver.

Situation-Aware Resource Allocation for Multi-Dimensional Intelligent Multiple Access: A Proactive Deep Learning Framework

To meet the ever-increasing communication services with diverse requirements, situation-aware intelligent utilization of multi-dimensional communication resources is becoming essential. In this paper, considering a time-division-duplex downlink cellular scenario, a deep learning-based framework for multi-dimensional intelligent multiple access (MD-IMA) scheme is developed for beyond 5G and 6G wireless networks to meet the real-time and diverse quality of service (QoS) requirements by fully utilizing the available radio resources in heterogeneous domains. To achieve intelligent operation of MD-IMA, the proposed deep learning scheme is achieved based on the convergence of long short term memory (LSTM) and deep reinforcement learning (DRL). Specifically, an LSTM neural network is used to predict the long-term network dynamics and inference changes in QoS requirements of the MD-IMA. Meanwhile, a deterministic policy gradient (DDPG) algorithm, a model-free DRL technique, is adopted to optimize the multi-dimensional radio resource allocation in real-time by dynamically following the fluctuations of the network situation. With the aid of the DDPG algorithm, radio resource management for MD-IMA can be achieved efficiently with reduced processing latency as compared to the conventional model-based approaches. Furthermore, the effectiveness of our proposed deep learning framework for MD-IMA is validated through real-world cellular traffic data-sets. The experimental results demonstrate that the proposed scheme can outperform state-of-the-art algorithms.

Optimal valid path prediction method for inter-domain networks considering commercial relationships*

In the security research of large-scale inter-domain networks, it is often necessary to consider network state changes under the condition of failure or attack. In this case, the connection between ASes often switches between disconnection and recovery frequently, requiring frequent prediction of the AS-level path. Previous AS-level path prediction methods generally require detailed network information data and are complicated to calculate, which is difficult to meet the needs of such large-scale dynamic computation. In this paper, an optimal valid path prediction method for inter-domain networks considering commercial relations is proposed. This method predicts the optimal valid path according to the commercial relationships between ASes, which requires less network information data and low computational complexity. Through experiments, it is found that compared with the shortest path method commonly used in the security simulation of large-scale inter-domain networks, the prediction results are closer to the actual path of the network and can better reflect the actual path situation of the inter-domain network with lower computational complexity.

Experimental Review of Neural-Based Approaches for Network Intrusion Management

The use of Machine Learning (ML) techniques in Intrusion Detection Systems (IDS) has taken a prominent role in the network security management field, due to the substantial number of sophisticated attacks that often pass undetected through classic IDSs. These are typically aimed at recognising attacks based on a specific signature, or at detecting anomalous events. However, deterministic, rule-based methods often fail to differentiate particular (rarer) network conditions (as in peak traffic during specific network situations) from actual cyber attacks. In this paper we provide an experimental-based review of neural-based methods applied to intrusion detection issues. Specifically, we i) offer a complete view of the most prominent neural-based techniques relevant to intrusion detection, including deep-based approaches or weightless neural networks, which feature surprising outcomes; ii) evaluate novel datasets (updated w.r.t. the obsolete KDD99 set) through a designed-from-scratch Python-based routine; iii) perform experimental analyses including time complexity and performance (accuracy and F-measure), considering both single-class and multi-class problems, and identifying trade-offs between resource consumption and performance. Our evaluation quantifies the value of neural networks, particularly when state-of-the-art datasets are used to train the models. This leads to interesting guidelines for security managers and computer network practitioners who are looking at the incorporation of neural-based ML into IDS.

Grouping Service Chains of Multiple Flows in NFV-Based Networks

In Software Defined Networks (SDNs), flows usually request to be processed by a service chain (an ordered set of virtualized network services). SDN-enabled networks manage the routing and processing of flows by a large number of associated finer-granularity rules. These rules are maintained by switches in their local hardware such as Ternary Content Addressable Memories (TCAMs), which support high-speed parallel lookup on wildcard patterns. However, the capacity of hardware switches is limited to thousands because of their high requirements for cost and power. In order to avoid much slower matching by using software switches or even packet loss, we can group flows so that all matching rules can be placed in hardware switches. In such a grouping, all flows in each group match only one rule and will be forwarded to the same routing path, instead of each flow matching one rule. This will result in a longer delay because of processing by the longer newly-grouped service chain. In this paper, we efficiently group flows to minimize the total cost while satisfying the capacity constraint of the forwarding tables in hardware switches. We first prove the submodularity of our objective function and propose a corresponding performance-guaranteed solution. Additionally, we design an efficient heuristic solution based on the classic k-means algorithm. Furthermore, we include discussions on dynamic network situations (insertion, deletion, and update of flows) and an alternative objective. We also conduct real experiments on our testbed to indicate the practicality of our motivation. Extensive simulations are conducted to evaluate the performance of our proposed algorithms.

Network Coordination, Self-Regulation Mode, and Autonomous Control Space Planning of Microgrid

This paper investigates the network coordination, self-regulation mode, and autonomous control spatial planning of microgrid. To do this, the cooperative self-discipline model of multi-microgrid intelligent distribution network is constructed. Based on combing the power control of multi-microgrid with high penetration situation, the concept and characteristics of distribution network and self-regulation mode are defined according to the new requirements, and the basic framework and decision-making process of self-regulation mode are put forward. Further, the formulation method of autonomous spatial planning for multi-micronetworks is described. By constructing cooperative self-discipline space scheme and its reward and punishment mechanism, the network operation behavior is restrained and guided, and the control dimension of the distribution network is reduced. Finally, a typical case is given to illustrate the effectiveness of cooperative self-discipline supervision mode in reducing the space-time dimension of distribution network supervision.

Determining an efficient and precise choice set for public transport based on tracking data

To understand the route choices of public transport users, it is important to know the information available to them, and the context present at that moment. In fact, each choice situation in a transport network has different characteristics and possibilities, also depending on the current status of the transport network. In this regard, travel diaries based on tracking technologies can capture precise observations for a long term. In this work, we exploit a large-scale tracking dataset, collected through a mode detection algorithm, to understand route choices of public transport users. We propose a choice set generation algorithm, able to cover more than 94% of the collected trips without any computational constraint. We compare the users’ paths in the public transport network with different choice sets, under multiple performance indicators, including coverage, size, and fit. This latter is computed by the estimation of a Path Size Logit model. The use of Automatic Vehicle Location (AVL) data allows comparing the available paths in terms of public transport vehicles used. We also consider different information provisions of network conditions and disturbances (full knowledge, no knowledge and current knowledge), and study which information provision best represents the choice set inferred by the observed users’ behaviour. Estimating a Mixed Path Size Logit model, we identified high heterogeneity among the users in only a few aspects. Overall, a condition of no knowledge results as the best fit, i.e. users seem to take into account in a minor way the realized delays in the alternatives considered when deciding their public transport route.

Devising a Distributed Co-Simulator for a Multi-UAV Network

Practical evaluation of the Unmanned Aerial Vehicle (UAV) network requires a lot of money to build experiment environments, which includes UAVs, network devices, flight controllers, and so on. To investigate the time-sensitivity of the multi-UAV network, the influence of the UAVs’ mobility should be precisely evaluated in the long term. Although there are some simulators for UAVs’ physical flight, there is no explicit scheme for simulating both the network environment and the flight environments simultaneously. In this paper, we propose a novel co-simulation scheme for the multiple UAVs network, which performs the flight simulation and the network simulation simultaneously. By considering the dependency between the flight status and networking situations of UAV, our work focuses on the consistency of simulation state through synchronization among simulation components. Furthermore, we extend our simulator to perform multiple scenarios by exploiting distributed manner. We verify our system with respect to the robustness of time management and propose some use cases which can be solely simulated by this.

Multipath MMT-based approach for streaming high quality video over multiple wireless access networks

Demand for video streaming content and multimedia services have increased dramatically. Delivering high throughput and low delay video data are real challenges to provide and sustain high quality mobile video streaming services. One type of solution to provide quality of experience is to exploit multipath strategies by taking advantage of the multiple network interfaces that are currently available in most wireless devices. Our work aims at delivering contributions by exploring the advantages of multipath video streaming using the MPEG Media Transport protocol. MPEG Media Transport is a multimedia application layer protocol with the capability of hybrid media delivery. In this article, we propose a Content-Aware and Path-Aware (CAPA) scheduling strategy for this protocol, considering both video features and path condition. The performance of our CAPA proposal is evaluated using ns-3 DCE with different realistic multipath network scenarios. We evaluate the performance of CAPA over heterogeneous wireless networks under congested network and wireless lossy network conditions, which are common network situations with significant adverse effects on video quality. Our approach yields considerable video quality improvement in both scenarios compared to Path-Aware strategy and a simple scheduling strategy, called Evenly Splitting. For congested network scenario, CAPA could increase PSNR, respectively, by up to 4.25 dB (12.97%), and 7.22 dB (20.58%) compared to Path-Aware and Evenly Splitting strategies. It could also improve SSIM, respectively, by up to 0.033 (3.78%), and 0.102 (12.54%) compared to Path-Aware and Evenly Splitting strategies. For wireless lossy network scenario, CAPA increases PSNR, respectively, by up to 6.84 dB (20.30%), and 9.43 dB (30.32%) compared to Path-Aware and Evenly Splitting strategies. The proposed strategy also provides improvements in terms of SSIM, by up to 0.100 (12.72%), and 0.113 (14.23%) compared to Path-Aware and Evenly Splitting strategies, respectively. We also evaluate how proposed CAPA handles the streaming of different video bitrates under congested network conditions to provide sufficient video quality. Finally, we provide an initial validation of fairness of CAPA to confirm fair access to the resources available across all paths.

An MMT-Based Hierarchical Transmission Module for 4K/120fps Temporally Scalable Video

High frame rate (HFR) video is attracting strong interest since it is considered as a next step toward providing Ultra-High Definition video service. For instance, the Association of Radio Industries and Businesses (ARIB) standard, the latest broadcasting standard in Japan, defines a 120 fps broadcasting format. The standard stipulates temporally scalable coding and hierarchical transmission by MPEG Media Transport (MMT), in which the base layer and the enhancement layer are transmitted over different paths for flexible distribution. We have developed the first ever MMT transmitter/receiver module for 4K/120fps temporally scalable video. The module is equipped with a newly proposed encapsulation method of temporally scalable bitstreams with correct boundaries. It is also designed to be tolerant to severe network constraints, including packet loss, arrival timing offset, and delay jitter. We conducted a hierarchical transmission experiment for 4K/120fps temporally scalable video. The experiment demonstrated that the MMT module was successfully fabricated and capable of dealing with severe network constraints. Consequently, the module has excellent potential as a means to support HFR video distribution in various network situations.

Adaptive Networking Control Method for 5G VANET Nodes

With the development of Internet and Internet of Things, 5G has opened a new era of interconnection, and the technology of Vehicle Ad-hoc Network (VANET) has attracted great attention from all walks of life. However, the MAC protocol proposed at present has many drawbacks in supporting real-time services. To solve this problem, Hybrid MAC protocol, ET-MAC, came up with this paper by combining the Enhanced Distributed Channel Access (EDCA) and Time Division Multiple Access (TDMA) defined in IEEE 802.11p technology. Then, a performance comparison with the MAC protocol of the TDMA is performed in a simulation experiment to evaluate the function of the protocol. Finally, the simulation experiment proves that ET-MAC protocol is obviously superior to the MAC protocol based on TDMA is based on transmission delay, network throughput and packet loss rate, and more suitable for the current situation of high-density vehicle network in urban environment.

Real-Time Prediction of Electricity Distribution Network Status Using Artificial Neural Network Model: A Case Study in Salihli (Manisa, Turkey)

Electricity distribution networks are critical to the delivery of energy and the continuity of the economy. The healthy and efficient operation of these networks depends on the prediction of failures, their early detection and the rapid recovery of the resulting failures. The causes of failure are internal and external factors. Many studies in different sectors that use different techniques for failure prediction in the literature. The use of artificial intelligence techniques, which are becoming increasingly important today, in failure estimates; in terms of estimation success and effectiveness, it brings many privileges compared to other techniques. In this study, a status prediction model has been developed by using artificial neural network (ANN) technique for power outages and healthy working conditions of the electricity distribution network installed in Salihli district of Manisa province. In previous studies, using artificial intelligence techniques in the energy sector generally focused on one component of network, lifetime, energy demand estimation, battery life and goods failures. The effect of meteorological factors has not been studied on the distribution network situation using artificial intelligence techniques. In this study we use hourly power outages and hourly meteorological factors that cause failures or healthy conditions. It is aimed to effective risk management and make anticipation of power outage occurring in electricity transmission network, to make preventive maintenance for failures, to make suggestions for early intervention and shortening downtime and maintenance.