Network Failures Research Articles

Deadly acceleration in dehydration of Eucalyptus viminalis leaves coincides with high-order vein cavitation.

Xylem cavitation during drought is proposed as a major driver of canopy collapse, but the mechanistic link between hydraulic failure and leaf damage in trees is still uncertain. Here, we used the tree species manna gum (Eucalyptus viminalis) to explore the connection between xylem dysfunction and lethal desiccation in leaves. Cavitation damage to leaf xylem could theoretically trigger lethal desiccation of tissues by severing water supply under scenarios such as runaway xylem cavitation, or the local failure of terminal parts of the leaf vein network. To investigate the role of xylem failure in leaf death, we compared the timing of damage to the photosynthetic machinery (Fv/Fm decline) with changes in plant hydration and xylem cavitation during imposed water stress. The water potential at which Fv/Fm was observed to decline corresponded to the water potential marking a transition from slow to very rapid tissue dehydration. Both events also occurred simultaneously with the initiation of cavitation in leaf high-order veins (HOV, veins from the third order above) and the analytically derived point of leaf runaway hydraulic failure. The close synchrony between xylem dysfunction and the photosynthetic damage strongly points to water supply disruption as the trigger for desiccation of leaves in this hardy evergreen tree. These results indicate that runaway cavitation, possibly triggered by HOV network failure, is the tipping agent determining the vulnerability of E. viminalis leaves to damage during drought and suggest that HOV cavitation and runaway hydraulic failure may play a general role in determining canopy damage in plants.

Overcoming knowledge network failures: evidence from a French interregional scientific alliance in green chemistry

ABSTRACT Competition in research has led to the emergence of new regional spaces and interorganizational arrangements, designed to improve their participants’ visibility, at the crossroads of regional systems and global exchanges. This article investigates the determinants of the formation of interregional scientific alliances through the in-depth case study of a federation of research laboratories in green chemistry in western France. Working from bibliometric and qualitative data and using a network analysis approach to evidence interurban network failures in this scientific field, we examine the federation’s geographical scope. We highlight the decisive role of (1) the spatial organization of the field (structure effect), (2) interpersonal ties between consortium members (network effect) and (3) national scientific guidelines and incentives (policy effect). We propose a novel mixed-method approach to understand interregional alliances and defend its potential to uncover the importance of interregional institutional arrangements in prompting interurban knowledge exchanges.

Analysis of the effect of node attack method on cascading failures in multi-layer directed networks

In order to avoid large-scale cascading failures, we propose a cascading failure model with adjustable parameters in multi-layer directed dependent networks with target nodes. The model sets the initial load and limit capacity, designs load delay judgment mechanism, constructs a load distribution strategy, and analyzes the cascading failure conditions. Finally, the simulation comparison between our method and other four redistribution methods shows that our method can improve the robustness and efficiency of the network. Based on our redistribution method, the same number of nodes are attacked by four attack methods, showing that the cost of attacking the driver node set is small, but can cause cascading failure on a large scale.

Design of network monitoring system based on LibreNMS using Line Notify, Telegram, and Email notification

Institut Teknologi Telkom Jakarta (IT Telkom Jakarta) is an educational institution that supports student activities and provides internet capabilities to implement online learning systems. As the number of students increases with every year, so does the use of the internet and intranet networks and the experienced network problems. A network administrator is a person who is responsible for managing a computer network. Network administrators usually face network problems in monitoring network devices. This is because the process and operation are done manually. This means network administrators need direct access to the location to monitor all resources. Therefore, a network device monitoring system is needed to manage network devices centrally. This research focuses on the problem of monitoring network devices using open-source tools and software. Based on the implementation results, free network monitoring software such as LibreNMS can track and monitor all devices in all conditions and notify the active device condition in case of network failure such as up, down, reboot to the administrator via Line Notify, Telegram, and Email. With this network monitoring system, IT Telkom Jakarta is expected to be able to implement an integrated and well-monitored internet network system. Besides, the results of this study also produce real-time data on bandwidth usage, logging problems, and resource availability. This can significantly improve network availability and security.

Analysis of Seasonality and Causes of Equipment and Facility Failures in Electric Power Distribution Networks

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

HPIM‐DM: A fast and reliable dense‐mode multicast routing protocol

AbstractThe Hard‐state Protocol Independent Multicast—Dense Mode (HPIM‐DM) multicast routing protocol is proposed. HPIM‐DM is a hard‐state version of PIM‐DM that overcomes its poor convergence times and lack of resilience to replay attacks. Like PIM‐DM, HPIM‐DM is meant for dense networks and supports its operation on a unicast routing protocol and reverse path forwarding. However, routers maintain sense of the multicast trees at all times, allowing fast reconfiguration in the presence of network failures or unicast route changes. This is achieved by (i) keeping information on all upstream neighbours from which multicast data can be received, (ii) ensuring the reliable transmission and sequencing of control messages, and (iii) synchronizing the routing information immediately when a new router joins the network. The correctness of the protocol was extensively validated using model checking and logical reasoning. The protocol was fully implemented in Python, and the implementation is publicly available. Finally, we show both theoretically and experimentally that HPIM‐DM has much better convergence times than PIM‐DM.

Adaptive Route Sink Relocation Using Cluster Head Chain Cycling Model in WSN

Wireless Sensor Networks (WSN) have revolutionized the processes involved in industrial communication. However, the most important challenge faced by WSN sensors is the presence of limited energy. Multiple research investigations have been conducted so far on how to prolong the energy in WSN. This phenomenon is a result of inability of the network to have battery powered-sensor terminal. Energy-efficient routing on packet flow is a parallel phenomenon to delay nature, whereas the primary energy gets wasted as a result of WSN holes. Energy holes are present in the vicinity of sink and it is an important efficient-routing protocol for WSNs. In order to solve the issues discussed above, an energy-efficient routing protocol is proposed in this study named as Adaptive Route Decision Sink Relocation Protocol using Cluster Head Chain Cycling approach (ARDSR-CHC2H). The proposed method aims at improved communication at sink-inviting routes. At this point, Cluster Head Node (CHN) is selected, since it consumes low energy and permits one node to communicate with others in two groups. The main purpose of the proposed model is to reduce energy consumption and define new interchange technology. A comparison of simulation results demonstrates that the proposed algorithm achieved low cluster creation time, better network error and high Packet Delivery Rate with less network failure.

Analysis and identification of network failures in the information network

This article shows that each information system has its own characteristics, which are determined by the scope of its application. The importance and responsibility of tasks solved using real-time systems have led to high requirements for the reliability of these systems, and the failure of the entire information system or its individual components can lead to negative consequences. Network failure recognition is based on the principle of determining the nature of network failures based on the product system. This principle is implemented through a dialogue procedure within the framework of the Bayesian approach, which allows you to accumulate information coming from various sources in order to confirm (not confirm) a certain hypothesis. A logical conclusion management strategy was developed, the control parameters of which are the current probability of the truth of the hypotheses, the limits of their change, and the weight of the signs associated with these hypotheses. Accounting for the current probability of hypotheses and the limits of their change allows in the process of conclusion, firstly, to focus attention on the most promising (probable) hypothesis, and secondly, to stop the conclusion after reaching the upper threshold, which will reduce the number of signs to be checked, and thus shorten the dialogue time yourself. In addition, having reached the lower threshold, the hypothesis is rejected as implausible and no longer participates in the conclusion process, which also leads to a reduction in recognition time. Accounting for the weights of signs in the process of logical inference allows you to first of all check those signs that maximize the probability of the plausibility of hypotheses. In general, the developed strategy, unlike the classical scheme, generates a purposeful process of testing the plausibility of hypotheses, which leads to a reduction in recognition time.

A comparative analysis of the robustness of multimodal comprehensive transportation network considering mode transfer: A case study

<abstract> <p>China has built a nationwide transportation network, but there needs to be a smooth connection and transfer between different modes. Five networks are constructed to explore the characteristics of a multimodal comprehensive transportation network (CNet) in Jiangsu Province based on the optimized modeling method and multisource data. Statistical and robustness characteristics are analyzed for CNet and other single-mode networks including the highway, railway, navigation channel and airway networks (HNet, RNet, NNet and ANet, respectively). The research results show following: (ⅰ) In Jiangsu, CNet, HNet, RNet and NNet are not scale-free networks and do not have small-world properties. However, ANet is the opposite. (ⅱ) The five networks in Jiangsu are robust to the random attack and their robustness changes during the attack. However, their robustness is different under different calculated attacks. For all attack strategies, CNet is the most robust. (ⅲ) In Jiangsu, the three optimized methods enhance the robustness significantly. The network failure is delayed by 12.34, 2.79 and 2.44%, respectively. The average connectivity degree is improved by 265.69, 52.95 and 32.54%, respectively. The more hubs there are with powerful transfer capacity, the stronger the network robustness. The results reveal the key points of the construction of a multimodal comprehensive transportation system and can guide the design and optimization of it.</p> </abstract>

MCAD: A Machine Learning Based Cyberattacks Detector in Software-Defined Networking (SDN) for Healthcare Systems

The healthcare sector deals with sensitive and significant data that must be protected against illegitimate users. Software-defined networks (SDNs) are widely used in healthcare systems to ensure efficient resource utilization, security, optimal network control, and management. Despite such advantages, SDNs suffer from a major issue posed by a wide range of cyberattacks, due to the sensitivity of patients’ data. These attacks diminish the overall network performance, and can cause a network failure that might threaten human lives. Therefore, the main goal of our work is to propose a machine learning-based cyberattack detector (MCAD) for healthcare systems, by adapting a layer three (L3) learning switch application to collect normal and abnormal traffic, and then deploy MCAD on the Ryu controller. Our findings are beneficial for enhancing the security of healthcare applications by mitigating the impact of cyberattacks. This work covers the testing of MCAD using a wide spectrum of both ML algorithms and attacks, and provides a performance comparison for every pair of ML algorithms/attacks to illustrate the strengths and weaknesses of different algorithms against a specific attack. The MCAD shows impressive performance, achieving an F1-score of 0.9998 and of 0.9882 on normal and attack classes, respectively, which implies a high level of reliability. MCAD also achieved 5,709,692 samples per second on throughput, which reflects a high-performance real-time system with respect to complexity. Additionally, it showed a positive impact on the network KPIs by increasing the throughput by 609%, and decreasing delay and jitter by 77% and 23%, respectively, compared to attack results.

A Backhaul Adaptation Scheme for IAB Networks Using Deep Reinforcement Learning With Recursive Discrete Choice Model

Challenges such as backhaul availability and backhaul scalability have continued to outweigh the progress of integrated access and backhaul (IAB) networks that enable multi-hop backhauling in 5G networks. These challenges, which are predominant in poor wireless channel conditions such as foliage, may lead to high energy consumption and packet losses. It is essential that the IAB topology enables efficient traffic flow by minimizing congestion and increasing robustness to backhaul failure. This article proposes a backhaul adaptation scheme that is controlled by the load on the access side of the network. The routing problem is formulated as a constrained Markov decision process and solved using a dual decomposition approach due to the existence of explicit and implicit constraints. A deep reinforcement learning (DRL) strategy that takes advantage of a recursive discrete choice model (RDCM) is then proposed. The incorporation of the RDCM helps to address the challenges of backhaul availability and scalability with the aim to increase robustness to backhaul failure in IAB networks. The advantage of applying the RDCM for this problem is that it incorporates choice aversion from prospect theory and the reward is not the only factor affecting the learning rates, but also the punishment. The performance of the proposed algorithm was compared to that of conventional DRL, i.e., without RDCM, and generative model-based learning (GMBL) algorithms. The simulation results of the proposed approach reveal risk perception by introducing certain biases on alternative choices. For instance, applying cumulative prospect theory in the context of route choices reveal different node behaviors as the learning rate and noise levels are varied. The simulation results showed that the proposed algorithm provides better throughput and delay performance over the two baselines.

Enhanced E-commerce Fraud Prediction Based on a Convolutional Neural Network Model

The rapidly escalating sophistication of e-commerce fraud in recent years has led to an increasing reliance on fraud detection methods based on machine learning. However, fraud detection methods based on conventional machine learning approaches suffer from several problems, including an excessively high number of network parameters, which decreases the efficiency and increases the difficulty of training the network, while simultaneously leading to network overfitting. In addition, the sparsity of positive fraud incidents relative to the overwhelming proportion of negative incidents leads to detection failures in trained networks. The present work addresses these issues by proposing a convolutional neural network (CNN) framework for detecting e-commerce fraud, where network training is conducted using historical market transaction data. The number of network parameters reduces via the local perception field and weight sharing inherent in the CNN framework. In addition, this deep learning framework enables the use of an algorithmic-level approach to address dataset imbalance by focusing the CNN model on minority data classes. The proposed CNN model is trained and tested using a large public e-commerce service dataset from 2018, and the test results demonstrate that the model provides higher fraud prediction accuracy than existing state-of-the-art methods.

Two-Stage Stochastic Programming Model for Improving Transportation Network Resilience of Relief Supplies in Sequential Hazards Scenario

Cascading failure of road transport networks caused by complex sequential natural hazards adversely affects the use of pre-positioning relief supplies. Therefore, it is vital to improve the transportation network resilience of relief supplies by using an optimization model. The purpose of this paper is to pre-position and distribute relief supplies in uncertain scenarios of sequential hazards. A two-stage stochastic programming model to maximize the total resilience is proposed to provide an optimal plan against the uncertain impact of sequential natural hazards. The combined impact of the Jiuzhaigou 7.0 magnitude earthquake and its associated landslide is a prototype disaster scenario for the implementation of the method proposed in this paper. The model is solved by a neighborhood search-based genetic algorithm (NS-based GA), which has both the global search capability of a genetic algorithm and the local search capability of a large-scale neighborhood search algorithm, can improve the solution finding capability. A case study focusing on finding the optimal solution for the pre-position and distribution of relief supplies in the sequential hazard of Jiuzhaigou earthquake is conducted to illustrate the validity of the proposed model.

Research of virtualized log management system in communication dispatching center of power grid

Logs happen every day. They record all kinds of events of current network and its management facilities. Communication dispatchers can use it to aware network or facility failures. In the purpose of improving awareness of communication network and its management facilities, this research is focusing on a log management system for different types of communication devices and network management servers. From log system architecture, to actual situations of log collection. Then, the distribution of system, from bare-metal servers to virtual machines. Most a virtualized log management system is deployed and tested in a dispatching center of power grid.

Methodology for ensuring the sustainability of the information direction in the conditions of information and technical influences on the elements of the memory communication network

The article presents a methodology for ensuring the stability of the information direction (vector) in the conditions of information and technical destructive impacts (ITDI) on the elements of the memory communication network. The possibility of realizing the transition from the approach of object protection of correspondents and elements of communication networks to the approach of group protection is substantiated, as well as increasing the reliability of data transmission under conditions of blocking the spread of destructive ITI and corresponding nodes of their sources. It is shown that ensuring the stability of the information direction (vector) is achieved, firstly, by increasing the security of correspondents and elements of the communication network due to the controlled physical separation of the paths for receiving and transmitting information directions (vectors), which excludes the presence of a physical path for the implementation of destructive ITEs, and, secondly, by increasing the probability of data transmission, reducing the time of their transmission and the load on the bandwidth of communication lines in the conditions of network reconfiguration, failures and overloads of its elements due to the use of in the process of protecting against destructive ITOs.

Интеллектуальная система управления сельскохозяйственными роботами: формирование структуры

Digital technologies introduced into agriculture are aimed at improving the efficiency of agricultural production. Agricultural robots carrying out technological operations, monitoring the field, crops, or crop yields may reduce their productivity, quality and safety of operation due to the geographical distance from the cloud storage, low bandwidth and network unreliability, significant delays and failures in data transmission. To increase the performance of units with unmanned robotic technical means for agricultural purposes, to ensure the high quality of their technological operations, as well as their safe operation, an intelligent control system using edge computing technologies based on Edge Computing is proposed. During the research, the authors used methods of complex structural-dynamic analysis and an expert-analytical method of information processing. As a result of the research, a structural and functional diagram of an intelligent control system for agricultural units with robotic technical means has been proposed to ensure centralized control of the technological process. Agricultural machines adjust the working tools and their operation process using built-in autonomous control systems that transmit data to the control system to generate commands. The operation of the working tools is adjusted in case of serious failures in the unit operation. The implementation of the Edge Computing concept in digital agriculture will reduce the amount of transmitted information and the load on the data transmission network, without reducing the technological process quality in case of failures in the operation of farm machines and embedded systems.

МОДЕЛЬ ДЛЯ МОНІТОРИНГУ ТА ОЦІНКИ ЕФЕКТИВНОСТІ ЕЛЕКТРОЕНЕРГЕТИЧНОЇ СИСТЕМИ КРАЇНИ

Abstract. The research focuses on addressing the challenges of evaluating the effectiveness of modern power systems in countries with different configurations. Given the growing interest of con-sumers in the impact on the electricity supply schedule, the research is divided into two main parts. Firstly, a model has been developed to assess the operational efficiency of the power system. Secondly, a method for analyzing the efficiency of the energy system has been developed, based on a model of changes in energy system quality indicators. The presented model takes into account energy system characteristics such as the probability of network failure, normalized frequency of electricity, and the volume of consumed and supplied electrical energy. It is expected that this model will provide a more accurate assessment of the efficiency of the energy system under normal operating conditions. Additionally, a method for analyzing the operational efficiency of the energy system has been developed, based on a model of changes in energy system quality indicators. The uniqueness of this method lies in the introduction of a new comprehensive indicator of energy system efficiency, deter-mined by the convolution method of partial indicators, namely: the volume of electricity supply, supply quali-ty, and electricity supply efficiency. The reliability of the model is confirmed by calculations of the daily schedule of supply and load. Keywords: electric power system, electricity supply efficiency, evaluation of power supply system effi-ciency, electricity indicators, load schedule, consumer management, electricity supply quality.

МОДЕЛЬ ДЛЯ МОНІТОРИНГУ ТА ОЦІНКИ ЕФЕКТИВНОСТІ ЕЛЕКТРОЕНЕРГЕТИЧНОЇ СИСТЕМИ КРАЇНИ

Abstract. The research focuses on addressing the challenges of evaluating the effectiveness of modern power systems in countries with different configurations. Given the growing interest of con-sumers in the impact on the electricity supply schedule, the research is divided into two main parts. Firstly, a model has been developed to assess the operational efficiency of the power system. Secondly, a method for analyzing the efficiency of the energy system has been developed, based on a model of changes in energy system quality indicators. The presented model takes into account energy system characteristics such as the probability of network failure, normalized frequency of electricity, and the volume of consumed and supplied electrical energy. It is expected that this model will provide a more accurate assessment of the efficiency of the energy system under normal operating conditions. Additionally, a method for analyzing the operational efficiency of the energy system has been developed, based on a model of changes in energy system quality indicators. The uniqueness of this method lies in the introduction of a new comprehensive indicator of energy system efficiency, deter-mined by the convolution method of partial indicators, namely: the volume of electricity supply, supply quali-ty, and electricity supply efficiency. The reliability of the model is confirmed by calculations of the daily schedule of supply and load Keywords: electric power system, electricity supply efficiency, evaluation of power supply system effi-ciency, electricity indicators, load schedule, consumer management, electricity supply quality.

Few-Shot Object Detection Based on the Transformer and High-Resolution Network

Now object detection based on deep learning tries different strategies. It uses fewer data training networks to achieve the effect of large dataset training. However, the existing methods usually do not achieve the balance between network parameters and training data. It makes the information provided by a small amount of picture data insufficient to optimize model parameters, resulting in unsatisfactory detection results. To improve the accuracy of few shot object detection, this paper proposes a network based on the transformer and high-resolution feature extraction (THR). High-resolution feature extraction maintains the resolution representation of the image. Channels and spatial attention are used to make the network focus on features that are more useful to the object. In addition, the recently popular transformer is used to fuse the features of the existing object. This compensates for the previous network failure by making full use of existing object features. Experiments on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.

Battery State-of-Health Prediction-Based Clustering for Lifetime Optimization in IoT Networks

The Internet of Things (IoT) represents a pervasive system that continuously demonstrates an expanded application in various domains. The energy-efficiency problem has always been a crucial issue linked to this type of network where the system lifetime strongly depends on devices’ batteries. Numerous energy-efficient networking protocols have been proposed in the literature to increase the system lifetime. However, most of the proposed approaches deal with the short-term vision of energy consumption and omit to consider the rechargeable battery degradation when evaluating the network lifetime. Indeed, the major parts of the network devices use rechargeable batteries that age and degrade over time due to several factors (temperature, voltage, charging/discharging cycle, etc.). Therefore, it is essential to promptly detect these internal and environmental degradation factors to avoid network failures. Clustering represents one of the main wireless network protocols and plays an essential role in network self organizing. In this work, we propose a novel long-term energy optimization clustering approach based on battery State of Health (SoH) prediction, called LECA_SOH. The objective is to predict the impact of cluster heads election on the rechargeable batteries SoH before applying the clustering. LECA_SOH fosters the selection of the nodes, which will less suffer from battery degradation during the future rounds, leading to extend the system lifetime. The obtained results demonstrate that the proposed clustering approach improves the network lifetime in the long term and extends the number of recharging cycles compared to the conventional energy-efficient approaches.