Network Failures Research Articles

LSTGCN: Inductive Spatial Temporal Imputation Using Long Short-Term Dependencies

Spatial temporal forecasting of urban sensors is essentially important for many urban systems, such as intelligent transportation and smart cities. However, due to the problem of hardware failure or network failure, there are some missing values or missing monitoring sensors that need to be interpolated. Recent research on deep learning has made substantial progress on imputation problem, especially temporal aspect (i.e., time series imputation), while little attention has been paid to spatial aspect (both dynamic and static) and long-term temporal dependencies. In this article, we proposed a spatial temporal imputation model, named Long Short-Term Graph Convolution Networks (LSTGCN), which includes gated temporal extraction (GTE) module, multi-head attention-based temporal capture (MHAT) module, long-term periodic temporal encoding (LPTE) module, and bidirectional spatial graph convolution (BSGC) module. The GTE adopts a gated mechanism to filter short-term temporal information, while the MHAT utilizes position encoding to enhance the difference of each timestamps, then use multi-head attention to capture short-term temporal dependency. The BSGC is adopted to handle with spatial relationships between sensor nodes. And we design a periodic encoding technique to process long-term temporal dependencies. The BSGC handles spatial relationships between sensor nodes, and a periodic encoding technique is used to process long-term temporal dependencies. Our experimental analysis includes completion and forecasting tasks, as well as transfer and ablation analyses. The results show that our proposed model outperforms state-of-the-art baselines on real-world datasets.

Fault-tolerant partition resolvability of cycle with chord.

In the realm of connected networks, distance-based parameters, particularly the partition dimension of graphs, have extensive applications across various fields, including chemistry and computer science. A notable variant of the partition dimension is the fault-tolerant resolving partition, which is critical in computer science for networking, optimization, and navigation tasks. In networking, fault-tolerant partitioning ensures robust communication pathways even in the event of network failures or disruptions. In optimization, it aids in developing efficient algorithms capable of withstanding errors or changes in input data. In navigation systems, fault-tolerant partitioning supports reliable route planning and navigation services under uncertain or dynamic conditions. This paper focuses on the fault-tolerant partition dimension within the specific context of the cycle with chord graphs, exploring its properties and implications for enhancing the robustness and reliability of networked systems.

NFV recovery strategies for critical services after massive failures in optical networks

Today, more critical services than ever rely on the communication infrastructure of 5G and beyond, demanding resilient recovery strategies when disasters occur. The inherent uncertainty of disasters makes post-disaster recovery a complex challenge. Today’s solutions focus on external infrastructure, such as alternative power supply or ad-hoc UAVs, to restore communication. However, the programmable nature introduced in 5G also allows us to migrate (relocate) Virtual Network Functions (VNFs) to restore communication more efficiently. In this paper, we develop an experimental framework to evaluate the performance of recovery strategies utilizing VNF migration in an optical network. We demonstrate that selecting the appropriate post-disaster recovery strategy can significantly accelerate the restoration of critical services by several hours in some disaster scenarios. Furthermore, we create ClusPRi, a modification of the virtual resource allocation algorithm ClusPR. ClusPRi prioritizes critical traffic when allocating resources in a post-disaster scenario. We show that adding routing priority to the resource allocation algorithm further accelerates the restoration of critical communication in a disaster scenario.

A new DC bus voltage control strategy for energy routers based on distributed coordination optimization

Abstract The types of power ports of energy routers at the side of the distribution network platform are complex and have different characteristics, which makes the operation conditions of energy routers complex and changeable. Although many pieces of literature have proposed more relevant control strategies to effectively realize the efficient and stable operation of energy routers under the two basic working conditions of grid-connected and isolated islands. However, when considering various complex operating conditions such as start-up, overload/overcharge, sudden failure of distribution network, grid-connected, and inter-island switching, the reliable operation of the existing energy routers is still faced with great challenges. In this paper, a DC bus voltage control strategy based on distributed coordination optimization is proposed for typical operating conditions of energy routers. Simulation results show that this strategy can effectively achieve the DC bus voltage control performance of energy routers under various burst operating conditions and strengthen the robustness of output voltage of energy routers under isolated islands.

Methodology for assessing the technical condition of electrical networks based on determining the probability of their failure

RELEVANCE. The current technical condition of electrical networks at the moment is generally unsatisfactory with an average level of physical wear of about 60-70 %, therefore,the development of fundamentally new methods for assessing their technical condition is currently an urgent task. The methods of assessing the technical condition of electrical networks analyzed in the article are based to a greater extent on the subjective and insufficiently accurate method of expert assessments, which actually does not take into account the data of its technical diagnostics, as well as statistics of defects and failures. OBJECT. To develop a methodology for assessing the technical condition of electrical networks, which would be based on probabilistic models characterizing the physical processes occurring in electrical networks, as well as data from their technical diagnostics, statistics of defects and failures. METHODS. To assess the technical condition of electrical networks, a probabilistic assessment method was used. In this case, the methodology involves sequential determination of the probabilities of occurrence of selected groups of emergency modes, the probability of failure of a network element as a whole, as well as the probability of failure of an object and a group of objects of the power grid. RESULTS. Based on the research results, probabilistic models of failure of electrical networks were obtained, which, unlike the assessment methods discussed in the article, are based on data from technical diagnostics and inspections of electrical equipment. An example of the implementation of the developed methodology on real electrical network objects is given. CONCLUSION. The developed methodology allows for a more reasonable prioritization of technical impacts on electrical grid equipment, which will make it possible to replace or repair equipment that has actually exhausted its physical resource, which will reduce the frequency of emergency conditions and reduce damage from power supply interruptions.

Overload cascading failure detection algorithm for multi-layer supply chain network considering delay probability factor

At present, the supply chain is multi-layered and complicated, and its failure detection is based solely on the characteristic threshold, which does not lack the ability to describe the complex supply chain. An overload cascading failure detection algorithm for multi-layer supply chain network considering delay probability is proposed. Based on the multi-layer supply chain network including raw material suppliers, manufacturers, distributors and retailers, the overload cascading failure model of the multi-layer supply chain network is constructed through networking, and the overload cascading failure process of the multi-layer supply chain network is described through three aspects: initial load, node capacity and overload node load distribution. By monitoring and analyzing the status update messages of each node in the supply chain network, and considering the delay probability factor, the node and path that may lead to cascading failure are identified by predicting the delay probability of the next message through the historical message delay probability, and the overload cascading failure detection of the multi-layer supply chain network is realized. The experimental results show that the algorithm can effectively detect the failure of each overloaded enterprise node in the multi-layer supply chain network, which is helpful to enhance the early warning and response ability of the cascade failure of the supply chain network. The algorithm can realize the failure detection of supply chain network under different attack conditions and initial failure ratio of nodes, and measure the anti-risk ability of supply chain network. Under deliberate attack and when the initial attack node is a supplier network layer node, it has a greater impact on the multi-layer supply chain network.

A novel recovery controllability method on temporal networks via temporal lost link prediction

Abstract Temporal networks are essential in representing systems where interactions between elements evolve over time. A crucial aspect of these networks is their controllability the ability to guide the network to a desired state through a set of control inputs. However, as these networks evolve, links between nodes can be lost due to various reasons, such as network failures, disruptions, or attacks. The loss of these links can severely impair the network’s controllability, making it challenging to recover desired network functions. In this paper, while investigating the destructive effects of various attacks on controllability processes in temporal networks, a new controllability recovery method is proposed, in which it prevents disruptions in this type of network processes by predicting lost links. In the proposed method, using network embedding and feature extraction, the dissimilarity of the nodes is calculated and then the missing links are predicted by designing a neural network. The results of the implementation of the proposed method on the datasets have demonstrates that the proposed method performed better than other conventional methods.

Large language model-based optical network log analysis using LLaMA2 with instruction tuning

The optical network encompasses numerous devices and links, generating a significant volume of logs. Analyzing these logs is significant for network optimization, failure diagnosis, and health monitoring. However, the large-scale and diverse formats of optical network logs present several challenges, including the high cost and difficulty of manual processing, insufficient semantic understanding in existing analysis methods, and the strict requirements for data security and privacy. Generative artificial intelligence (GAI) with powerful language understanding and generation capabilities has the potential to address these challenges. Large language models (LLMs) as a concrete realization of GAI are well-suited for analyzing DCI logs, replacing human experts and enhancing accuracy. Additionally, LLMs enable intelligent interactions with network administrators, automating tasks and improving operational efficiency. Moreover, fine-tuning with open-source LLMs protects data privacy and enhances log analysis accuracy. Therefore, we introduce LLMs and propose a log analysis method with instruction tuning using LLaMA2 for log parsing, anomaly detection and classification, anomaly analysis, and report generation. Real log data extracted from the field-deployed network was used to design and construct instruction tuning datasets. We utilized the dataset for instruction tuning and demonstrated and evaluated the effectiveness of the proposed scheme. The results indicate that this scheme improves the performance of log analysis tasks, especially a 14% improvement in exact match rate for log parsing, a 13% improvement in F1-score for anomaly detection and classification, and a 23% improvement in usability for anomaly analysis, compared with the best baselines.

Resilience optimization analysis of smart mining cluster cyber-physical systems based on the NK model

This paper explores the challenges and opportunities presented by the digitization and intelligent clustering in the mining industry, focusing on the security and inter-system influences within Cyber-Physical Systems (CPS) of smart mining clusters. Utilizing the NK model and complex adaptive systems theory, we construct a dual-layered network and cascade failure model, alongside a dynamic repair strategy that adapts to environmental changes. By analyzing the critical parameters of node number (N) and relationship density (K), we assess the resilience of smart mining CPS under cascade failures with dynamic repairs. Our findings reveal that dynamic repair strategies significantly enhance network resilience against both random and deliberate attacks, with N and K acting as crucial regulators of repair effectiveness. This suggests that adapting N and K can improve repair outcomes and system resilience, despite increasing complexity. The paper discusses the practical implications of these strategies through simulation and real-world scenarios, highlighting the need for adaptive repair strategies in enhancing smart mining CPS resilience and process safety. Introducing the NK model to CPS resilience research offers new insights into the resilience of smart mining cyber-physical systems and provides a comprehensive risk analysis method for these systems in complex scenarios.

Towards more realistic stochastic modelling of leakage in water distribution systems

ABSTRACT A new stochastic model is developed to predict leak types and sizes in water distribution systems. By linking records of actual failures in water networks with new tools for stochastic modelling, the study provides more accurate estimates of network leakage behaviour. District metered areas (DMAs) have been adopted internationally as best practice for monitoring and controlling leakage, with the infrastructure leakage index (ILI) as the main performance indicator. The leakage exponent (N1), which quantifies the relationship between leakage rate and pressure, is a key parameter in describing DMA leakage behaviour. After analysing thousands of stochastic networks for different combinations of pipe material and ILI, the study found that different pipe materials have distinct leakage exponent distributions. Iron and asbestos cement (AC) pipes display N1 values close to 0.5, polyvinyl chloride (PVC) pipes have the highest N1 values, and polyethylene (PE) lies between PVC and iron/AC. The study further showed that current guidelines linking ILI to N1 for different pipe materials depart substantially from the observed results, indicating that these guidelines may need to be updated. The study provides a tool for more realistic modelling of leakage that offers valuable insights into the behaviour of leakage in different pipe materials.

Energy Management System and Control of Plug-in Hybrid Electric Vehicle Charging Stations in a Grid-Connected Microgrid

In the complex environment of microgrid deployments targeted at geographic regions, the seamless integration of renewable energy sources meets a variety of essential challenges. These include the unpredictable nature of renewable energy, characterized by intermittent energy generation, as well as ongoing fluctuations in load demand, the vulnerabilities present in distribution network failures, and the unpredictability that results from unfavorable weather conditions. These unexpected events work together to disturb the delicate balance between energy supply and demand, raising the alarming threat of system instability and, in the worst cases, the sudden advent of damaging blackouts. To address this issue, a fuzzy logic-based energy management system has been developed to monitor, manage, and optimize energy consumption in microgrids. This study focuses on the control of diesel generators and utility grids in a grid-connected microgrid which manages and evaluates numerous energy consumption and distribution features within a specified system, e.g., building or a microgrid. An energy management system is suggested based on fuzzy logic as a swift fix for complications with effective and competent resource management, and its presentation is compared with both the grid-connected and off-grid modes of the microgrid. In the end, the results exhibit that the proposed controller outclasses the predictable controllers in dropping sudden variations that arise during the addition of sources of renewable energy, supporting the refurbishment of the constant system.

Node Importance Evaluation of Urban Rail Transit Based on Signaling System Failure: A Case Study of the Nanjing Metro

Assessing the importance of nodes in urban rail transit systems helps enhance their ability to respond to emergencies and improve reliability in view of the fact that most of the existing methods for evaluating the importance of rail transit nodes ignore the disturbance effect of signaling system failures and are unable to objectively identify critical stations in specific disturbance scenarios. Therefore, this paper proposed a method for evaluating the importance of urban rail transit nodes in signaling system failure scenarios. The method was based on the research background of the signaling system failure that occurs most frequently and analyzed the network failure mechanism after the occurrence of a disturbance. The node importance evaluation indices were selected from the network topology and network operation performance in two aspects. The variation coefficient–VIKOR method was employed to comprehensively assess the significance of urban rail transit stations during signaling system failures. The Nanjing Metro network was also used as an example to evaluate the importance of network stations. The results showed that under the attack method of signaling system failure, most ECC and interlocking stations experienced significantly higher network performance losses compared to the original attack method, and a few interchange stations showed smaller performance losses. The critical stations identified based on the proposed method are mainly distributed in the passenger flow backbone of the Nanjing Metro and were constructed in the early stage; of these, 85% are ECC stations or interlocking stations, which are easily neglected in daily management, in contrast to interchange stations with heavy passenger flow. The results of this study can provide an important reference for the stable operation and sustainable construction of urban rail transit.

Modeling multiple-pipe failures in stormwater networks using graph theory

ABSTRACT One way to enhance the resilience of urban stormwater networks is to identify critical components of the network for targeted management and maintenance strategies. However, tackling multiple-pipe failures poses a significant hurdle due to the computational burden of conventional methods, making it impractical to analyze all potential combinations of pipe failures. To address this research gap, this study proposes a novel method based on complex network analysis to determine critical pipe failure combinations. The method incorporates network topology and estimates flooding impacts based on graph measures instead of solving complex hydraulic equations. The method is tested on two case studies with varying loop degrees and for different failure levels and compared with the state-of-the-art hydrodynamic modeling method (HMM) in terms of accuracy and computational time. Results show that the proposed graph theory method (GTM) can be used to identify the most critical pipe failure combinations. However, the accuracy of the GTM decreases slightly with increasing failure level and with increasing loop degree of the network. A hybrid graph-hydrodynamic model (GHM) is also developed as part of the study which combines advantages from both GTM and HMM.

Research on the Robustness of Command and Control Networks under Cascading Failures

The current analysis of cascading failures in command and control networks pays little attention to their roles and mechanisms, resulting in challenges in quantifying survivability evaluation metrics and limiting practical application. To address these issues, this paper designs a command and control network model with a recovery strategy to improve the scientific evaluation of critical nodes and enhance the reliability of subsequent cascading failure simulations. Two capacity parameters are introduced to analyze the nonlinear behavior between network node load and capacity, and an optimal recovery strategy is proposed. This strategy prioritizes the recovery of critical nodes, thereby minimizing the overall probability of network failure. Simulations were conducted under both random failure and deliberate attack scenarios, comparing the proposed strategy with random recovery and betweenness-priority recovery strategies to identify the optimal recovery approach. The experiments showed that the optimal recovery strategy significantly enhanced the network’s survivability and recovery efficiency, allowing for the restoration of basic network functions in the shortest possible time and reducing the impact of cascading failures. By integrating the operability and uncertainty of real-world command and control networks, this method improved the network’s recovery capability and overall stability in the face of cascading failures through scientific evaluation and strategy optimization.

Retraction Note: Anticipating network failures and congestion in optical networks a data analytics approach using genetic algorithm optimization

Retraction Note: Anticipating network failures and congestion in optical networks a data analytics approach using genetic algorithm optimization

Activity centrality-based critical node identification in complex systems against cascade failure

Identifying critical nodes in the network has been a concern permanently. Cascading failure would cause catastrophic events, and in the field of cascading failure in complex networks, the structure and dynamics are considered as the key in the process of cascading failure. It is vital to have an applicable centrality to find critical nodes that could control and prevent the cascading failure. In this paper, we propose a steady-state activity centrality to evaluate the importance of each node, and the proposed centrality is related to the degree of each node and the activity of its neighbor nodes. The giant component, the average activity, and the tipping point under different attack strategies are introduced to compare the attack effect of these three centralities including steady-state activity centrality, betweenness centrality and closeness centrality. The results show that the attack effect under the proposed centrality is better than the effect under the other two centralities. In particular, for the network with the SIS and gene regulation dynamic, the attack effect under the steady-state activity centrality driven strategy is obviously better than the effect under the betweenness centrality driven strategy when the network is heterogeneous.

Porous Iron Electrodes Reduce Energy Consumption During Electrocoagulation of a Virus Surrogate: Insights into Performance Enhancements Using Three-Dimensional Neutron Computed Tomography.

Electrocoagulation has attracted significant attention as an alternative to conventional chemical coagulation because it is capable of removing a wide range of contaminants and has several potential advantages. In contrast to most electrocoagulation research that has been performed with nonporous electrodes, in this study, we demonstrate energy-efficient iron electrocoagulation using porous electrodes. In batch operation, investigation of the external pore structures through optical microscopy suggested that a low porosity electrode with sparse connection between pores may lead to mechanical failure of the pore network during electrolysis, whereas a high porosity electrode is vulnerable to pore clogging. Electrodes with intermediate porosity, instead, only suffered a moderate surface deposition, leading to electrical energy savings of 21% and 36% in terms of electrocoagulant delivery and unit log virus reduction, respectively. Neutron computed tomography revealed the critical role of electrode porosity in utilizing the electrode's internal surface for electrodissolution and effective delivery of electrocoagulant to the bulk. Energy savings of up to 88% in short-term operation were obtained with porous electrodes in a continuous flow-through system. Further investigation on the impact of current density and porosity in long-term operation is desired as well as the capital cost of porous electrodes.

Consensus-based economic dispatch for DC microgrids incorporating realistic cyber delays

This paper introduces a distributed control approach for optimizing the operation of islanded DC microgrids (µGs), accounting for cyber delays and network failures. Initially, an integrated network emulation platform is utilized to calculate realistic cyber delays for islanded DC µGs employing IEC 61850 communication standard. To address the instability issues arising from cyber delays, the Artstein-Kwon-Pearson reduction technique is employed to convert the system with cyber delays into one without delays. The Subsequently, a consensus-based generation-cost optimization algorithm is designed for maintaining the economic operation of the µG. This algorithm efficiently equalizes the incremental costs (ICs) of all distributed generators (DGs) in a fully distributed manner, while adhering to the DGs capacity limits. Additionally, a fully distributed voltage regulation control is developed to stabilize µG average voltage, ensuring the balance between generation and demand. The efficacy of the formulated control technique is substantiated through diverse case studies, thereby underscoring its superior operational performance.

Vulnerability Analysis of a Multilayer Logistics Network against Cascading Failure

One of the most challenging issues in contemporary complex network research is to understand the structure and vulnerability of multilayer networks, even though cascading failures in single networks have been widely studied in recent years. The goal of this work is to compare the similarities and differences between four single layers and understand the implications of interdependencies among cities on the overall vulnerability of a multilayer global logistics network. In this paper, a global logistics network model set as a multilayer network considering cascading failures is proposed in different disruption scenarios. Two types of attack strategies—a highest load attack and a lowest load attack—are used to evaluate the vulnerability of the global logistics network and to further analyze the changes in the topology properties. For a multilayer network, the vulnerability of single layers is compared as well. The results suggest that compared with the results of a single global logistics network, a multilayer network has a higher vulnerability. In addition, the heterogeneity of networks plays an important role in the vulnerability of a multilayer network against targeted attacks. Protecting the most important nodes is critical to safeguard the potential “vulnerability” in the development of the global logistics network. The three-step response strategy of “Prewarning–Response–Postrepair” is the main pathway to improving the adjustment ability and adaptability of logistics hub cities in response to external shocks. These findings supplement and extend the previous attack results on nodes and can thus help us better explain the vulnerability of different networks and provide insight into more tolerant, real, complex system designs.

Trust-Based Detection and Mitigation of Cyber Attacks in Distributed Cooperative Control of Islanded AC Microgrids

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats.