Network Failures Research Articles

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.

Modeling and analysis of cascading failures in multilayer higher-order networks

With the increasing application of network science, understanding the behavior of higher-order networks has become particularly important. Especially, the study of multilayer higher-order networks is significant for revealing the interdependence and higher-order interactions in complex systems. This paper proposes a mathematical framework to explore multilayer higher-order networks composed of multiple fully interdependent hypergraphs. Each hypergraph consists of the same number of nodes, and nodes between layers are connected one-to-one. By analyzing the cascading failures of multilayer hypergraphs under different topologies, we found that although the network sizes are the same in the steady state, different topological structures (such as star-like, tree-like, and chain-like) significantly affect the time required to reach a stable state, with the star-like topology reaching stability the fastest. Furthermore, we explored the impact of network parameters on the robustness of networks. We found that in multilayer homogeneous hypergraphs, the robustness of the network becomes stronger with an increase in the average hyperdegree or average hyperedge cardinality; in multilayer heterogeneous hypergraphs, the robustness of the network becomes more fragile as the power exponent increases. Finally, experimental results indicate that with the rise in the number of network layers, the network becomes more fragile.

Dynamic resilience analysis of the liner shipping network: From structure to cooperative mechanism

Liner shipping networks play a vital role in global and regional trade. However, they are susceptible to damage from unexpected interruptions, which can trigger dynamic cascading failures and undermine the system’s resilience. To address this challenge, we propose a novel cascading failure model for liner shipping networks that considers the characteristics of the network structure and port functions. First, we design two load redistribution methods that rely on network topology and employ a cooperative mechanism for coordination. This cooperative mechanism aims to balance the benefits for carriers and shippers by effectively controlling losses. Subsequently, we develop three metrics—network congestion rate, failure rate, and shipper loss—to assess the resilience of the network during cascading failures. To verify the impact of the cooperative mechanism, we apply the proposed methods to the China-Europe liner shipping network. Through simulations involving various port failures and resistance levels, we analyze the effectiveness of the cooperative mechanism. The results demonstrate that redistributing the load to downstream ports within the network effectively mitigates deep cascading failures. Additionally, the implementation of a port cooperative mechanism enhances resilience in the face of uncertainties by safeguarding crucial ports within the network and significantly reducing shipper losses. When port resistance is low, the cooperative mechanism reduces shipper losses by nearly half and lowers the average congestion rate. Although port reserve capacity can resist cascading failures, it falls short in the face of severe disruptions. In such cases, the cooperative mechanism compensates for capacity shortages, enhancing port resilience at a low cost. This study contributes to combating and minimizing cascading congestion in liner shipping networks, offering valuable insights for risk prevention and management strategies for ports and shipping companies. It also has implications for yield management and policy decisions from a network perspective.

Improvement of low-frequency oscillation damping in power systems using a deep learning technique

Over the last few years, machine learning tools have significantly progressed and attracted extensive applications in many parts of contemporary life. The power sector is one of the leading domains implementing such appealing and effective technologies for diverse applications as a part of the digital transformation of electric networks. A power system's low-frequency oscillation (LFO) is a non-threatening but slow-burning problem that might cause complete network failure unless adequately handled. This article proposes a state-of-the-art procedure of LFO damping in electric power networks via the sine cosine algorithm and deep learning (DL) technique. It uses two networks of power systems, in which the synchronous generator is fitted with a power system stabilizer (PSS) in the case of the first network; in the other, the synchronous machine is conjoined to the PSS that coordinates with a unified power flow controller. The proposal is developed based on the statistical assessment of the analyzed networks to improve the LFO damping via real-time adjustment of PSS parameters/variables. The proposed technique was evaluated using power system stability performance measuring criteria, such as the eigenvalue and minimum damping ratio. In the end, the effectivity of the stability-gaining procedure is also tested by time-domain simulation to implement in real-time. The study also dealt with a comparative investigation and discussion of the findings of some published works to conclude the capability of the proposed DL tool for stability improvement of the system in real-time by removing undesirable LFOs.

Pipe Failure Prediction in the Water Distribution System Using a Deep Graph Convolutional Network and Temporal Failure Series

Pipe Failure Prediction in the Water Distribution System Using a Deep Graph Convolutional Network and Temporal Failure Series

A Dynamic Topology Optimization Method for Tactical Edge Networks Based on Virtual Backbone Networks.

To address the issues of low survivability and communication efficiency in wireless sensor networks caused by frequent node movement or damage in highly dynamic and high-mobility battlefield environments, we propose a dynamic topology optimization method based on a virtual backbone network. This method involves two phases: topology reconstruction and topology maintenance, determined by a network coverage threshold. When the coverage falls below the threshold, a virtual backbone network is established using a connected dominating set (CDS) and non-backbone node optimization strategies to reconstruct the network topology, quickly restore network connectivity, effectively improve network coverage, and optimize the network structure. When the coverage is above the threshold, a multi-CDS scheduling algorithm and slight position adjustments of non-backbone nodes are employed to maintain the network topology, further enhancing network coverage with minimal node movement. Simulations demonstrate that this method can improve coverage and optimize network structure under different scales of network failures. Under three large-scale failure operational scenarios where the network coverage threshold was set to 80%, the coverage was enhanced by 26.12%, 15.88%, and 13.36%, and in small-scale failures, the coverage was enhanced by 7.55%, 4.90% and 7.84%.

A hybrid SgDT framework for risk analysis of container-handling operations at automated container terminals

With the booming maritime economy and the steady expansion of port production, automated container terminals (ACTs) have rapidly developed worldwide. The safety and stability of container-handling operations at ACTs are crucial for maintaining high efficiency and performance in maritime transportation and global supply chains. However, few studies have focused on the quantitative risk analysis of container-handling operations at ACTs. This study proposes a hybrid SgDT (STPA-grey-DEMATEL-TAISM) approach to analyze risk factors and their coupling interrelationships within ACT handling operations. Initially, 22 factors influencing ACT handling operations were identified based on the system theoretic process analysis (STPA) model and literature. Subsequently, grey theory and decision-making test and evaluation laboratory (DEMATEL) were fused to visualize the cause-effect relationships of these factors. Finally, the total adversarial interpretive structural model (TAISM) was adopted to extract the hierarchical interrelationships among these factors using an adversarial hierarchical structure. The findings showed that poor supervision, communication network failures, sensor malfunctions, equipment malfunctions, lack of safety awareness, and interruptions of the power supply are the most critical factors influencing ACT handling operations. This study provides theoretical insights and practical implications for managers to enhance ACT handling risk management.

Aperiodic small signal stability method for detection and mitigation of cascading failures in smart grids

The occurrence of cascading failures poses significant risks to the stability and reliability of modern smart grids. This article presents a novel hybrid algorithm designed to assess and mitigate these failures. The technique combines advanced clustering algorithms, specifically Affinity Propagation Graph (APG) and Self-Propagating Graph (SPG), to detect critical nodes, and Unified Power Flow Controllers (UPFCs) to provide compensation to grid networks. The algorithm first uses APG to divide the network into clusters and considers the center bus as the critical node. If the center bus is not critical, SPG is applied to identify the critical node. This hybrid approach identifies the critical node in just 0.02 s (for both APG and SPG) and with improved accuracy compared to existing methods. After identifying critical nodes, UPFCs are strategically installed to regulate power flow and reduce the probability of cascading failures, with compensation taking approximately 0.14 s. Simulation results demonstrate the effectiveness of the proposed method in enhancing grid resilience and reducing the likelihood of cascading failures. By strategically deploying UPFCs at critical nodes, this approach ensures resilient grid operation in various scenarios. This research significantly contributes to the development of smart grid technologies by providing a comprehensive framework to address cascading failures in power distribution networks. The proposed method shows potential for improving the reliability and stability of power grids amid changing system dynamics and uncertainties.

Reliability enhancement through optimal placement of photovoltaic power plant and battery energy storage in distribution system

Integration of distributed generation (DG) units could reduce the duration of power outage for a certain number of consumers in distribution networks after a network failure. Prerequisites are that i) the DG unit has the technical characteristics that enable its islanded operation, the degree of automation of the distribution network allows it and ii) it is in accordance with the current technical regulations of the country. The extent to which integration of distributed sources can improve reliability depends on the DG size, type, and the point of common coupling. Besides, an energy storage system could be installed along with DG unit, so that energy supply availability during fault periods can be secured. This paper proposes an algorithm based on mixed-integer linear programming (MILP) approach for optimal placement of photovoltaic power plant (PVPP) and battery energy storage system (BESS). The optimal location is determined so that expected non supplied energy is minimized. The uncertainties in the estimation of production of PVPP, load and BESS state of charge (SoC) were taken into account by Monte Carlo simulations (MCS).

Embolism propagation in Adiantum leaves and in a biomimetic system with constrictions.

Drought poses a significant threat to forest survival worldwide by potentially generating air bubbles that obstruct sap transport within plants' hydraulic systems. However, the detailed mechanism of air entry and propagation at the scale of the veins remains elusive. Building upon a biomimetic model of leaf which we developed, we propose a direct comparison of the air embolism propagation in Adiantum (maidenhair fern) leaves, presented in Brodribb et al. (Brodribb TJ, Bienaimé D, Marmottant P. 2016 Revealing catastrophic failure of leaf networks under stress. Proc. Natl Acad. Sci. USA 113, 4865-4869 (doi:10.1073/pnas.1522569113)) and in our biomimetic leaves. In particular, we evidence that the jerky dynamics of the embolism propagation observed in Adiantum leaves can be recovered through the introduction of micrometric constrictions in the section of our biomimetic veins, mimicking the nanopores present in the bordered pit membranes in real leaves. We show that the intermittency in the propagation can be retrieved by a simple model coupling the variations of pressure induced by the constrictions and the variations of the volume of the compliant microchannels. Our study marks a step with the design of a biomimetic leaf that reproduces particular aspects of embolism propagation in real leaves, using a minimal set of controllable and readily tunable components. This biomimetic leaf constitutes a promising physical analogue and sets the stage for future enhancements to fully embody the unique physical features of embolizing real leaves.

Node and edge centrality based failures in multi-layer complex networks

Multi-layer complex networks (MLCN) appears in various domains, such as, transportation, supply chains, etc. Failures in MLCN can lead to major disruptions in systems. Several research have focussed on different kinds of failures, such as, cascades, their reasons and ways to avoid them. This paper considers failures in a specific type of MLCN where the lower layer provides services to the higher layer without cross layer interaction, typical of a computer network. A three layer MLCN is constructed with the same set of nodes where each layer has different characteristics, the bottom most layer is Erdos–Renyi (ER) random graph with shortest path hop count among the nodes as gaussian, the middle layer is ER graph with higher number of edges from the previous, and the top most layer is preferential attachment graph with even higher number of edges. Both edge and node failures are considered. Failures happen with decreasing order of centralities of edges and nodes in static batch mode and when the centralities change dynamically with progressive failures. Emergent pattern of three key parameters, namely, average shortest path length (ASPL), total shortest path count (TSPC) and total number of edges (TNE) for all the three layers after node or edge failures are studied. Extensive simulations show that all but one parameters show definite degrading patterns. Surprising, ASPL for the middle layer starts showing a chaotic behaviour beyond a certain point for all types of failures.

LOGICAL-PROBABLIC MODELING OF FAILURES OF GEODESIC NETWORKS

The credibility and reliability of the results of geodetic and cadastral works depends mainly on the accuracy of the instruments, the qualification of the observer, his professionalism and the reliability of the points of the geodetic network (GN). As a rule, in most cases, there is a well-founded choice of geodetic tools suitable for accuracy and the involvement of a highly qualified geodetic engineer to perform certain types of geodetic work. However, certain questions may arise regarding the determination of starting points for the creation of a reliable geodetic survey justification. The issue of selecting reliable geodetic points for performing engineering and geodetic works based on the application of reliability assessment methods is an urgent scientific problem today. The purpose of the work is to establish the optimal reliability assessment method based on the analysis of existing ones. Adapt this method to solve the problem of assessing the reliability of geodetic networks as one of the most important parameters characterizing its condition. Assess the reliability of the Dnipro geodetic network by constructing a fault tree. Calculate the dynamics of the intensity of GN failures during the 100-year period of its existence. Calculate the appropriate periodicity of surveys (monitoring) of GN depending on the intensity of failures of elements (geodetic points). Methodology. Analysis of international and national standards for reliability assessment, Standards of Ukraine, where the methods of system reliability assessment are given. Review of methods for assessing GN reliability. The deductive method with the construction of a geodetic network failure tree has been applied. This method is based on a logic-probabilistic model of cause-and-effect relationships of system failures with failures of its elements and other events. Scientific novelty. The optimal method of assessing the reliability of GN has been established. A GN failure tree has been built in the TopEvent FTA software tool. The reliability of the geodetic network has been evaluated, the intensity of failures at various stages of its operation has been calculated. Mathematical expressions of the failure functions of the geodetic network have been described by applying Boolean functions. Practical value. International and national regulatory documents and Standards of Ukraine of reliability assessment have been analyzed. In Standarts the methods of system reliability assessment have been given. The performed assessment of the reliability of the state of the geodetic network of the city of Dnipro helps to choose stable geodetic points for the creation of local geodetic networks and survey justification. Reasonable periodicity of monitoring for the survey of geodetic points has been substantiated. The use of reliable points of geodetic networks will allow obtaining reliable observation data, which will significantly increase the quality of engineering and geodetic works. Results. The functioning of the geodetic network of the city of Dnipro on the constructed sequential tree of failures has been considered. Boolean functions to describe the mathematical expressions of the failure functions of the geodetic network, which includes 5 and 10 elements have been used.

Cascading failures on interdependent hypergraph

Scientists are aware of the importance of studying interdependent networks, as they represent real-world scenarios better than isolated networks, such as the interdependent communication and power networks. However, in real scenarios, higher-order interactions exist in these networks. Recent research has shown that higher-order interactions that cannot be reflected in simple networks (i.e., a network only has pairwise interaction) can be reflected through hypergraph. This paper proposes an interdependent hypergraph model that considers the dependencies of interlayer nodes. We studied the cascading failures in the hypergraph with different interlayer dependencies. Through theoretical analysis, we have determined the maximum attack intensity the network can withstand and how its robustness changes under different attack intensities. In completely interdependent hypergraph, the network becomes increasingly fragile as the attack intensity increases, and the final network size suddenly jumps to zero, indicating a first-order phase transition in the network. We conducted experiments on multiple artificially synthesized hypergraph. The experimental results indicate that our analytical solution is consistent with the simulated solution. These findings will help us better understand the impact of different topologies on network robustness in interdependent hypergraph, enabling us to take more effective measures to address potential network failures and attacks.

Occurrence and identification of microplastics retained in corrosion deposits of drinking water transmission pipes

The irregular structure and high porosity of corrosion deposits create suitable conditions for the retention, accumulation and adsorption of microplastics (MPs) and nanoplastics (NPs) transported by distributed water. Due to the low mass and continuous degradation of MPs, under certain conditions (e.g., changes in water composition or hydraulic conditions, network failures), these particles can be re-released into the water, causing secondary contamination. This paper presents preliminary results on the degree of MP contamination of sediments lining the inner surface of metal alloy pipes taken from a municipal drinking water distribution network. The isolated particles were assessed in terms of number, shape, residence time in the network, and origin. Plastic fragments classified as MPs and NPs were found in all analyzed corrosion deposits. Fragments smaller than 50 μm predominated, indicating a high level of plastic fragmentation associated with advanced degradation and prolonged residence in the environment. The predominant plastics identified were polyethylene (PE), polyethylene terephthalate (PET), and polyamides. High-carbon particles, most likely NP particles, whose presence in drinking water may pose a high health risk to consumers due to their potential to migrate into body tissues, were very abundant in the sediments but impossible to count with the techniques used. The results indicate the need to intensify research on the content of MPs and NPs not only in drinking water, but also in the sediments covering the interior of distribution pipes, and to identify factors that may cause their secondary release into bulk water.

A numerical study on tensile strength of low-density Kagome networks made of brittle fibers

In this paper, we present a numerical study on the tensile strength of low-density Kagome networks made of brittle fibers. First, an elastic beam model is employed to analytically predict the effective elastic properties and tensile strength, as well as the critical condition for buckling of the fibers in Kagome networks. A series of finite element analyses are then conducted to simulate the elastic deformation and failure of Kagome networks under tension. The numerical results based on unit-cell models reveal four possible failure modes of the Kagome networks subject to uniaxial tension, summarized in a phase diagram in terms of the relative density and the fiber strength. The pre-buckling failure mode is restricted to cases with relatively high density and low fiber strength. A low-density Kagome network is likely to fail by one of the post-buckling modes, with an effective tensile strength much lower than the prediction by the elastic beam model. For Kagome networks consisting of a large number of unit cells, the effect of boundary conditions on the tensile strength is examined. Under periodic boundary conditions, the effective tensile strength is nearly identical to that predicted by the unit-cell model, independent of the model size. Under a roller boundary condition, with damage initiation near the free edges followed by a diffusive damage progression, the effective tensile strength is lower than that under periodic boundary conditions for the cases of relatively low fiber strengths. Under a clamped boundary condition, the effective tensile strength is higher than that under periodic boundary conditions for the cases of relatively high fiber strengths, where fiber buckling is largely suppressed by the clamped boundaries. Finally, the effect of a crack-like defect on the effective tensile strength is studied for Kagome networks under the clamped boundary condition. With a small defect, the effective strength is nearly independent of the defect size. In contrast, with a relatively long defect, the effective strength decreases almost linearly with the length of the crack-like defect. The effective toughness for damage initiation and steady-state damage progression in the Kagome networks is discussed from an energetic perspective.

MOMTA-HN: A Secure and Reliable Multi-Objective Optimized Multipath Transmission Algorithm for Heterogeneous Networks

With the rapid development of heterogeneous network technologies, such as mobile edge computing, satellite communications, self-organizing networks, and the wired Internet, satisfying users’ increasingly diversified and complex communication needs in dynamic and evolving network environments has become a critical research topic. Ensuring secure and reliable information transmission is essential for stable network operation in these complex environments. Addressing this challenge, this study proposed a secure and reliable multi-objective optimized multipath transmission algorithm for heterogeneous networks to enhance security and reliability during data transmission. The core principle of this algorithm was that multipath transmission can provide additional protection through redundant paths. This redundancy ensured that even if one path is attacked or fails, alternative paths can maintain data integrity and reachability. In this study, we employed the Optimized Non-dominated Sorting Genetic Algorithm II (ONSGA-II) to determine the range of the initial population and filter suitable paths by optimizing them according to different demand objectives. In the path selection process, we introduced an innovative deletion graph method, which ensures that redundant paths do not share any common links with the original paths, except when there are unique links. This approach enhances the independence of transmission paths and improves the security of the transmission process. It effectively protects against security threats such as single points of failure and link attacks. We have verified the effectiveness of the algorithm through a series of experiments, and the proposed algorithm can provide decision-makers with high-reliability and low-latency transmission paths in heterogeneous network environments. At the same time, we verified the performance of the algorithm when encountering attacks, which is superior to other classical algorithms. Even in the face of network failures and attacks, it can maintain a high level of data integrity and security.

Secure-by-Design Real-Time Internet of Medical Things Architecture: e-Health Population Monitoring (RTPM)

The healthcare sector has undergone a profound transformation, owing to the influential role played by Internet of Medical Things (IoMT) technology. However, there are substantial concerns over these devices’ security and privacy-preserving mechanisms. The current literature on IoMT tends to focus on specific security features, rather than wholistic security concerning Confidentiality, Integrity, and Availability (CIA Triad), and the solutions are generally simulated and not tested in a real-world network. The proposed innovative solution is known as Secure-by-Design Real-Time IoMT Architecture for e-Health Population Monitoring (RTPM) and it can manage keys at both ends (IoMT device and IoMT server) to maintain high privacy standards and trust during the monitoring process and enable the IoMT devices to run safely and independently even if the server is compromised. However, the session keys are controlled by the trusted IoMT server to lighten the IoMT devices’ overheads, and the session keys are securely exchanged between the client system and the monitoring server. The proposed RTPM focuses on addressing the major security requirements for an IoMT system, i.e., the CIA Triad, and conducts device authentication, protects from Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, and prevents non-repudiation attacks in real time. A self-healing solution during the network failure of live e-health monitoring is also incorporated in RTPM. The robustness and stress of the system are tested with different data types and by capturing live network traffic. The system’s performance is analysed using different security algorithms with different key sizes of RSA (1024 to 8192 bits), AES (128 to 256 bits), and SHA (256 bits) to support a resource-constraint-powered system when integrating with resource-demanding secure parameters and features. In the future, other security features like intrusion detection and prevention and the user’s experience and trust level of such a system will be tested.

Learning local cascading failure pattern from massive network failure data

Abstract This article proposes a novel multivariate point process regression model for a large-scale physically distributed network infrastructure with two failure modes, i.e. primary failures caused by the long-term usage and degradation of assets, and cascading failures triggered by primary failures in a short period. We exploit large-scale field pipe failure data from a UK-based water utility to support the rationale of considering the two failure modes. The two modes are not self-revealed in the data. To make the inference of the large-scale problem possible, we introduce a time window for cascading failures, based on which the likelihood of the pipe failure process can be decomposed into two parts, one for the primary failures and the other for the cascading failure processes modulated by the primary failure processes. The window length for cascading failures is treated as a tuning parameter, and determined through maximizing the likelihood based on all failure data. To illustrate the effectiveness of the model, two case studies are presented based on real data from the UK-based water utility. Interesting features of the cascading failures are identified from massive field pipe failure data. The results provide insights on advanced modelling and practical decision-making for both researchers and practitioners.

불균형 데이터의 분류성능 향상을 위한 데이터 리샘플링 효과

Imbalanced data is often found in many types of data subject to machine learning. Imbalanced data can be found in various fields such as fraud detection, unauthorized network intrusion detection, failure detection, and medical diagnosis, and it is known that if there is an imbalance problem in data, it affects the learning stage and reduces the classification performance of the learning model. Techniques to alleviate the imbalance data problem include an under-sampling technique that matches a class with a high distribution of data by a low class and an over-sampling technique that matches a class with a low distribution by a high class. In order to improve the classification performance of imbalanced data, this study aims to determine whether the problem of imbalanced data can be alleviated by applying various data resampling techniques to various analysis methods and then comparing the classification performance. To this end, an oversampling technique and an undersampling technique that can alleviate the problem of unbalanced data were briefly introduced, and a case analysis was conducted using financial data provided by DACON to compare the performance of the data resampling technique according to various analysis methods.

Deterministic networks using segment routing

The aim of the work is to analyze the new direction in telecommunications - creation of deterministic networks using segment routing technology within IPv6. The results obtained cover the consideration of network functions in a deterministic IP network (DIP), its architecture and principle of operation. The various aspects of segment routing (SR) technology are given, as well as the many use cases, incl. traffic engineering, network failure recovery, stateless chaining of services, introduction of 5G network "slices", cross-domain switching to connect users to applications on servers in multiple data centers, segment routing in SD-WAN. The main technologies of a DIP network are considered: traffic flow shaping (smoothing), input scheduling, time slot scheduling. The contributions relate to analysis and conclusions on the applicability, scope and development of DIP networks using segment routing technology within IPv6.