Network Failures Research Articles

Customers Reactions on Service Delivery by Major Telecommunications Operators in Nigerian Newspapers

Data from the Nigeria Communications Commission (NCC) stated that MTN lost a record of 667,245 Internet subscribers in one month in 2019.Figure shows that MTN’s total Internet subscribers stood at 52.2 million in July 2019 while the number dropped to 51.6 million in August 2019. It was observed that customers’ reactions bordered on issues ranging from unsolicited calls and short message services (SMS), unsubstantiated deductions of call credit units, poor service connection, network failure, cumbersome SIM swap process among others. This paper, thus, examines two Nigerian national dailies-The Punch and The Nation newspapers coverage of customers’ reaction to mobile telecommunication network services from 2015 to 2017. Using the content analysis research method, the study is anchored on the Social Exchange theory. The findings revealed a significant pattern of more negative than positive reactions by subscribers within the years under study. The negative complaints were high in 2015, dropped significantly in 2016, and rose again in 2017. On this, MTN recorded 15 (or 32%); Airtel 13 (or 28%), Glo10 (or 21%), while Etisalat (now 9Mobile) was 9 (or 19%), as against Etisalat (9 Mobile), 9 (or 19%), Glo 4 (or 8%) and Airtel 3 (or 6%) positive reactions by the customers during the period. Study thus recommends that Nigerian newspapers should once in a while carryout a survey on quality of service delivery by GSM operators and use such stories on the front page of their respective newspapers.

Cascading failures in interdependent networks with reinforced crucial nodes and dependency groups

Previous studies of group percolation models in interdependent networks with reinforced nodes have rarely addressed the effects of the degree of reinforced nodes and the heterogeneity of group size distribution. In this paper, a cascading failure model in interdependent networks with reinforced crucial nodes and dependency groups is investigated numerically and analytically. For each group, we assume that if all the nodes in a group fail on one network, a node on another network that depends on that group will fail. We find that rich percolation transitions can be classified into three types: discontinuous, continuous, and hybrid phase transitions, which depend on the density of reinforced crucial nodes, the group size, and the heterogeneity of group size distribution. Importantly, our proposed crucial reinforced method has higher reinforcement efficiency than the random reinforced method. More significantly, we develop a general theoretical framework to calculate the percolation transition points and the shift point of percolation types. Simulation results show that the robustness of interdependent networks can be improved by increasing the density of reinforced crucial nodes, the group size, and the heterogeneity of group size distribution. Our theoretical results can well agree with numerical simulations. These findings might develop a new perspective for designing more resilient interdependent infrastructure networks.

Robustness of edge-coupled interdependent networks with reinforced edges

Abstract Previous studies on cascade failures in interdependent networks have mainly focused on node coupling relationships. However, in realistic scenarios, interactions often occur at the edges connecting nodes rather than at the nodes themselves, giving rise to edge-coupled interdependent networks. In this article, we extend the model of partially edge-coupled interdependent networks by introducing reinforced edges with a ratio of ρ. We analyse the formation of finite surviving components in edge-coupled networks, wherein the reinforced edges can function and support their neighbouring nodes to form functional components. To accomplish this, we develop a framework through a detailed mathematical derivation of the proposed model. We then investigate the critical value ρ* of the reinforced edge ratio that can change the phase transition type of the network. Our model is verified by theoretical analysis, simulation experiments and real network systems. The results show that the introduction of a small proportion of reinforced edges in the edge-coupled interdependent network can avoid the sudden collapse of the network and significantly improve the robustness of the network.

Exploring the effectiveness of node attacks based on combined centrality measures in scale-free networks

Abstract This article introduces a novel method for targeting complex networks that involves using a hybrid centrality score to rank nodes and carry out attacks. Unlike previous studies that have focused on using individual centrality measures, this approach takes into consideration the varying significance of nodes across different centrality measures. The study utilizes simulations on scale-free networks to demonstrate that the proposed strategy can be highly effective in inducing network failure, and certain combinations of centrality measures can result in greater attack severity than using individual measures alone. Overall, the research offers valuable insights into improving node-attack strategies for complex networks, which are typically resilient to random failures but susceptible to targeted attacks.

Smart Real-Time IoT mHealth-based Conceptual Framework for Healthcare Services Provision during Network Failures

A series of healthcare problems related to frequent failures in telemedicine architecture, particularly in multi-sensors (Tier 1), medical center servers (Tier 3), and potential failures in network integration between these system components, needed to be addressed. The objective of this research was to present a novel smart real-time IoT mHealth framework within the context of IoT that could select an appropriate hospital during the aforementioned failures. The research methodology involved a new local multi-sensor fusion triage algorithm called the three-level localization triage (3LLT). This aimed to exclude the control process of patient triage and sensor fusion from the medical center, while also alarming failures related to medical body sensors. Additionally, the proposed framework was implemented using the multi-criteria decision-making (MCDM) method, connecting mHealth directly with distributed hospital servers. The distribution of hospitals was determined using the AHP (Analytic Hierarchy Process) based on the crossover between ‘healthcare services/time of arrival of the patient at the hospital’ and ‘hospitals list’ to estimate small power consumption. Validation processes were conducted for the proposed framework. The expected output from this research is to enhance the provision of healthcare services during various network failures.

Inter-Core Crosstalk-Aware Backup Network Design Model against Probabilistic Link Failures in Multi-Core Fiber Optical Path Network

Multi-core fiber (MCF) is one of the promising space-division multiplexing technologies to increase the capacity of optical networks. MCF-based networks have two challenges. One is the inter-core crosstalk (XT) that degrades the quality of optical signals in two neighboring fiber cores. The other is network protection against link failures that cause massive data loss. One way to protect against multiple link failures is to prepare physically separated links as a backup network. Probabilistic protection improves the efficiency of protection by allowing a certain probability of protection failure. Existing studies on backup network design with probabilistic protection do not target MCF-based networks, which raises problems such as protection failure due to the inter-core XT and excessive consumption of optical resources. To address these problems, this paper proposes a XT-aware backup network design model for the MCF optical path networks. The proposed model protects the network against probabilistic multiple link failures. We adopt probabilistic protection that allows a certain probability of protection failure due to the inter-core XT and minimizes the required number of links in the backup network. We present an algorithm to satisfy the probabilistic protection requirement and formulate the model as an integer linear programming problem. We develop a heuristic approach to apply the proposed model to larger networks. Numerical results observe that the proposed model requires fewer links than the dedicated allocation model, which provisions the backup paths in the same manner as the primary paths.

The Failure Risk Analysis of the Water Supply Network

The primary objective of this work is to introduce a novel approach that modifies the method for analyzing and assessing the risk of water supply network failure. The approach aligns with recommendations from the World Health Organization and the European Union regarding the reliability and safety of water supply to consumers. The presented method for assessing the risk in the water distribution subsystem was based on the vulnerability identifying method (VIM) and involves the determination of the vulnerability index (VI). The VIM vulnerability factors considered encompass the failure rate, chemical stability of water, and issues related to water corrosion properties in water distribution subsystems. The obtained risk assessment includes parameters such as the probability of hazard occurrence, the consequences of these hazards, and vulnerability to them. This concept was evaluated using real operational data from the water distribution subsystem. The estimated risk level, under the given operating conditions, indicates its acceptability.

Resilience of Networks in Intelligent Urban Environments

As urban areas develop to integrate new technology, smart city infrastructures depend significantly on seamless and resilient networks for operational stability, safety, and service continuity. This article examines the essential significance of robust network infrastructure in smart cities and emphasizes techniques aimed at maintaining uninterrupted connectivity during disasters, network failures, or peak demand periods. This article seeks to deliver a thorough analysis of resilient network practices that protect vital infrastructure, sustain data flows, and facilitate key services by investigating real-world case studies, contemporary best practices, and available technology. Principal findings underscore the incorporation of decentralized networking models, adaptive routing protocols, and hybrid connectivity systems as vital components in enhancing smart city resilience.

An efficient evacuation path optimization for passengers in subway stations under floods

The optimization of passenger evacuation paths in subway stations under flood scenarios plays an important role in improving evacuation efficiency and ensuring escape safety. Considering the impact of evacuation network failures such as gates on path planning, a two-stage passenger evacuation path optimization method under flood scenarios of subway stations is established with the objectives of minimizing total evacuation time, risk, and congestion. The black widow algorithm is proposed to optimize the BP neural network model to predict the travel time of passengers at nodes, which could improve the prediction accuracy of calculating the total evacuation time. The path optimization model is solved by the NSGA-II algorithm, and the optimal Pareto solution is determined based on the minimum total cost. Taking a subway station in Qingdao, China as an example, a passenger evacuation simulation system under flood scenarios is built using PathFinder software. The effect of the path optimization strategy is comprehensively evaluated through comparative experiments. It is found that the overall evacuation optimization degree could be increased by 18.75%, by comparing the evacuation time, congestion, and risk objectives under the situations with and without path optimization strategies.

In-depth Analysis of Kubernetes Node and Pod Communication in Google Kubernetes Engine——Exploring the Power of GKE Networking

This paper analyzes the communication between Kubernetes nodes and pods in Google Kubernetes Engine (GKE). We explore the structure and features of GKE networking, including communication between nodes, nodes, and pods, and pods themselves. We also consider the network security and performance aspects. In addition, we analyze common networking issues such as latency, packet loss, and network failures and provide solutions. Through our experimental results, we demonstrate the effectiveness of our proposed solutions and offer insights for future research in this area. This paper provides valuable insights for developers and operators who use GKE and want to optimize their networking configuration.

Improving node connectivity by optimized dual tree-based effective node consolidation

As an effective way to defend against network failure, nodes or links in a network can be protected to ensure connectivity. The consolidation of critical nodes is a necessary strategy, especially when nodes are important infrastructure elements, such as in wireless environments. However, since the problem is NP-hard, even the optimal algorithm is of high computational complexity on small-scale graphs, which makes it difficult to handle large-scale networks in feasible time. Fortunately, we have found an effective pairwise node cut set (NCS)-tree search method to locate small NCSes. The critical nodes in these NCSes are then selected to construct the optimal dual tree, and to determine the near optimal set of consolidation nodes. Comparison results show that the serial version of the proposed algorithm achieves a remarkable acceleration ratio of more than 105 compared to the optimal algorithm, with only a minimal additional cost. In addition, the consolidation solution provides protection for over 99.9% of vulnerable node pairs in large-scale networks and can significantly outperform common heuristics. Moreover, the ready-to-parallelization algorithm has the potential to be further accelerated hundreds of times in full parallelization.

Interaction graph learning of line cascading failure in power networks and its statistical properties

We consider line failure cascading in power networks where an initial random failure of a few lines leads to consecutive other line overloads and failures before the system settles in a steady state. Such cascades are rooted in non-obvious, long-range, and higher-order couplings among the lines’ flows induced by physical constraints on the network. Failure interaction graph encodes which and to what extent other lines in a networked system are affected after each line failure and can help to predict the final state after an initial disturbance. We perform data analytics on the final lines’ steady states of cascade trajectories to infer a specific line’s state given the states of others. We use a generative model to reconstruct possible steady states, and a predictive model aims to predict the probability of each line’s failures after the initial failure as a regression problem. The generative model uses regularized pseudolikelihood estimator to infer interaction weights by solving the inverse Ising problem and deploys Glauber dynamics to generate steady states. The discriminative model uses boosted trees to efficiently learn over training and predict over test data the state of each line as a target finding an appropriate subset of other lines’ states as explanatory variables. We analyze the degree distribution of the corresponding interaction graphs to study the number of other components affected by each line failure (out-degree) or the number of lines that affect the state of a given line (in-degree). Both models show that the in-degree follows a power-law distribution. Finally, we discuss the possible application of the interaction graph for early link removal to mitigate the failure-cascading consequences.

Failure of load-bearing dyke networks as a trigger for volcanic edifice collapse

Most large ocean-island volcanoes are gravitationally unstable. Some deform slowly, forming long-lived slumps, while others collapse and generate potentially dangerous debris avalanches. Here we investigate the effect of pervasive dyke networks on edifice instability, using data from La Palma, Spain. Like fibre-reinforced composites, where rigid layers are embedded in a compliant matrix, we find that dykes experience higher stress than surrounding host rocks. If the ratio of dyke to host stiffness is larger than the corresponding strength ratio, the dyke network will fail first, causing a rapid stress redistribution and possibly triggering edifice collapse. Fibre bundle models of a weak layer crosscut by dykes suggest this can occur with less seismicity or deformation than models without dykes. The models also suggest that dyke network strength could determine the potential for rapid collapse rather than gradual slump-type deformation. We conclude that dyke networks should be considered when assessing volcanic edifice stability.

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

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

Predicting mechanical and electrical failure of nanowire networks in flexible transparent electrodes

Flexible transparent electrodes employing metal nanowires (NWs) find extensive use in various applications such as optoelectronic devices, solar cells, light-emitting diodes, and transparent heaters. NW networks in flexible transparent electrodes can withstand mechanical deformations and conduct electricity but are susceptible to localized damage caused by mechanical stress and current density concentration. This localized damage ultimately results in electrode failure. Our study aims to track locally induced damage from both mechanical and electrical sources and assess their collective influence on electrode performance until failure occurs. To this end, we create two-dimensional digital samples that represent the NW networks, transform them into beam networks and equivalent resistor networks, and perform finite element simulations of the mechanical and electrical network responses while varying the NW content.Our simulations reveal crack-like patterns in the distribution of damaged elements at network failure that depend on the process inducing the damage. While our results suggest that the impact of electrically induced damage on overall network stability is more significant than that of mechanically induced damage, the latter must not be ignored.

Cascading failure in urban rail transit network considering demand variation and time delay

To study the cascading failure in real-world urban rail transit network, the cascading failure model is proposed by considering two essential characteristics of demand variation and time delay, which has rarely been considered in most existing studies. Specifically, the model utilizes a simulation-based approach where passengers in the rail transit network are abstracted as packets moving through the network. The movement of these packets, including their response to station failures, is planned to model the travel behavior of passengers. The Shanghai rail transit network is chosen as the case study for this research. Comparative experiments suggest that the proposed model may produce very different results for stations with high closeness and enough travel demand. The proposed model can depict the dynamic failure process and yield a longer and more dynamic failure process, which is beneficial for managers to identify the period for prevention. Besides, sensitivity analysis shows that higher travel demand is more likely to cause a cascading failure, while time delay slightly impacts the final cascade size but affects propagation duration. In summary, travel demand plays a vital role in cascading failures, and the presence of time delays complicates the cascading failure propagation process. The research results provide references and support for studying cascading failure in real complex environments.

Electronic Businesses, Services and Profitability of Banking Sector in Nigeria (Bank-Focused Theory, Bank-Led Theory, Non-Bank Led Theory)

The introduction of e-banking has indeed had a positive effect on the profitability of the bank since it was introduced. It has also improved the banks customer relationship by rendering effective services. Network failure from internet connection and the break-down of ATMs, fraud and general infrastructure are major challenge facing customers using e- banking products in Africa. However, banks? profitability has been enhanced due to the streamlining of operations brought about by technology mainly driven by electronic banking platforms. This research examined between E-Banking services and Commercial Bank Profitability in Nigeria using A Cointegrationand Causality Approach. Electronic banking was proxied by value of Point-of-Sale, USSD, Internet Banking, Mobile Banking, ATM transactions while commercial banking performance was proxied by net profit from electronic banking transfer/processing. Engle-Granger cointegration model was used to analyse data for the sample period January 2008 to December 2022. The study’s findings revealed that ATM (Automated Teller Machine) and POS (Point of Sale) long-run relationship with bank performance. The result of the short-run indicates that an ATM (Automated Teller Machine), USSD services and POS (Point of Sale) have a positive significant effect on NP (Net Profit). Internet banking services (IBS) and Mobile banking Services (MBS) have a negative and positive insignificant effect on NP (Net Profit). The value of the adjusted R2 (0.508994) indicates that POS (Point of Sales), USSD services (USS), ATM (Automated Teller Machine), Internet Banking Services (IBS) and Mobile Banking Services(MBS) explain 50.89% of the variation in NP (Net Profit margin from the electronic businesses), while the remaining 49.11% is captured outside the model. The findings of the study are consistent with Amu and Nathaniel (2016); Gichungu and Oloko (2015), but inconsistent with Kiragu (2017); Obiekwe and Anyanwaokoro (2017). It is

Modeling cascading failures of urban rail transit network based on passenger spatiotemporal heterogeneity

To explore and alleviate large-scale failures under urban rail transit network accidents, cascading failures are usually investigated integrating the network topology and passenger flow. However, the impacts of the heterogeneity of travel demand and its interactions with the spatiotemporal heterogeneity of rail passenger flow on cascading failures are still left unknown. In this study, the dynamic changes of origin-destination travel demand under failures, the time-delay of cascading failures and the interaction between these dynamic properties and spatiotemporal heterogeneity of passenger flow are modeled and explored in an improved disaster spread theory model. The approach is applied to Shanghai urban rail transit network under different attack scenarios. Results show that the scale of cascading failures on Shanghai urban rail transit network importantly depends on the ratio of passengers changing travel demand and time-delay of cascading failures. Besides, large in-flow of stations adjacent to the failed station would not only cause fast spread of cascading failures at the early stage but also result in many failed stations in the end. The heterogeneity of passenger flows in different directions on the network would reduce the scales of cascading failures. It is also found out that the circle line could obviously prevent the spread of cascading failures.

Model cascading overload failure and dynamic vulnerability analysis of facility network of metro station

The existing studies mainly focused on the cascading failure of the whole metro but rarely on stations. In this paper, we map a metro station into a directed and hierarchical network, and propose a cascading overload failure model to study the network dynamic vulnerability. The passenger behavior and node load update are considered based on impedance function and mutual flow redistribution. Without changing the network topology, a flow optimization strategy is proposed to improve the network performance against cascading failure. Yuantong station of Nanjing Metro is studied. The results show the network is vulnerable and global failure occurs easily under small node tolerance. When the node tolerance excesses a critical value, the network can transit abruptly from global failure to balance state. We obtain that the critical node tolerances are 0.17 for the largest degree-based attack(LDA) and 0.19 for the largest load-based attack(LLA). The ranges of the optimal flow redistribution coefficient are identified by the flow optimization strategy, which are [0.15 0.2] for LDA and [0.2 0.25] for LLA. The result indicates that a proper guidance of passenger flow can be beneficial to the network performance while a strong intervention to passenger flow can instead increase the cascading failure risk.

Failure Analysis of Urban Rail Transit Networks Incorporating Ridership Patterns

Abstract In any nonlinear system as complex as an urban rail transit network or metrorail network, some incidence of perturbations of its state is inevitable. These perturbations, such as natural hazards, can highly affect the networks' resilience. Increasing the ability of metrorail networks to withstand such perturbations requires robustness and vulnerability assessments as key attributes of resilience and necessary steps toward developing reliable networks. Most models developed for this purpose associate a network's failures to binary representations of the failure of its components without incorporating weight factors. Since ridership is a primary factor to define the metrorail network performance, this paper proposes a general ridership pattern, considers different failure cases, and uses a novel methodology to quantitatively measure the weighted-network resilience attributes incorporating ridership throughout the Washington, DC Metrorail as a case study. The proposed methodology has clear relationships to adjacency and link-weight matrices and defines a new expression for the weighted global network efficiency based on the sum of weights on each geodesic path. Results show that the most vulnerable stations and links hold critical positions in the network topological structure and/or bear larger amounts of ridership. For the case study, the most vulnerable components include transfer stations located in the city center as well as stations and links on the northwest section of the Red Line. The methodology presented herein provides insights for enhancing critical components during the planning and operation of a metrorail by mitigating the risks associated with failure events.