Network Failures Research Articles

Performance evaluation using hybrid glowworm swarm-Pareto optimization in the smart communication system

The Wireless Sensor Network (WSN) is described as a combination of sensor nodes that are installed in close proximity to one another in specific circumstances. Different research work is carried out for data sharing in the area of WSN and it cannot adapt to the network in the real-time network because of its characteristics. If the cluster head is selected without bothering the availability of resources, it results in network failure. Due to this, there will be more loss of packet and violation of security when they transfer the data collected from the cluster to the sink node which is a problem and still research is done in this area. Authentication is done to guarantee security among the CH and the sink/base station, and the base station must authenticate the CH. After the authentication task is completed, the data is transmitted. The main intent of the proposed scheme is to attain effective transmission and QoS by utilizing optimal node selection. Based on the findings of the simulation trials, the suggested approach outperformed the previous research studies.

Public administration and politics meet turbulence: The search for robust governance responses

Public administration and politics meet turbulence: The search for robust governance responses

Cascading failure of complex networks based on load redistribution and epidemic process

Diseases spreading over humans and animals is a ubiquitous process that has attracted much attention recently. To deeply explore the dynamics of the disease spread process on physical-contact networks, considering immunity exists in the population and dynamical behavior of viral transmission that have similar characteristics to cascading failure model based on complex network, the cascading failure model with load distribution parameters and epidemiological SIR model is proposed based on the local characteristic of nodes. Using this model, we investigate the virus outbreak conditions of Erdős–Rény (ER) and Albert–László Barabási (BA) networks, the formulas of critical values for networks recovery are derived. With numerical simulations, we obtain the relationship between the resilience of the networks against risk and the node redundancy factor. Furthermore, the intrinsic link between virus propagation rate and redundancy coefficient is obtained with the dynamical analysis, which is crucial for keeping networks robust against perturbations. These findings may help expand the understanding of the dynamic interactions between viral transmission and cascading failure, encouraging further study on the control and prevention of infectious disease spread.

A Fine-Grained Network Congestion Detection Based on Flow Watermarking

With the rapid development of the network, how to effectively reduce the dynamic delay and improve the performance of the network is an important and challenging problem. Specifically, network congestion is one of the key factors that hurt the network performance, so real-time detection of the network congestion is critical for recovering the network failure quickly. Current research in congestion detection mainly faces the problems of occupying extra bandwidth, decreasing the ratio of the effective payload of the packet, increasing the burden of the switches, etc. In this paper, we apply flow watermarking to network congestion detection and propose a fine-grained network congestion detection method based on flow watermarking. We also combine it with the eBPF (extended Berkeley Packet Filter) to improve the performance of congestion detection. Theoretical analysis and experimental results show that the changes in network status can be reflected in real-time and accurately in the watermark decoding information. The network congestion detection based on flow watermarking can detect network status on a small time scale and realize low-overhead and easily deployed congestion detection.

Analysis of Infrastructural Challenges, Cybercrime, and the Cashless Policy in Nigeria

Abstract
 This study makes an analysis of infrastructural challenges and the cashless policy in Kaduna state, Nigeria. A survey was conducted to collect the primary data through a structured questionnaire and interviews with bank customers, bank officials, and security personnel to obtain information on infrastructural challenges since the introduction of the Cashless policy by the Central Bank of Nigeria in 2012. Frequency distribution and simple percentage methods are used to analyze the data. Findings show that infrastructural challenges include network failure and infrastructural deficiency, debiting by Automated Teller Machine (ATM) without disbursing cash to beneficiaries, problems not rectified quickly by the banks, and charging by banks for using electronic banking methods. This leads to fraud, cybercrimes, and reduced confidence in the banking system. It is recommended that banks should be made to provide the minimum infrastructure required for cashless policy to succeed. In addition, there should be adequate cyber security measures implemented by banks and the government.

Deep learning technology of Internet of Things Blockchain in distribution network faults

AbstractNowadays, the development of human society and daily life are inseparable from the power supply. Therefore, people also put forward higher requirements for the reliability of distribution network, but power companies can only passively deal with distribution network failures, which is a bottleneck for the improvement of distribution network reliability. The Internet of Things (IoT) is the best solution for online equipment status monitoring and basic data sharing for large, widely distributed, relatively fixed, and large numbers of equipment. The construction of the IoT for power distribution equipment faces many important problems, including the selection of networking, equipment selection, and interaction standards. When researching the implementation plan, research on the distribution of IoT market was carried out. Based on the grid, the idea of optimizing the investment selection plan of the power distribution using IoT was discussed, and a result verification model was established. After the completion of the theoretical part, a case study of medium-voltage distribution grid equipment management and medium-voltage distribution network equipment management based on the grid was carried out by taking a real enterprise application situation as an example. Realizing fault diagnosis of distribution network will not only provide decision support for operation and maintenance of distribution network for power companies, but also have great economic and social benefits. Aiming at the shortcomings of single data mining method in distribution network fault diagnosis, hybrid data mining method is proposed. First, rough set theory is used to reduce the original fault data and form a simplified rule set. Because of the non-linearity of distribution network fault and the strong learning ability, adaptability, and robustness of Bayesian network, Bayesian network can be used to classify distribution network faults. Therefore, a simplified fault diagnosis system is established in this paper, and its correctness is confirmed. Then, the learning and training are carried out by using Bayesian network to call the simplest rule set, which has the characteristics of short learning and training time and high diagnostic accuracy.

Mineral and Ester Nanofluids as Dielectric Cooling Liquid for Power Transformers.

Amidst the new techniques facing the improvement of cooling and insulating efficiency and the design of electric transformers, constrained by the current technologies, one of the more promising is the substitution of traditional dielectric oils for nanofluids. Research on nanofluids for their application in transformers as a coolant and dielectric medium have been performed during the last two decades and continue today. This review tries to collect and analyze the available information in this field and to offer it already dissected to researchers, focusing on the preparation methods and how nanoparticles affect the main properties of the base fluids. Here we also addressed the influence of different parameters as particle characteristics or environmental conditions in nanofluids performance, the evolution with time of the measured properties, or the neighboring relationship of nanofluids with other transformer components. In this sense, the most reviewed articles reflect enhancements of thermal conductivity or dielectric strength, as well as an improvement of time evolution of these properties, with respect to those that are found in base fluids, and, also, a better interaction between these nanofluids and dielectric cellulosics. Thus, the use of dielectric nanofluids in transformers may allow these machines to work safer or over their design parameters, reducing the risk of failure of the electrical networks and enhancing their life expectancy. Nevertheless, these advantages will not be useful unless a proper stability of nanofluids is ensured, which is achieved in a small part of revised articles. A compendium of the preparation methodology with this aim is proposed, to be checked in future works.

Research on the application of artificial intelligence in computer network technology in the era of big data

SummaryIn order to improve the security and reliability of computer network, especially the three aspects of fault network diagnosis overall model, computer network link fault detection and computer network congestion prediction, we propose a fuzzy neural network identification and prediction model. In addition, we use rough artificial neural network (RANN) network link mixture to manage all subnets in the network for unified remote allocation network, so as to improve the quality and robustness of the network. The results show that the RANN learning and recognition algorithm we introduced achieves the best performance with 85.12% reduction in total time and 86.82% reduction in the number of steps. In addition, the average error rate of RANN is reduced by 45.94% compared with Lagrangian neural storage algorithm. The iteration number of fuzzy neural network (FNN) neural network prediction model based on incremental rule extraction algorithm is reduced by 28.93%, and the packet loss rate of network peak shaving module controlled by FNN prediction model is reduced to 0.272%, which is 87.69% lower than that of random early detection algorithm. This is expected to provide better performance for the orderly control of network failure, data flow and network data transmission with higher load in the future, and further improve the security and reliability of computer networks.

Self-Healing and Shortest Path in Optical Fiber Sensor Network

In this study, a new square-based fiber Bragg grating (FBG) sensor network model is proposed to address possible link failures in FBG sensor networks and improve their reliability. Graph theory and optical switching are simultaneously applied to these sensor networks to improve their self-healing ability; the FBG sensor network is regarded as a directed graph. Three commonly used self-short-circuit algorithms are compared in terms of the self-healing capabilities that they provide to the optical fiber sensor network. Among these, the shortest-path faster algorithm achieved a high, nearly 90% repair accuracy and had an average repair time of 0.103 s, the shortest in this study. The newly designed FBG self-healing network can be reorganized and repaired when local damage occurs, thereby improving its reliability.

Availability-Aware Multiobjective Task Allocation Algorithm for Internet of Things Networks

Node failures are known to be among the generic problems in Internet of Things (IoT) networks. These failures can be caused by communication disturbances, battery depletion, or even hardware faults. The larger the IoT network and the larger the task to be executed in the network, the higher is the probability of a node failure in the relevant part of the network. This article studies the node failures and proposes a new task allocation algorithm based on multiobjective optimization to address this issue. This article proposes a specialized archive-selection mechanism to enhance diversity in the search space of the so-called multiobjective task allocation algorithm (MOTA). High diversity in the archive allows a reliable selection of alternative task assignments in the case of node failures in the IoT network. We evaluate the performance of the proposed approach regarding the network lifetime, its latency, and its availability, using a network simulation model and compare the results with the baseline MOTA and the dynamic task allocation scheduler (DTAS). The results show that the proposed approach provides significant performance improvements over the existing algorithms, especially in scenarios with high task-to-node ratios.

Heuristic-Based Approach for Near-Optimal Response to Water Distribution Network Failures in Near Real Time

Heuristic-Based Approach for Near-Optimal Response to Water Distribution Network Failures in Near Real Time

Plan B: Design Methodology for Cyber-Physical Systems Robust to Timing Failures

Many Cyber-Physical Systems (CPS) have timing constraints that must be met by the cyber components (software and the network) to ensure safety. It is a tedious job to check if a CPS meets its timing requirement especially when it is distributed and the software and/or the underlying computing platforms are complex. Furthermore, the system design is brittle since a timing failure can still happen (e.g., network failure, soft error bit flip). In this article, we propose a new design methodology calledPlan Bwhere timing constraints of the CPS are monitored at runtime, and a proper backup routine is executed when a timing failure happens to ensure safety. We provide a model on how to express the desired timing behavior using a set of timing constructs in a C/C++ code and how to efficiently monitor them at the runtime. We showcase the effectiveness of our approach by conducting experiments on three case studies: (1) the full software stack for autonomous driving (Apollo), (2) a multi-agent system with 1/10th-scale model robots, and (3) a quadrotor for search and rescue application. We show that the system remains safe and stable even when intentional faults are injected to cause a timing failure. We also demonstrate that the system can achieve graceful degradation when a less extreme timing failure happens.

Epidemic Characteristics of HIV Drug Resistance in Hefei, Anhui Province.

To study the characteristics of HIV pretreatment drug resistance (PDR) and acquired drug resistance (ADR) in Hefei, a cross-sectional survey was used to collect 816 samples from newly reported HIV infections from 2017 to 2020 and 127 samples from HIV infections with virological failure from 2018 to 2019 in Hefei. HIV drug resistance levels and drug resistance mutations were interpreted using the Stanford Drug Resistance Database. Molecular networks were constructed by HIV-TRACE. Among the newly reported infections in Hefei, the prevalence of PDR was 6.4% (52/816). The drug resistance mutations were mainly V179E/D/T (12.4%), K103N (1.3%), and V106I/M (1.3%). In addition, it was found that the CRF55_01B subtype had a higher drug resistance rate than other subtypes (p < 0.05). Molecular network analysis found that K103N and V179E may be transmitted in the cluster of the CRF55_01B subtype. The prevalence of ADR among HIV infections with virological failure was 38.6% (49/127), and the drug resistance mutations were mainly M184V (24.4%), K103N/S (15.7%), Y181C (11.0%), G190S/A/E (10.2%), and V106M/I (10.2%). The molecular network was constructed by combining HIV infections with virological failure and newly reported infections; M184V and Y181C may be transmitted between them. The chi-square trend test results indicated that the higher the viral load level, the greater the number of newly reported infections linked to the infections with virological failure in the molecular network. In conclusion, interventions should focus on infections of the CRF55_01B subtype to reduce the transmission of drug-resistant strains. However, improving the treatment effect of HIV infections is beneficial for reducing the second-generation transmission of HIV.

Application of solar cells and wireless system for detecting faults in phasor measurement units using non-linear optimization

This research study examines the integration of wireless system and solar cells for detection and location of faults in power grids by implementing minimum number of Phasor Measurement Units (PMUs) after considering zero injection buses. A fault in the grid networks indicates a failure in the entire network that interfaces the normal flow of current which can be measured using wireless systems. The fault location in power grid is problematic because of high existence in several lateral tap-offs. In addition, the identification of exact location with presence of faults in the power system and severity of the faults are challenging tasks due to bulk and complex nature of the electrical power system state estimation. The major requirement of PMU placement approach is to find the minimum number of PMUs and their location with maximum wireless coverage under fault conditions with a prerequisite for the projected algorithm. The integrated algorithm is tested and validated for different fault conditions where the results are compared with different conventional methods using different optimization models such as heuristics, decision rules and mathematical programming where the outcomes proves to be much effective in terms of number of PMU installation for proper placements and detection, location in number of fault systems thus achieving 82% reliability using Ant Lion Optimizer (ALO).

Analysis of conditions and factors affecting cyber security in the special purpose information and telecommunication system

The increase in the number of cases of failure of information and telecommunication networks due to cyber attacks determines the need to protect them from this type of attacks. The issue of increasing cyber security in the conditions of conducting operations by groups of troops (forces) is very important. Due to the armed conflict in the East of Ukraine, the military-political instability in the Middle East, the struggle for influence on world financial and energy flows, the global military-political instability is intensifying. This is due to an increase in the number of communication devices in information and telecommunication networks, as well as an increase in the number of possible attacks that can be used to disrupt the operation of an information and telecommunication network. Considering the above, the object of research is a special purpose information and telecommunication system. The subject of research is cyber security of a special purpose information and telecommunication system. Classical methods of scientific knowledge, namely analysis and synthesis, were used during the research. The research identifies factors that affect cyber security in a special purpose information and telecommunications system. All this must be taken into account while planning and deploying a special purpose information and telecommunication system. The analysis of the consequences of the impact on the information and telecommunications system of special purpose of modern devices of defeat and the impact of devices of radio-electronic suppression and other factors was carried out. A typical special purpose information and telecommunication system does not fully meet the requirements for constant readiness to ensure the management of troops (forces), stability, mobility and throughput. A formalized description of the task of improving cyber security in a special purpose information and telecommunications network is provided. The components that affect the level of cyber security of the special purpose information and telecommunication network during the group's operations have been established. The impact of the specified conditions and factors must be reflected: in the planning documents during the planning of the deployment and operation of the group's information and telecommunications system; in the software, during operational management.

Cascading expert failure

AbstractRecent research has shown how experts may fail in their duty as advisors by providing advice that leads to a worse outcome than that anticipated by the user of expert opinion. However, those models have focused on the immediate effects of the failure on experts and nonexperts. Using a cascading network failure model, I show how expert failure can cascade throughout multiple sectors, even those not necessarily purchasing the expert opinion. Consequently, even relatively small failures end up having outsized aggregate effects. To provide evidence of my theory, I look at two case studies of COVID expert advice to show how one seemingly minor failure ended up contributing to the pandemic. I conclude with a discussion on institutional frameworks that can prevent such cascades.

Robustness Analysis of CPPS considering Power Flow Constraints

Comprehensively considering the power flow constraints of the power network, the cyber-physical power system (CPPS) network integration model is established by combining with the topological structure information of the power network and information network. The Newton–Raphson method is used to solve the AC power flow distribution in the power network. Based on the interdependent network theory, a CPPS cascading fault analysis procedure considering the power flow constraints is given. Finally, the importance of CPPS network nodes is evaluated and analyzed. Simulation experiments are carried out with the IEEE39 node system as an example. The results show that some electrical components failures in the power network may lead to large-scale transfer of power flow, resulting in overloading of other electrical components or transmission lines. It promotes further cascade propagation of failures in the CPPS network, which eventually leads to a sharp decrease in the robustness of CPPS.

Hybrid of monolithic and staggered solution techniques for the computational analysis of fracture, assessed on fibrous network mechanics

The computational analysis of fiber network fracture is an emerging field with application to paper, rubber-like materials, hydrogels, soft biological tissue, and composites. Fiber networks are often described as probabilistic structures of interacting one-dimensional elements, such as truss-bars and beams. Failure may then be modeled as strong discontinuities in the displacement field that are directly embedded within the structural finite elements. As for other strain-softening materials, the tangent stiffness matrix can be non-positive definite, which diminishes the robustness of the solution of the coupled (monolithic) two-field problem. Its uncoupling, and thus the use of a staggered solution method where the field variables are solved alternatingly, avoids such difficulties and results in a stable, but sub-optimally converging solution method. In the present work, we evaluate the staggered against the monolithic solution approach and assess their computational performance in the analysis of fiber network failure. We then propose a hybrid solution technique that optimizes the performance and robustness of the computational analysis. It represents a matrix regularization technique that retains a positive definite element stiffness matrix while approaching the tangent stiffness matrix of the monolithic problem. Given the problems investigated in this work, the hybrid solution approach is up to 30 times faster than the staggered approach, where its superiority is most pronounced at large loading increments. The approach is general and may also accelerate the computational analysis of other failure problems.

The role of fintech in driving financial inclusion in developing and emerging markets: issues, challenges and prospects

PurposeIn recent times, various governments in the developing and emerging markets are increasingly embracing financial technology to help improve financial inclusion and integration within the governments' countries. One of the primary goals of using such technology is to reduce poverty. This paper explores Fintech innovations' effectiveness in developing and emerging markets in driving financial inclusion using Nigeria as a case study. The paper explores the challenges militating against financial inclusion and the role of government, financial institutions, and fintech companies in ensuring financial inclusion for the vast majority of the unbanked population in the developing and emerging markets.Design/methodology/approachThis paper is based on doctrinal, sociological, and comparative research methodologies. The researchers conducted a content analysis drawing on data from both primary and secondary sources, including existing legislation, journal articles, newspaper reports, and policy documents.FindingsThe research showed that the financial inclusion gap has expanded despite the government, regulators, and financial institutions' various efforts by developing various digital platforms, including encouraging the use of smartphones for mobile payments and automated teller machines (ATMs) and mobile money. Several reasons are responsible for the gap in financial inclusion: illiteracy, poor infrastructural facilities, intermittent power supply, poor mobile receptions, especially in rural areas, constant banks' network failures, unnecessary charges, information asymmetry and data privacy breaches, amongst others.Practical implicationsFinancial inclusion through fintech is essential in eradicating poverty in developing and emerging markets if adequately implemented. Therefore, this paper will be useful to researchers exploring how technology influences financial inclusion. The paper will also aid policymakers and practitioners in financial technology regulation to improve the effectiveness of policymakers and practitioners' policies and implementation strategies of financial inclusion in developing and emerging markets.Originality/valueThis research is significant, especially in developing and emerging markets, by exploring issues and challenges of fintech in promoting financial inclusion in challenging institutional contexts. This paper suggested potential areas for further research, particularly women's attitudes and expectations towards services provided by fintech companies and other financial institutions.

ARCMT: Anchor node-based range free cooperative multi trusted secure underwater localization using fuzzifier

Secure and precise node localization is a critical challenge for the underwater acoustic sensor network. The sensor node distribution in the aquatic environment is the most vulnerable to many external attacks, interrupting the nodes to find the precise location information and causing complete network failure due to excessive, unnecessary communication and power consumption. This paper proposes an anchor nodes-based range-free cooperative multi-trust management system using fuzzy logic to provide a secure and reliable underwater localization. The multi-trust-based scheme protects the sensor nodes from untrusted or malicious nodes, and the lightweight Mamdani fuzzy model is used to reduce circuit complexity and memory consumption. The proposed scheme does not require the cluster head to perform trust-related tasks, which reduces the heavy overburden on the cluster heads with their regular task and energy budget to enhance their lifetime. Therefore, the proposed method uses Anchor node-based joint trust calculation and localization of unlocalized cluster members, providing a better reliable trust evaluation and better accuracy and stability in terms of the localization error. The simulation report shows that the proposed scheme improves the average accuracy by 10.77%, average stability by 23.7%, average malicious node detection rate by 42.4%, and average false detection rate by 38.25% compared to other single-cluster head-based and traditional energy trust-based systems.