Reliability Analysis Of Networks Research Articles

Network reliability analysis on casualty rescue for natural disaster evaluation

Network reliability analysis on casualty rescue for natural disaster evaluation

Supply reliability analysis of natural gas pipeline network based on demand-side economic loss risk

• An evaluation method for the economic loss risk on the demand side of a natural gas pipeline network system in a certain period is proposed. • User priorities are considered when there is a shortage of gas in the pipeline network. • The economic consequences on the demand side are quantified in terms of the alternative energy costs. • The effect of supply sequence of users on gas supply reliability is described. Most studies on the natural gas supply reliability evaluation have reflected on whether the volume of gas supply to users is sufficient. However, different types of users suffer from the same natural gas shortage, the consequences of loss are different. The abundance of the supplied gas volumes does not reflect the actual losses incurred by users. Moreover, when a gas shortage occurs, the supply flow of different types of users is redistributed according to the supply order. Therefore, this study proposes an evaluation method for the gas supply reliability based on demand-side economic risk. The user's supply sequence is integrated into the cost matrix to optimize the distribution of the user's demand flow under gas shortage. A calculation model of the economic loss cost is established using alternative energy sources. Finally, a reliability index is established based on economic risk to evaluate the degree of supply security. The results showed that the reliability based on the economic risk assessment was slightly higher than that based on the gas quantity assessment. The reliabilities were 99.988% and 99.991%, respectively. The economic risk is reduced and the reliability is improved for the important users after considering the influence of the demand-side supply order.

Reliability assessment of a stochastic-flow distribution network with carbon emission constraint

• Emission is incorporated into reliability analysis of a distribution network • A new performance indicator is proposed to assess a distribution network • An MP based algorithm is developed to compute the new indicator • A real case study of a fruit distribution network is presented This paper proposes a new reliability assessment for a distribution network with carbon emission constraint where a node represents a supplier, a distribution center, or a market, and an arc connecting a pair of nodes represents a carrier. The available capacity of each carrier (e.g., number of vehicles) is stochastic due to unexpected events. In this sense, such a distribution network is considered to be a stochastic-flow distribution network (SFDN). In addition, transportation emissions that have direct hazardous effects on environment and human health are becoming the major concern in the process of commodity distribution. Thus, SFDN reliability with carbon emission constraint, defined as the probability that the network can successfully deliver a specified quantity of goods from the source to the destination with the total transportation emissions no more than a given limitation, is taken as a key performance indicator for distribution activity. Grounded on minimal paths (MPs), an algorithm is proposed to calculate the new indicator. A simple example is provided to illustrate the procedure, and then, a real fruit distribution network is adopted to demonstrate the utility of the proposed algorithm.

Reliability analysis and recovery measure of an urban water network

<abstract><p>Urban water networks are important infrastructures for cities. However, urban water networks are vulnerable to natural disasters, causing interruptions in water. A timely analysis of the reliability of urban water networks to natural disasters can reduce the impact of natural disasters. In this paper, from the perspective of network reliability, the reliability analysis method of urban water networks under disaster is proposed. First, a reliability model is established with the flow rate of nodes in the water network as the index. Second, the user's demand is considered, as well as the impact of water pressure on water use. Therefore, a node failure model considering node water pressure and flow rate is established. The performance degradation of the urban water network is analyzed by analyzing the cascading failure process of the network. Third, the recovery process of the urban water network is analyzed, and the changes in the reliability of the urban water network before and after the disaster are analyzed to assess the ability of the urban water network to resist the disaster. Finally, an urban water network consisting of 28 nodes, 42 edges and 4 reservoirs is used to verify the effectiveness of the proposed method.</p></abstract>

Approximation Knowledge-Based Recurrent Neural Network for Estimating N-Terminal Reliability

The main problem dispersed with in this paper is to find a novel method for the improvement in the reliability analysis of Computer Network. Reliability prediction are estimated during the life cycle of a computer network with the aim of estimating failure. In designing a variable size network, the serviceability, availability and reliability of the any network is a primary consideration. The reliability calculation in varying size network is a problem of NP-hard; it requires more calculation and effort with the amplifying no of nodes and links. Many different approaches have been taken for reliability and probability calculation for triumphant communication between any pair of computers. The paper presents a method for identifying n-terminal network reliability based on RNN technique. The method derived in this paper preceding inputs which increases the speed of computation. The approach works efficiently and overcome the difficulties of the previous approaches defined with neural network model and other reliability estimation techniques. It is proposed that the RNN model be used to replace the most time-consuming component of the system reliability evaluation approach. A variable-length sequence input can be handled by RNN. The main goal of this paper is to predict asperity of reliability which is highly correlated with performance of network in any unfavorable conditions.

Entropy determination in the problem of the analysing an electrical network state under the condition of independence of functioning of its elements

The possibility of applying the measure of information uncertainty (entropy) in structural reliability analysis of electricity distribution networks is assessed. The essential factors influencing the process of reliability analysis and related to the presence of information uncertainty are highlighted. The peculiarities of reliability analysis in network designing and operation, when it is required to estimate the state of elements and choose the efficient structure of a network are considered. The opportunities and limitations in the application of existing methods of structural reliability analysis are evaluated; it proves the importance of information uncertainty measurement. The conceptual apparatus of entropy includes the mathematical procedure of its calculation. Fail-safe operation probability and probability of network element failure are taken as reliability index. The mathematical expressions proposed for calculating entropy are applicable when there are two independently functioning network elements, each of which will be able to be in one of two states with a fraction of probability: operable and non-operable. The obtained results allowed identifying the more reliable elements/objects. The application of the proposed mathematical procedure allows to compare network elements with each other through the value of entropy and to choose the most reliable ones. Thus, the proposed solution is a preliminary step in the uncertainty removal task.

Reliability Analysis of Survivable Networks under the Hostile Model

This article studies the Generalized Steiner Problem with Node-Connectivity Constraints and Hostile Reliability and introduces a metaheuristic resolution approach based on Greedy Randomized Adaptive Search Procedure and Variable Neighborhood Descent. Under the hostile model, nodes and links are subject to probabilistic failures. The research focuses on studying the relationship between the optimization and the reliability evaluation in a symmetric network design problem. Relevant research questions are addressed, linking the number of feasible networks for the full probabilistic model, the sensitivity with respect to elementary probabilities of operation for both edges and nodes, and the sensitivity of the model with respect to the symmetric connectivity constraints defined for terminal nodes. The main result indicates that, for the hostile model, it is better at improving the elementary probabilities of operation of links than improving the elementary probabilities of Steiner nodes, to meet a required reliability threshold for the designed network.

Reliability Analysis of the Cactus-Based Networks Based on Subsystem

Abstract Multiprocessor systems play a significant role in big data era. As the probability of presence of processor failures in a multiprocessor system raises with the increase of the system scale, the effect of processor failure is worthy of quantifying. The subsystem reliability of a multiprocessor system is the probability that a fault-free subsystem of certain size still operate with the rise of individual faults. In this work, we employ the probabilistic fault model and the Principle of Inclusion-Exclusion (PIE) to establish the approximation and upper bound on the subsystem reliability of the cactus-based networks through decomposition into $(n-1)$-dimensional subsystems by fixing one position-pair. Numerical simulations show that the upper bound derived in this way is close to the approximation of the accurate subsystem reliability.

Reliability of Augmented 3-Ary n-Cubes with Extra Faults

Fault tolerance is critical to reliability analysis of interconnection networks since the vulnerability of component failure increases with the growth of network scale. Extra connectivity and extra diagnosability are two decisive indicators of the ability of parallel and distributed systems to tolerate and diagnose faulty nodes. This paper mainly establishes the [Formula: see text]-extra connectivity and [Formula: see text]-extra diagnosability of augmented [Formula: see text]-ary [Formula: see text]-cubes [Formula: see text], which is a generalization of [Formula: see text]-ary [Formula: see text]-cubes and augmented cubes. In addition, we explore the [Formula: see text]-extra diagnosis algorithm of [Formula: see text] under the MM* model.

Reliability Analysis of Cognitive Radio Networks With Reserved Spectrum for 6G-IoT

Cognitive radio networks (CRNs) can facilitate ultra-reliable communication among IoT devices in the 6G environment by enhancing channel availability (CA) for primary and secondary users. However, CA does not necessarily lead to successful connection establishment unless receiver’s accessibility (RA) is guaranteed. This motivates us to propose the notion of connection availability (CoA) that incorporates RA into CA. We also introduce the idea of service maintainability (SM) that includes the effect of RA in service retainability. Additionally, spectrum utilization efficiency (SUE) is expressed and analyzed with and without considering the impact of RA. For performance evaluation, a channel reservation algorithm with customizable configurations is proposed. Furthermore, an analytical model is used to investigate the network performance for all key performance indicators (KPIs) under multiple channel failures and PU arrival rates and determine valuable tradeoffs among KPIs.

Reliability analysis of interdependent hypergraph network under different attack strategies

Nodes usually cooperate to form groups and survive or fail in real-world systems. Researchers typically consider the interdependence between node groups in studying the interdependent network. This paper studies the robustness of interdependent hypergraph networks under different attack strategies. According to the characteristics of the network model, we propose a series of target attack strategies and compare the destructive effect of these strategies on the network. Second, we analyze the impact of the random edge removal strategy on the robustness of hypergraph networks under different edge removal ratios. Finally, we propose four target-node edge removal strategies and compare their destructive effects on the network at the same edge removal ratios. Simulation results show that target attack and edge removal strategies can appreciably reduce the robustness of interdependent hypergraph networks and accelerate the networks’ collapse.

Reliability Analysis of MV Electric Distribution Networks Including Distributed Generation and ICT Infrastructure

In recent years, the increased distributed generation (DG) capacity in electric distribution systems has been observed. Therefore, it is necessary to research existing structures of distribution networks as well as to develop new (future) system structures. There are many works on the reliability of distribution systems with installed DG sources. This paper deals with a reliability analysis for both present and future medium voltage (MV) electric distribution system structures. The impact of DG technology used and energy source location on the power supply reliability has been analyzed. The reliability models of electrical power devices, conventional and renewable energy sources as well as information and communications technology (ICT) components have been proposed. Main contribution of this paper are the results of performed calculations, which have been analyzed for specific system structures (two typical present network structures and two future network structures), using detailed information on DG types, their locations and power capacities, as well as distribution system automation applied (automatic stand-by switching on—ASS and automatic power restoration—APR). The reliability of the smart grid consisting of the distribution network and the coupled communications network was simulated and assessed. The observations and conclusions based on calculation results have been made. More detailed modeling and consideration of system automation of distribution grids with DG units coupled with the communication systems allows the design and application of more reliable MV network structures.

Reliability analysis of complex networks based on irredundant subset cut group

This paper presents a unified framework to analyze the performance of a complex network containing diversified link capacities with specified flow requirement in terms of Capacity Related Reliability (CRR). An efficient minimal cutset based methodology is proposed to enumerate irredundant subset cut groups (SCGs) to evaluate flow networks reliability. The proposed approach is a two-step process. The first step requires a prior knowledge of all minimal cutsets of the network along with an enumeration of all irredundant subsets of cutset group (SCG) from a cut matrix to block or allow a prescribed amount of flow. In the second step, the enumerated SCGs can be fed as input to any multi-variable inversion sum-of-disjoint-product (MVI-SDP) based reliability evaluation algorithm to obtain the CRR of the network. The versatility of the proposed approach is that a single framework can be used to evaluate all the reliability measures such as 2-terminal, k-terminal and all-terminal. Various benchmark networks available in literature have been used to analyze the performance of the proposed methodology in terms of complexity and time constraint.

Exploring the vulnerability of transportation networks by entropy: A case study of Asia–Europe maritime transportation network

With the rise of global trade, maritime transportation networks have become an indispensable element of logistics networks. Approximately 80% of the global trade volume is transported by sea with the maritime logistics network fueling global economic integration. Due to the uncertainty of the transportation process and the impact of accidents, the reliability analysis of the logistics network is a topic of immense interest. In this paper, we propose an original and novel model to quantitatively analyze the vulnerability of the maritime logistics network by considering the importance of each port in the network. Three centrality measures that consider different topology information of the network are used in this paper to identify the importance of ports. Different information about the network is considered through the joint entropy and the multiscale factor q to evaluate the vulnerability of the logistics network. The Asia–Europe maritime transportation network serves as a real-world example to demonstrate the effectiveness and applicability of our proposed model. The experimental results suggest that the performance of the maritime network is closely related to the heterogeneity of the connectivity pattern and the process of decentralization can reduce the vulnerability of the maritime network.

Wireless Network Reliability Analysis for Arbitrary Network Topologies

In this paper, the outage performance of wireless networks with unstructured topologies is investigated. The network reliability perspective of graph theory is used to obtain the network outage polynomial of generalized wireless networks for both uncorrelated and correlated wireless channels. A relationship is established between the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max-flow min-cut</i> theorem and key communication performance indicators. The diversity order is equal to the size of the minimum cut-set between source and destination, and the coding gain is the number of cut-sets with size equal to the minimum cut. An ergodic capacity analysis of arbitrary network topologies based on the network outage polynomial is also presented. Numerical results are used to illustrate the technical definitions and verify the derivations.

Genetic Algorithm-based Reliability of Computer Communication Network

With the rapid development of network technology and computer technology, network applications can be seen everywhere. From life, finance to politics, the development of all walks of life has been inseparable from computer communication networks. Once there is a problem with the computer communication network, it will cause serious economic losses and may even cause national turmoil. Therefore, the reliability requirements of computer communication networks are getting higher and higher, and the reliability analysis and application of computer communication networks have become the focus of research. Based on genetic algorithm, this paper optimizes the reliability of computer communication networks. Firstly, the theory of reliability of computer communication network is briefly introduced, and the factors affecting reliability are simply analysed. Secondly, based on the influencing factors, the reliability optimization analysis model of computer communication network is established. Finally, the genetic algorithm is used to solve the established reliability optimization analysis model. The experimental results show that with the increase in the number of iterations of the genetic algorithm for data regression calculation, the algorithm reaches more than 0.99, and the optimal solution is obtained, which improves the reliability of the network, and the adaptability of the network link also increases. The genetic algorithm is applied to the reliability analysis of computer communication network, and the corresponding reliability optimization method is designed, which can improve the reliability of computer network while reducing the cost.

Data Transmission Reliability Analysis of Wireless Sensor Networks for Social Network Optimization

With the rapid development of the Internet in recent years, people are using the Internet less and less frequently. People publish and obtain information through various channels on the Internet, and online social networks have become one of the most important channels. Many nodes in social networks and frequent interactions between nodes create great difficulties for privacy protection, and some of the existing studies also have problems such as cumbersome computational steps and low efficiency. In this paper, we take the complex environment of social networks as the research background and focus on the key issues of mobile wireless sensor network reliability from the mobile wireless sensor networks that apply to large-scale, simpler information, and delay tolerance. By introducing intelligent learning methods and swarm intelligence bionic optimization algorithms, we address reliability issues such as mobile wireless sensor network fault prediction methods and topology reliability assessment methods in industrial application environments, the impact of mobile path optimization of mobile wireless sensor networks on data collection efficiency and network reliability, reliable data transmission based on data fusion methods, and intelligent fault tolerance strategies for multipath routing to ensure mobile wireless sensor networks operate energy-efficiently and reliably in complex industrial application environments.

Reliability Analysis of Social Network Data Transmission in Wireless Sensor Network Topology

In this paper, the reliability of data transmission in social networks is thoroughly studied and analyzed using wireless sensor network topology technology. This paper, based on the introduction of sensor network reliability analysis-related technology, combined with the characteristics, and needs of the sensor network itself, focuses on the study of the reliability analysis of the sensor network under the state of perturbation scheme. Based on the idea of making full use of data changes to respond to the sensor state, this paper takes the actual monitoring data of the wireless sensor network as the research object, selects the temporal correlation and spatial correlation of the measured environmental data as the reliability index by extracting the features of the wireless sensor network data, and proposes the Evidential reasoning rule- (ER-) based wireless sensor network data reliability assessment model based on Evidential reasoning rule (ER) is proposed. The data are mined, analyzed, and quantified from the perspective of content popularity, and the interest indicators of nodes on data under content popularity are analyzed to derive stable interest quantification values. Combined with the network properties, i.e., node autoassembly community, we analyze the data dissemination characteristics of social networks in wireless sensor network topology environment and derive the upper and lower bounds of data transmission capacity under node interest-driven and its variation on network performance. Social relationships among nodes affected by social attributes are considered; in turn, the data forwarding behavior of nodes is modeled using data transmission probability and data reception probability; finally, the data forwarding process is analyzed and a closed expression for the average end-to-end transmission capacity is derived in turn.

Theory and applications of an integrated model for capacitated-flow network reliability analysis

To evaluate the performance of engineering systems, this study acts as a bridge between the theories of classical and capacitated-flow network reliabilities and helps integrate them. Unlike existing works, in which a deterministic probability distribution was used for arcs in a network, in this study, the time-dependent reliability function is applied for components in arcs. In particular, the proposed model applies an arbitrary reliability function to evaluate reliability. In arc-level reliability analysis, the components in an arc are characterized by a reliability function, and such components comprise a capacitated-flow arc. Therefore, a certain number of components can be derived from the probability distribution of the capacity provided by an arc. In system-level reliability analysis, an algorithm is used to generate the minimal component vectors (MCV) for the given demand and time constraints. The system reliability can be calculated in terms of the MCVs using the derived capacity probability distribution. Based on the reliability analysis, a maintenance issue with two policies is discussed. Examples, including a large-scale case study of the Taiwan Academic Network, are discussed to validate the correctness, applicability, and scalability of the proposed model.

Reliability Analysis of Metro Door System Based on Fuzzy Multi-State Bayesian Network

Considering the shortcomings of the fault tree analysis (FTA) method in the reliability analysis of metro door systems, Bayesian network (BN) and fuzzy theory were introduced to establish the failure probability model of a metro door system. A fault tree of the metro door system was established based on the structure of the metro door, the operation data record and the practical experience of relevant engineers. The BN of the metro door system was constructed based on the fault tree. For the problem that the prior probabilities of root nodes with missing data were unavailable, fuzzy theory was introduced to convert the expert language values on these missing data nodes to corresponding prior probabilities, which were substituted into the BN along with the root nodes whose prior probabilities were obtained from the operation fault data to calculate the leaf node probability. Cause analysis of the metro door system was performed with bi-directional reasoning of BN, which provided a way to find the key factors that caused door faults and the metro door system fault probabilities.