Network Reliability Measures Research Articles

Holistic Resilience and Reliability Measures for Cellular Telecommunication Networks

Nowadays, our lives have become increasingly inseparable from the services facilitated by telecommunication networks. Disruption of this service may result in a significant detrimental effect, especially in smart manufacturing, remote surgery, and harbor logistic. With the development and deployment of the new generation of telecommunication, i.e., 5G mobile networks, reliability has been repeatedly emphasized as a critical issue. Contemporary telecommunication networks often have a large scale. Evaluating network reliability is a challenging issue related to the spatiotemporal characteristics of the network and of the connection principles. In this paper, we bring together concepts from computational geometry and reliability engineering to forge a new approach to address the challenging issue. We first formulate a Voronoi-based Graphic network (VGN) model with a novel built-in dependence, coverage dependence, that manifests the resilience of the network. Based on the VGN model, we propose two new measures for holistically assessing the resilience and reliability of telecommunication networks in a large region. Ultimately, we employ the designed approach to telecommunication networks. The result indicates that coverage dependence and resilience are important factors for delivering ultra-reliable service to users promised by the 5G.

Relationship Between Component Connectivity And Component Diagnosability Of Some Regular Networks

Abstract As a kind of conditional connectivity, component connectivity is an improvement of traditional connectivity, which is conducive to enhance the reliability of the network. To be specific, the $r$-component connectivity of a network $G$, written as $c\kappa _{r}(G)$, is defined as the minimum number of all node cuts whose removal causes the remaining network to have at least $r$ components. Component diagnosability, as another measure of network reliability, is usually related to the number of components in the remaining network. The $r$-component diagnosability, written as $ct_{r}(G)$, is defined as the maximum number of faulty sets such that at least $r$ components in the surviving network and all faulty nodes can be diagnosed. This paper mainly explores the relationship between component connectivity and component diagnosability of some regular networks. Once knowing the component connectivity of such a network, with the help of this relationship, we can easily obtain the component diagnosability of the network. Furthermore, we apply this relationship to some famous regular networks to obtain their component diagnosabilities under the PMC model.

Genetic Algorithm Energy Optimization in 3D WSNs with Different Node Distributions

Optimal node clustering in wireless sensor networks (WSNs) is a major issue in reducing energy consumption and extending network node life time and reliability measures. Many techniques for optimizing the node clustering process in WSN have been proposed in the literature. The metaheuristic algorithms are a subset of these techniques. Genetic algorithm (GA) is an evolutionary metaheuristic technique utilized to improve the network reliability and extending the network life time by optimizing the clustering process in the network. The GA dynamic clustering (GA-DC) algorithm is proposed in this paper to extend the network reliability and node life time of three dimensional (3D) WSN. The GA-DC algorithm made use of an improved fitness function that takes into account a variety of metrics such as energy expenditure per protocol round, clustering distance, and the number of long-distance wireless connections. There have been two types of simulation scenarios run. First, simulation results show that the GA-DC algorithm increases network life time by 80% and network throughput by 55% when compared to the well-known LEACH protocol. Second, simulation results show that the uniform node distribution outperforms the normal and exponential distributions in terms of network life time by 5.7% and 7%, network reliability by 4.2% and 76%, and data throughput by 10.85% and 19.54%, respectively

Geodetic Network Geometry versus Reliability Measures in Examples of the Trilateration Network

AbstractThe article describes selected case studies of the trilateration network design. The analyzed case studies may be of interest to the geodetic community but may also be potentially interesti...

Disjoint Spanning Tree Based Reliability Evaluation of Wireless Sensor Network.

Wireless sensor networks (WSNs) are becoming very common in numerous manufacturing industries; especially where it is difficult to connect a sensor to a sink. This is an evolving issue for researchers attempting to contribute to the proliferation of WSNs. Monitoring a WSN depends on the type of collective data the sensor nodes have acquired. It is necessary to quantify the performance of these networks with the help of network reliability measures to ensure the stable operation of WSNs. Reliability plays a key role in the efficacy of any large-scale application of WSNs. The communication reliability in a wireless sensor network is an influential parameter for enhancing network performance for secure, desirable, and successful communication. The reliability of WSNs must incorporate the design variables, coverage, lifetime, and connectivity into consideration; however, connectivity is the most important factor, especially in a harsh environment on a large scale. The proposed algorithm is a one-step approach, which starts with the recognition of a specific spanning tree only. It utilizes all other disjoint spanning trees, which are generated directly in a simple manner and consume less computation time and memory. A binary decision illustration is presented for the enumeration of K-coverage communication reliability. In this paper, the issue of computing minimum spanning trees was addressed and it is a pertinent method for further evaluating reliability for WSNs. This paper inspects the reliability of WSNs and proposes a method for evaluating the flow-oriented reliability of WSNs. Further, a modified approach for the sum-of-disjoint products to determine the reliability of WSN from the enumerated minimal spanning trees is proposed. The proposed algorithm when implemented for different sizes of WSNs demonstrates its applicability to WSNs of various scales. The proposed methodology is less complex and more efficient in terms of reliability.

Minimal Path-Based Reliability Model for Wireless Sensor Networks With Multistate Nodes

Wireless sensor networks (WSNs) find application in various fields like environmental monitoring, health-care, land security, and many more. To ease our day-to-day activity, WSNs have become an integral tool for complex data gathering tasks. Monitoring a phenomenon by a WSN depends on the collective data provided by the sensor nodes. To ensure reliable operation of WSNs, it is important to quantify the performance of such networks in terms of network reliability measures. This article studies the reliability of WSNs with multistate nodes and proposes an approach to evaluate the flow-oriented network reliability of WSNs consisting of multistate sensor nodes. The proposed method takes into account the dynamic state of the network due to multistate sensor nodes. The proposed approach includes enumeration of shortest minimal paths from application-specific flow satisfying sensor nodes (source nodes) to the sink node. It then proposes a modified sum-of-disjoint products approach to evaluate WSN reliability in the presence of multistate nodes from the enumerated shortest minimal paths. Simulations are performed on WSNs of various sizes to show the applicability of the proposed approach on arbitrary WSNs.

K-Terminal Network Permutation Importance Measure Based on Mixture C-Spectrum

The construction spectrum(C-spectrum) is often used to exploit the network reliability and importance measure. It depends only on the network structure and hence called structure invariant. Importance measure can be used to quantify the criticality of edge within a network. This paper aim at generalizing the traditional permutation importance measure to accommodate the case of K-terminal network in which all the edges fail with independent and equal probability. A concept for mixture C-spectrum is introduced to evaluate the permutation importance measure of edges. It is proved that the rankings according to the permutation importance measure depend only on the network structure through the mixture C-spectrum when the network has special structure or the reliability of edge is sufficient large. Finally, numerical experiment show that the Monte Carlo algorithm based on the mixture C-spectrum can be efficiently used to evaluate the permutation importance measure.

Multi-level cross-layer protocol for end-to-end delay provisioning in wireless multimedia sensor networks

Rapid developments in information and communication technology in recent years have posed a significant challenge in wireless multimedia sensor networks (WMSNs). End-to-end delay and reliability are the critical issues in multimedia applications of sensor networks. In this paper we provide a new cross-layer approach for provisioning the end-to-end delay of the network at a desirable level of the packet delivery ratio (PDR), used here as a measure of network reliability. In the proposed multi-level cross-layer (MLCL) protocol, the number of hops away from the sink is used to set a level for each node. A packet is routed through the path with the minimum hop count to the sink using this level setting. The proposed protocol uses cross-layer properties between the network and medium access control (MAC) layers to estimate the minimum delay, with which a node can deliver a packet to the sink. When a node wants to send a packet, the MLCL protocol compares this minimum delay with the time to live (TTL) of a packet. If the TTL of the packet is higher than the minimum delay, the node sends the packet through the path with the minimum delay; otherwise, the node drops the packet as the node cannot deliver it to the sink within the TTL duration. This packet dropping improves network performance because the node can send a useful packet instead of an unusable packet. The results show a superior performance in terms of end-to-end delay and reliability for the proposed protocol compared to state-of-the-art protocols.

Distribution Power System Reliability Assessment Considering Cold Load Pickup Events

Power system reliability measures for residential distribution networks have gained a lot of attention in recent years. This is due to the introduction of smart grids, where the quality of the delivered power becomes a crucial issue. In this paper, a new methodology to include cold load pickup (CLPU) events in the reliability assessment of power distribution systems is proposed. The developed methodology presents a platform to include CLPU events in the reliability assessment assuming an optimal systematic procedure is used to restore the affected load points. The methodology is applied on a test system using a state-of-the-art search technique named lightning search algorithm (LSA). The optimal plan takes into account electrical code protection system settings, system operation limits, and stochastic behavior of the load. The reliability indices for a base case without considering CLPU is compared with the case that considers CLPU assuming an optimal restoration. Both cases are compared further under fixed-restoration plans with different time intervals. The results demonstrate the effectiveness of the proposed methodology to improve the distribution network reliability by considering the CLPU events.

The k-diameter component edge connectivity parameter

We focus on a network reliability measure based on edge failures and considering a network operational if there exists a component with diameter k or larger. The k-diameter component edge connectivity parameter of a graph is the minimum number of edge failures needed so that no component has diameter k or larger. This implies each resulting vertex must not have a k-neighbor. We give results for specific graph classes including path graphs, complete graphs, complete bipartite graphs, and a surprising result for perfect r-ary trees.

Analytic tools for investigating the structure of network reliability measures with regard to observation correlations

A global measure of observation correlations in a network is proposed, together with the auxiliary indices related to non-diagonal elements of the correlation matrix. Based on the above global measure, a specific representation of the correlation matrix is presented, being the result of rigorously proven theorem formulated within the present research. According to the theorem, each positive definite correlation matrix can be expressed by a scale factor and a so-called internal weight matrix. Such a representation made it possible to investigate the structure of the basic reliability measures with regard to observation correlations. Numerical examples carried out for two test networks illustrate the structure of those measures that proved to be dependent on global correlation index. Also, the levels of global correlation are proposed. It is shown that one can readily find an approximate value of the global correlation index, and hence the correlation level, for the expected values of auxiliary indices being the only knowledge about a correlation matrix of interest. The paper is an extended continuation of the previous study of authors that was confined to the elementary case termed uniform correlation. The extension covers arbitrary correlation matrices and a structure of correlation effect.

The system of technical diagnostics of the industrial safety information network

This research is devoted to problems of safety of the industrial information network. Basic sub-networks, ensuring reliable operation of the elements of the industrial Automatic Process Control System, were identified. The core tasks of technical diagnostics of industrial information safety were presented. The structure of the technical diagnostics system of the information safety was proposed. It includes two parts: a generator of cyber-attacks and the virtual model of the enterprise information network. The virtual model was obtained by scanning a real enterprise network. A new classification of cyber-attacks was proposed. This classification enables one to design an efficient generator of cyber-attacks sets for testing the virtual modes of the industrial information network. The numerical method of the Monte Carlo (with LPτ - sequences of Sobol), and Markov chain was considered as the design method for the cyber-attacks generation algorithm. The proposed system also includes a diagnostic analyzer, performing expert functions. As an integrative quantitative indicator of the network reliability the stability factor (Kstab) was selected. This factor is determined by the weight of sets of cyber-attacks, identifying the vulnerability of the network. The weight depends on the frequency and complexity of cyber-attacks, the degree of damage, complexity of remediation. The proposed Kstab is an effective integral quantitative measure of the information network reliability.

Travel Time Reliability for Urban Networks: Modelling and Empirics

The importance of travel time reliability in traffic management, control, and network design has received a lot of attention in the past decade. In this paper, a network travel time distribution model based on the Johnson curve system is proposed. The model is applied to field travel time data collected by Automated Number Plate Recognition (ANPR) cameras. We further investigate the network-level travel time reliability by connecting the network reliability measures such as the weighted standard deviation of travel time rate and the weighted skewness of travel time rate distributions with network traffic characteristics (e.g., the network density). The weighting is done with respect to the number of signalized intersections on a trip. A clear linear relation between the weighted average travel time rate and the weighted standard deviation of travel time rate can be observed for different time periods with time-varying demand. Furthermore, both the weighted average travel time rate and the weighted standard deviation of travel time rate increase monotonically with network density. The empirical findings of the relation between network travel time reliability and network traffic characteristics can be possibly applied to assess traffic management and control measures to improve network travel time reliability.

Reliability Evaluation of Network Systems with Dependent Propagated Failures Using Decision Diagrams

In a network system, a propagated failure (PF) is a failure originating from a network component that can cause extensive damages to other network components or even the failure of the entire system. Existing works on PFs have mostly assumed the deterministic effect from a component PF, i.e., a fixed subset of system components is affected whenever the PF occurs. However, in many real-world systems, the components may have different levels of protection, and the effect of damage from a component PF can be dependent upon the status of other components within the same system or the occurrence order of component failures. This paper proposes a new analytical method based on multi-valued decision diagrams (MDDs) for the reliability analysis of network systems with dependent propagation effects. Particularly, new MDD modeling procedures are proposed for considering different types of dependent PF effects introduced by different protection levels. After the system MDD is generated using a new MDD combination algorithm to efficiently handle the dependent PF effects, methods for computing the network reliability and component importance measures are presented. The detailed analysis of an example network system subjected to dependent PFs is presented to illustrate the basics and application of the proposed method. It is shown that the proposed MDD-based method generates smaller model size and thus presents lower computational complexity in the model generation and evaluation than the existing Markov method and separable method.

On the probability of facing random breakdowns: a measure of networks’ vulnerability

In this paper, we study the vulnerability of networks in the case of random removal of individual nodes. To achieve this goal, a network reliability measure, the probability of messages facing malfunctions, has been proposed. The benefit of this measure is its capability of applying in a wide range of graphs. Moreover, it is well suited to human activities and foraging patterns of some sorts of animals, as they try to make an alternative path in the case of facing failures. We examine the most prominent communication networks such as meshes and tori; further, CAN, Chord, Petersen, PRU, and Hypergrid, which are well suited for P2P networks, have been investigated. In order to gain a better understanding of the studied networks’ vulnerability and easier evaluation of the proposed measure, an ant colony-based communication protocol has been presented. Experimental results verify the correctness of the proposed measure.

Water distribution network robust design based on energy surplus index maximization

The aim of this paper is to show that energy surplus indices, such as resilience index, besides providing a very good indirect measure of water distribution network reliability, also represent a valuable and effective indicator of network robustness under alternative network scenarios. It can thus be profitably used for network design under conditions of uncertain future demands. The methodology adopted consisted of: (a) multi-objective design optimization performed in order to minimize construction costs while maximizing the resilience index; and (b) retrospective performance assessment of the alternative solutions of the Pareto front obtained, under demand conditions far from those assumed during the design phase. Two case studies of different topological complexity were considered. Results showed that the resilience index, which is one of the most effective indirect indices of reliability, represents a very good measure of robustness as well.

Enumeration of Minimal Cutsets for Directed Networks with Comparative Reliability Study for Paths or Cuts

An active research field is the evaluation of the reliability of a complex network. The most popular methods for such evaluation often use Minimal Paths (MP) or Minimal Cuts (MC) of the network. This paper proposes an algorithmic approach to enumerate MC of the directed network's reliability measures. Another attempt has been made in this paper to provide an answer to the question as to when MP or MC are suitable for evaluating reliability measures. An exhaustive study has been conducted to provide some guidelines in this respect. Copyright © 2015 John Wiley & Sons, Ltd.

The combined use of resilience and loop diameter uniformity as a good indirect measure of network reliability

The aim of this paper is to show that the combined use of the resilience index (Todini, 2000) with a loop based diameter uniformity index (here formulated) yields a good indirect reliability measure, which can be conveniently used within the optimization processes of the water distribution system design. The methodology adopted to show the advantages of the combined use of the two indexes consists of (a) a three-objective optimization performed in order to simultaneously minimize costs (first objective function) and maximize both the resilience and the loop diameter uniformity indexes (second and third objective functions respectively); (b) a retrospective assessment of performance indicators relative to critical operational scenarios on the solutions of the Pareto surface obtained at the end of the optimization process. Applications were performed considering a simple case study, which made it possible to easily compare the new approach, based on a three-objective optimization, with the two-objective optimization process based on the use of the resilience index alone and also with the two-objective optimization process based on the modified resilience index formulated by Prasad, Sung-Hoon, and Namsik (2003) (where the diameter uniformity is defined at nodal level and inserted as a weight in Todini's resilience index), being both indexes a surrogate to reliability. The comparison pointed out that using resilience and loop diameter uniformity as two separate objective functions in an optimization process leads to solutions which perform better during critical operational scenarios (particularly when dealing with segment isolation) than the equally expensive solutions obtained adopting the resilience index (independently of its formulation) alone as reliability related objective function. Since the proposed approach suggests that a three-objective optimization be utilized to perform an appropriate pipe-network optimal design, an improvement in the well-known NSGA-II algorithm (Deb, Pratap, Agarwal, & Meyarivan, 2002) is proposed as its original formulation proved to have some difficulties dealing with more than two objectives.

Approach for improving reliability in optimal network design

This paper present an optimal artificial neural network approach for improving upper bound on link reliability in optimal network design. Improving reliability in a growing variable-sized network is an important parameter for optimal network design. Many alternative methods for improving reliability have been used for optimal network design. Most of these methods, mentioned in the literature are simulation-based. These methods provide simple ways for measuring reliability when networks have limited size. These methods require significant computational effort and time for growing variable-sized networks. An optimal neural network method is therefore proposed for reliability improvement in optimal network design. The proposed algorithm has two phases: experimental setup and optimal phase. Experimental setup phase scans all possible network topologies for reliability measures. And, optimal phase constructs optimal network design with improved reliability upper-bound. Both neural networks were studied with fixed and varying links. Results are grouped using cross-validation method showing that the optimised artificial neural network approach gives precise measures for significant reliability improvement than the upper-bound than heuristic-based approach. Results show that the optimised ANN produces optimal network designs and reliability measures at reasonable computational cost.

A simulation method for network performability estimation using heuristically computed pathsets and cutsets

Consider a set of terminal nodes K that belong to a network whose nodes are connected by links that fail independently with known probabilities. We introduce a method for estimating a performability measure that depends on the hop distance between terminal nodes. The new measure generalises the diameter-constrained network reliability measure. We propose a Monte Carlo method with significant variance reduction compared to crude Monte Carlo. It is based on using edge sets named d-pathsets and d-cutsets for reducing the variance of the estimator. These edge sets, considered as a priori known in previous literature, heavily affect the attained performance; we hereby introduce and compare a family of heuristics for their selection. Numerical examples are presented, showing the significant efficiency improvements that can be obtained by chaining the edge set selection heuristics to the proposed Monte Carlo sampling plan.