Dynamic Reliability Block Diagrams Research Articles

Evaluating reliability of WSN with sleep/wake-up interfering nodes

A wireless sensor network (WSN) (singular and plural of acronyms are spelled the same) is a distributed system composed of autonomous sensor nodes wireless connected and randomly scattered into a geographical area to cooperatively monitor physical or environmental conditions. Adequate techniques and strategies are required to manage a WSN so that it works properly, observing specific quantities and metrics to evaluate the WSN operational conditions. Among them, one of the most important is the reliability. Considering a WSN as a system composed of sensor nodes the system reliability approach can be applied, thus expressing the WSN reliability in terms of its nodes' reliability. More specifically, since often standby power management policies are applied at node level and interferences among nodes may arise, a WSN can be considered as a dynamic system. In this article we therefore consider the WSN reliability evaluation problem from the dynamic system reliability perspective. Static–structural interactions are specified by the WSN topology. Sleep/wake-up standby policies and interferences due to wireless communications can be instead considered as dynamic aspects. Thus, in order to represent and to evaluate the WSN reliability, we use dynamic reliability block diagrams and Petri nets. The proposed technique allows to overcome the limits of Markov models when considering non-linear discharge processes, since they cannot adequately represent the aging processes. In order to demonstrate the effectiveness of the technique, we investigate some specific WSN network topologies, providing guidelines for their representation and evaluation.

RELIABILITY EVALUATION OF WSN WITH DYNAMIC-DEPENDENT NODES

A wireless sensor network (WSN) is a distributed system composed of autonomous sensor nodes wireless connected and randomly scattered into a geographical area to cooperatively monitor physical or environmental conditions. Adequate techniques and strategies are required to manage a WSN in order it works properly, mainly focusing on its reliability. From the system reliability perspective, it is important to take into account that WSN nodes are usually battery-powered and the WSN reliability strongly depends on the power management at node level. Since standby power management policies are often applied at node level and, moreover, interferences among nodes may arise, a WSN can be considered as a system affected by dynamic-dependent behaviors among its components and therefore, the dynamic reliability approach can be applied. Static-structural interactions are specified by the WSN topology. Active–sleep standby policies and interferences due to wireless communications can be instead considered as dynamic aspects. Thus, in order to represent and to evaluate the WSN reliability dynamic reliability block diagrams are used in this paper. The proposed technique allows to overcome the limits of Markov models when considering nonlinear discharge processes, since such models cannot adequately represent the node aging process. In order to demonstrate the effectiveness of the DRBD technique in this context, we investigate some specific WSN network topologies, providing guidelines for their representation and evaluation.

Reliability Assessment of Control Systems

Actually modern systems have to ensure higher and higher operating standards, thus including monitoring and control subsystems for their achievement. In safety critical systems control is a crucial task in order to satisfy strict reliability requirements. But it is also necessary that the control system is itself reliable. As a consequence, adequate techniques are necessary in order to perform reliability evaluation of both the controlled and the control systems. Techniques that therefore should avoid over-simplistic assumptions and/or approximations that, for example, are usually introduced when dependencies, interferences and other dynamic reliability aspects are not taken into the right consideration. In this paper, a technique for carefully evaluating the reliability of such systems, also considering dynamic aspects and behaviors, is proposed. Firstly the technique is detailed through the specification of the dynamic reliability block diagrams notation, and therefore in order to demonstrate its effectiveness, it is applied to an example of a computing-based control system taken from literature, thus providing guidelines for the reliability representation and evaluation through DRBD.

Automated Modeling of Dynamic Reliability Block Diagrams Using Colored Petri Nets

Computer system reliability is conventionally modeled and analyzed using techniques such as fault tree analysis and reliability block diagrams (RBDs), which provide static representations of system reliability properties. A recent extension to RBDs, called dynamic RBDs (DRBD), defines a framework for modeling the dynamic reliability behavior of computer-based systems. However, analyzing a DRBD model in order to locate and identify design errors, such as a deadlock error or faulty state, is not trivial when done manually. A feasible approach to verifying it is to develop its formal model and then analyze it using programmatic methods. In this paper, we first define a reliability markup language that can be used to formally describe DRBD models. Then, we present an algorithm that automatically converts a DRBD model into a colored Petri net. We use a case study to illustrate the effectiveness of our approach and demonstrate how system properties of a DRBD model can be verified using an existing Petri net tool. Our formal modeling approach is compositional; thus, it provides a potential solution to automated verification of DRBD models.

How to capture dynamic behaviours of dependable systems

In terms of reliability, a unit, subsystem or system is considered dynamic if its failure probability is variable. From the system point of view, the reliability depends on the units' dynamics, on the inter-dependencies arising among such units (load-sharing, standby redundancy, interferences, etc.) and on their reliability relationships, that can also be variable (phased-mission systems). Such peculiarities have great impact on the choice of the technique to be used to evaluate the reliability of a system. Combinatorial techniques can be adopted in case the system's units are stochastically independent. Otherwise, it is requested to recur to lower level techniques and formalisms, such as: state space methods, hybrid (combinatorial/state space) techniques or simulation. This paper analyses the reliability of fault tolerant/dependent/dynamic systems. The approach exploited is based on the concept of dependencies and their composition. In the paper, we deeply investigate such concepts from a high level of abstraction. Moreover, basing on the use of dynamic reliability block diagrams, a notation we developed by extending the well known reliability block diagrams, we detail how this modelling approach captures dynamic reliability behaviours. In order to demonstrate the effectiveness of such technique, we mainly focus the discussion on fault tolerant-dynamic-dependent computing systems, investigating some dynamic reliability/availability behaviours and providing the guidelines for their representation and evaluation. The discussion is supported by the results obtained evaluating a complex fault tolerant computing system with several units affected by common cause events, shared workloads and other dynamic-dependable behaviours.

Reliability and availability analysis of dependent–dynamic systems with DRBDs

Reliability/availability evaluation is an important, often indispensable, step in designing and analyzing (critical) systems, whose importance is constantly growing. When the complexity of a system is high, dynamic effects can arise or become significant. The system might be affected by dependent, cascade, on-demand and/or common cause failures, its units could interfere (load sharing, inter/sequence-dependency), and so on. It is also of great interest to evaluate redundancy and maintenance policies but, since dynamic behaviors usually do not satisfy the stochastic independence assumption, notations such as reliability block diagrams (RBDs), fault trees (FTs) or reliability graphs (RGs) become approximated/simplified techniques, unable to capture dynamic–dependent behaviors. To overcome such problem we developed a new formalism derived from RBDs: the dynamic RBDs (DRBDs). In this paper we explain how the DRBDs notation is able to adequately model and therefore analyze dynamic–dependent behaviors and complex systems. Particular emphasis is given to the modeling and the analysis phases, from both the theoretical and the practical point of views. Several case studies of dynamic–dependent systems, selected from literature and related to different application fields, are proposed. In this way we also compare the DRBDs approach with other methodologies, demonstrating its effectiveness.

Drbd: Dynamic Reliability Block Diagrams for System Reliability Modelling

With the rapid advances of computer-based technology in mission-critical domains such as aerospace, military, and power industries, critical systems exhibit more complex, dependent, and dynamic behaviours. Such dynamic system behaviours cannot be fully captured by existing reliability modelling tools. In this paper, we introduce a new reliability modelling tool, called dynamic reliability block diagrams (DRBD), to model dynamic relationships between system components. Due to the complexity of DRBD models that involve dynamic conceptual modelling constructs, such as a state dependency (SDEP) block, design errors, which are subtle and difficult to detect, can be easily introduced during the modelling process. To formally verify and validate the correctness of a DRBD model, we propose a Petri net based approach by converting DRBD constructs into coloured Petri nets (CPN). We use a case study to illustrate how to convert a DRBD model into CPN, and how to use an existing Petri net tool to analyse and verify dynamic system behavioural properties. Our case study and experimental results show that DRBD models are a powerful tool for system reliability modelling, and our proposed verification approach can effectively ensure the correct design of DRBD models for complex and large-scale computer-based systems.

Dependability Evaluation with Dynamic Reliability Block Diagrams and Dynamic Fault Trees

Dependability evaluation is an important often-mandatory step in designing and analyzing (critical) systems. Introducing control and/or computing devices to automate processes increases the system complexity, with an impact on the overall dependability. This occurs as a consequence of interferences, dependencies, and other similar effects that cannot be adequately managed through formalisms such as reliability block diagrams (RBDs), fault trees (FTs), and reliability graphs (RGs), since the statistical independence assumption is not satisfied. In addition, more enhanced notations such as dynamic FTs (DFTs) might not be adequate to represent all the behavioral aspects of dynamic systems. To overcome these problems, we developed a new formalism derived from RBD: the dynamic RBD (DRBD). DRBD exploits the concept of dependence as the building block to represent dynamic behaviors, allowing us to compose the dependencies and adequately manage the arising conflicts by means of a priority algorithm. In this paper, we explain how we can use the DRBD notation by specifying a practical methodology. Starting from the system knowledge, the proposed methodology drives to the overall system reliability evaluation through the entire phases of modeling and analysis. Such a technique is applied to an example taken from the literature, consisting of a distributed computing system.

REVIEW OF VARIOUS DYNAMIC MODELING METHODS AND DEVELOPMENT OF AN INTUITIVE MODELING METHOD FOR DYNAMIC SYSTEMS

Conventional static reliability analysis methods are inadequate for modeling dynamic interactions between components of a system. Various techniques such as dynamic fault tree, dynamic Bayesian networks, and dynamic reliability block diagrams have been proposed for modeling dynamic systems based on improvement of the conventional modeling methods. In this paper, we review these methods briefly and introduce dynamic nodes to the existing reliability graph with general gates (RGGG) as an intuitive modeling method to model dynamic systems. For a quantitative analysis, we use a discrete-time method to convert an RGGG to an equivalent Bayesian network and develop a software tool for generation of probability tables.