ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

Recent developments and technological advancements in wireless communication, MicroElectroMechanical Systems (MEMS) technology and integrated circuits has enabled low-power, intelligent, miniaturized, invasive/non-invasive micro and nano-technology sensor nodes strategically placed in or around the human body to be used in various applications, such as personal health monitoring. This exciting new area of research is called Wireless Body Area Networks (WBANs) and leverages the emerging IEEE 802.15.6 and IEEE 802.15.4j standards, specifically standardized for medical WBANs. The aim of WBANs is to simplify and improve speed, accuracy, and reliability of communication of sensors/actuators within, on, and in the immediate proximity of a human body. The vast scope of challenges associated with WBANs has led to numerous publications. In this paper, we survey the current state-of-art of WBANs based on the latest standards and publications. Open issues and challenges within each area are also explored as a source of inspiration towards future developments in WBANs.

Wireless Body Area Networks: A Survey

This paper presents a polynomial dimensional decomposition (PDD) method for global sensitivity analysis of stochastic systems subject to independent random input following arbitrary probability distributions. The method involves Fourier-polynomial expansions of lower-variate component functions of a stochastic response by measure-consistent orthonormal polynomial bases, analytical formulae for calculating the global sensitivity indices in terms of the expansion coefficients, and dimension-reduction integration for estimating the expansion coefficients. Due to identical dimensional structures of PDD and analysis-of-variance decomposition, the proposed method facilitates simple and direct calculation of the global sensitivity indices. Numerical results of the global sensitivity indices computed for smooth systems reveal significantly higher convergence rates of the PDD approximation than those from existing methods, including polynomial chaos expansion, random balance design, state-dependent parameter, improved Sobol’s method, and sampling-based methods. However, for non-smooth functions, the convergence properties of the PDD solution deteriorate to a great extent, warranting further improvements. The computational complexity of the PDD method is polynomial, as opposed to exponential, thereby alleviating the curse of dimensionality to some extent. Mathematical modeling of complex systems often requires sensitivity analysis to determine how an output variable of interest is influenced by individual or subsets of input variables. A traditional local sensitivity analysis entails gradients or derivatives, often invoked in design optimization, describing changes in the model response due to the local variation of input. Depending on the model output, obtaining gradients or derivatives, if they exist, can be simple or difficult. In contrast, a global sensitivity analysis (GSA), increasingly becoming mainstream, characterizes how the global variation of input, due to its uncertainty, impacts the overall uncertain behavior of the model. In other words, GSA constitutes the study of how the output uncertainty from a mathematical model is divvied up, qualitatively or quantitatively, to distinct sources of input variation in the model [1].

Reliability Engineering and System Safety

The Grid 2: Blueprint for a New Computing Infrastructure

Mathematica--A System for Doing Mathematics by Computer.

This paper proposes a generalized phased-mission system (GPMS) analysis methodology called GPMS-CPR that has high computation efficiency and is easy to implement. GPMS-CPR can evaluate a wider range of more practical systems with less restrictive mission requirements, while offering more human-friendly performance indices such as multilevel grading as compared to the previous PMS approaches. GPMS-CPR also accounts for imperfect coverage. This method implements an exciting synthesis of several approaches into a single methodology. Further, it extends the methodology to allow for the analysis of sensitivity of each component for each phase as well as for the entire phased mission, with respect to the multilevel reliability for the GPMS. The conventional phase-OR PMS appear as a special case this GPMS. The advantages of this approach are in the low computational complexity, broad applicability, easy implementation, and high integration (reliability, performance, and sensitivity for GPMS). The methodology is illustrated with examples.

Analysis of generalized phased-mission system reliability, performance, and sensitivity

Multistate systems can model many practical systems in a wide range of real applications. A distinct characteristic of these systems is that the systems and their components may assume more than two levels of performance (or states), varying from perfect operation to complete failure. The nonbinary property of multistate systems and their components makes the analysis of multistate systems difficult. This paper proposes a new decision-diagram-based method, called multistate multivalued decision diagrams (MMDD), for the analysis of multistate systems with multistate components. Examples show how the MMDD models are generated and evaluated to obtain the system-state probabilities. The MMDD method is compared with the existing binary decision diagram (BDD)-based method. Empirical results show that the MMDD method can offer less computational complexity and simpler model evaluation algorithm than the BDD-based method.

A New Decision-Diagram-Based Method for Efficient Analysis on Multistate Systems

This paper proposes efficient methods to assess the reliability of phased-mission systems (PMS) considering both imperfect fault coverage (IPC), and common-cause failures (CCF). The IPC introduces multimode failures that must be considered in the accurate reliability analysis of PMS. Another difficulty in analysis is to allow for multiple CCF that can affect different subsets of system components, and which can occur s-dependently. Our methodology for resolving the above difficulties is to separate the consideration of both IPC and CCF from the combinatorics of the binary decision diagram-based solution, and adjust the input and output of the program to generate the reliability of PMS with IPC and CCF. According to the separation order, two equivalent approaches are developed. The applications and advantages of the approaches are illustrated through examples. PMS without IPC and/or CCF appear as special cases of the approaches

Reliability Evaluation of Phased-Mission Systems With Imperfect Fault Coverage and Common-Cause Failures

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.

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Automated Modeling of Dynamic Reliability Block Diagrams Using Colored Petri Nets

Reliability and sensitivity analysis is a key component in the design, tuning, and maintenance of network systems. Tremendous research efforts have been expended in this area, but two practical issues, namely, imperfect coverage (IPC) and common-cause failures (CCF), have generally been missed or have not been fully considered in existing methods. In this paper, an efficient approach for fully incorporating both IPC and CCF into network reliability and sensitivity analysis is proposed. The challenges are to allow multiple failure modes introduced by IPC and to cope with multiple dependent faults caused by CCF simultaneously in the analysis. Our methodology for addressing the aforementioned challenges is to separate the consideration of both IPC and CCF from the combinatorics of the solution, which is based on reduced ordered binary decision diagrams (ROBDD). Due to the nature of the ROBDD and the separation of IPC and CCF from the solution combinatorics, our approach has a low computational complexity and is easy to implement. A sample network system is analyzed to illustrate the basics and advantages of our approach. A software tool that we developed for fault-tolerant network reliability and sensitivity analysis is also presented.

Liudong Xing

Papers

Analysis of generalized phased-mission system reliability, performance, and sensitivity

A New Decision-Diagram-Based Method for Efficient Analysis on Multistate Systems

Reliability Evaluation of Phased-Mission Systems With Imperfect Fault Coverage and Common-Cause Failures

Automated Modeling of Dynamic Reliability Block Diagrams Using Colored Petri Nets

An Efficient Binary-Decision-Diagram-Based Approach for Network Reliability and Sensitivity Analysis