Advances in Probabilistic Model Checking

Katoen, Joost-Pieter

doi:10.1007/978-3-642-11319-2_5

Cited by 10 publications

(23 citation statements)

References 55 publications

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“…[10]. PRISM supports a range of probabilistic models and property specification languages based on temporal logic, and have been extended with costs and rewards [11].…”

Section: Probabilistic Model Checkingmentioning

confidence: 99%

“…The system to be analyzed is described in a high-level PRISM specification language then transformed into an internal representation, such as symbolic [12] or explicit [11]. Symbolic uses Multi Terminal Binary Decision Diagram (MTBDD) [13] mathematical data structure for compact representation of state space.…”

Section: Probabilistic Model Checkingmentioning

confidence: 99%

“…Whereas explicit enumerate complete state space in memory. Properties are expressed as temporal logical formulas, enriched with probabilistic operators, and may include additional features such as time bounds or rewards depending on the model [11].…”

Section: Probabilistic Model Checkingmentioning

confidence: 99%

“…The probabilistic model checker exhaustively search entire reachable state space associated with the system model, and generate two types of outputs, either true/false which indicate whether the specification holds in model or not, or the numerical value, for example, the probability or expected time to reach a state satisfying the specification [11].…”

Section: Probabilistic Model Checkingmentioning

confidence: 99%

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A Quantitative Analysis of Collision Resolution Protocol for Wireless Sensor Network

Patel¹,

Patel²

2015

JSEA

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In this paper, we present formal analysis of 2CS-WSN collision resolution protocol for wireless sensor networks using probabilistic model checking. The 2CS-WSN protocol is designed to be used during the contention phase of IEEE 802.15.4. In previous work on 2CS-WSN analysis, authors formalized protocol description at abstract level by defining counters to represent number of nodes in specific local state. On abstract model, the properties specifying individual node behavior cannot be analyzed. We formalize collision resolution protocol as a Markov Decision Process to express each node behavior and perform quantitative analysis using probabilistic model checker PRISM. The identical nodes induce symmetry in the reachable state space which leads to redundant search over equivalent areas of the state space during model checking. We use "ExplicitPRISMSymm" on-the-fly symmetry reduction approach to prevent the state space explosion and thus accommodate large number of nodes for analysis.

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“…[10]. PRISM supports a range of probabilistic models and property specification languages based on temporal logic, and have been extended with costs and rewards [11].…”

Section: Probabilistic Model Checkingmentioning

confidence: 99%

Section: Probabilistic Model Checkingmentioning

confidence: 99%

Section: Probabilistic Model Checkingmentioning

confidence: 99%

Section: Probabilistic Model Checkingmentioning

confidence: 99%

See 2 more Smart Citations

A Quantitative Analysis of Collision Resolution Protocol for Wireless Sensor Network

Patel¹,

Patel²

2015

JSEA

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“…timed [15] or probabilistic systems [11]) or quantitative model checking (see e.g. [10]). As far as we know, the focus has been on time and probabilistic/stochastic aspects in the tradition of Markovian models.…”

Section: Related Workmentioning

confidence: 99%

Exploiting the Hierarchical Structure of Rule-Based Specifications for Decision Planning

Boronat¹,

Bruni²,

Lafuente³

et al. 2010

Formal Techniques for Distributed Systems

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Abstract. Rule-based specifications have been very successful as a declarative approach in many domains, due to the handy yet solid foundations offered by rule-based machineries like term and graph rewriting. Realistic problems, however, call for suitable techniques to guarantee scalability. For instance, many domains exhibit a hierarchical structure that can be exploited conveniently. This is particularly evident for composition associations of models. We propose an explicit representation of such structured models and a methodology that exploits it for the description and analysis of model-and rule-based systems. The approach is presented in the framework of rewriting logic and its efficient implementation in the rewrite engine Maude and is illustrated with a case study.

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SAM-SoS: A Stochastic Software Architecture Modeling and Verification Approach for Complex System-of-Systems

et al. 2020

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A System-of-Systems (SoS) is a complex, dynamic system whose Constituent Systems (CSs) are not known precisely at design time, and the environment in which they operate is uncertain. SoS behavior is unpredictable due to underlying architectural characteristics such as autonomy and independence. Although the stochastic composition of CSs is vital to achieving SoS missions, their unknown behaviors and impact on system properties are unavoidable. Moreover, unknown conditions and volatility have significant effects on crucial Quality Attributes (QAs) such as performance, reliability and security. Hence, the structure and behavior of a SoS must be modeled and validated quantitatively to foresee any potential impact on the properties critical for achieving the missions. Current modeling approaches lack the essential syntax and semantics required to model and verify SoS behaviors at design time and cannot offer alternative design choices for better design decisions. Therefore, the majority of existing techniques fail to provide qualitative and quantitative verification of SoS architecture models. Consequently, we have proposed an approach to model and verify Non-Deterministic (ND) SoS in advance by extending the current algebraic notations for the formal models as a hybrid stochastic formalism to specify and reason architectural elements with the required semantics. A formal stochastic model is developed using a hybrid approach for architectural descriptions of SoS with behavioral constraints. Through a model-driven approach, stochastic models are then translated into PRISM using formal verification rules. The effectiveness of the approach has been tested with an end-to-end case study design of an emergency response SoS for dealing with a fire situation. Architectural analysis is conducted on the stochastic model, using various qualitative and quantitative measures for SoS missions. Experimental results reveal critical aspects of SoS architecture model that facilitate better achievement of missions and QAs with improved design, using the proposed approach.

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Advances in Probabilistic Model Checking

Cited by 10 publications

References 55 publications

A Quantitative Analysis of Collision Resolution Protocol for Wireless Sensor Network

A Quantitative Analysis of Collision Resolution Protocol for Wireless Sensor Network

Exploiting the Hierarchical Structure of Rule-Based Specifications for Decision Planning

SAM-SoS: A Stochastic Software Architecture Modeling and Verification Approach for Complex System-of-Systems

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