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Keywords = uppaal-SMC

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40 pages, 1890 KiB  
Review
A Systematic Review on the Applications of Uppaal
by Iwona Grobelna, Krystian Gajewski and Andrei Karatkevich
Sensors 2025, 25(11), 3484; https://doi.org/10.3390/s25113484 - 31 May 2025
Viewed by 651
Abstract
This paper presents a systematic review on possible applications of the Uppaal tool. This tool, an integrated environment for the modeling, validation, and verification of real-time systems modeled as networks of timed automata, is currently used in various domains of science and engineering. [...] Read more.
This paper presents a systematic review on possible applications of the Uppaal tool. This tool, an integrated environment for the modeling, validation, and verification of real-time systems modeled as networks of timed automata, is currently used in various domains of science and engineering. A systematic review of the literature from the years 2022 and 2023 was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedure. The aim was to identify the current application areas of various versions of the Uppaal tool, including CORA, TIGA, SMC, and Stratego. A total of 188 studies were included in the review. Quantitative information on the distribution of research papers regarding access options, scientific databases, types of papers, and geographical location was obtained. This review highlights the need for further development of the Uppaal tool. In addition, it includes a brief comparison with other mainstream formal validation tools, explores the applicability of different Uppaal versions, and offers practical guidelines for version selection. Finally, key open challenges and their potential solutions are discussed to support future research and tool enhancement. Full article
(This article belongs to the Special Issue Intelligent Industrial Process Control Systems: 2nd Edition)
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25 pages, 824 KiB  
Article
Formal Verification of Heuristic Autonomous Intersection Management Using Statistical Model Checking
by Aaditya Prakash Chouhan and Gourinath Banda
Sensors 2020, 20(16), 4506; https://doi.org/10.3390/s20164506 - 12 Aug 2020
Cited by 8 | Viewed by 4100
Abstract
Autonomous vehicles are gaining popularity throughout the world among researchers and consumers. However, their popularity has not yet reached the level where it is widely accepted as a fully developed technology as a large portion of the consumer base feels skeptical about it. [...] Read more.
Autonomous vehicles are gaining popularity throughout the world among researchers and consumers. However, their popularity has not yet reached the level where it is widely accepted as a fully developed technology as a large portion of the consumer base feels skeptical about it. Proving the correctness of this technology will help in establishing faith in it. That is easier said than done because of the fact that the formal verification techniques has not attained the level of development and application that it is ought to. In this work, we present Statistical Model Checking (SMC) as a possible solution for verifying the safety of autonomous systems and algorithms. We apply it on Heuristic Autonomous Intersection Management (HAIM) algorithm. The presented verification routine can be adopted for other conflict point based autonomous intersection management algorithms as well. Along with verifying the HAIM, we also demonstrate the modeling and verification applied at each stage of development to verify the inherent behavior of the algorithm. The HAIM scheme is formally modeled using a variant of the language of Timed Automata. The model consists of automata that encode the behavior of vehicles, intersection manager (IM) and collision checkers. To verify the complete nature of the heuristic and ensure correct modeling of the system, we model it in layers and verify each layer separately for their expected behavior. Along with that, we perform implementation verification and error injection testing to ensure faithful modeling of the system. Results show with high confidence the freedom from collisions of the intersection controlled by the HAIM algorithm. Full article
(This article belongs to the Special Issue Sensor Data Fusion for Autonomous and Connected Driving)
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31 pages, 1653 KiB  
Article
A Model-Checking-Based Framework for Analyzing Ambient Assisted Living Solutions
by Ashalatha Kunnappilly, Raluca Marinescu and Cristina Seceleanu
Sensors 2019, 19(22), 5057; https://doi.org/10.3390/s19225057 - 19 Nov 2019
Cited by 8 | Viewed by 4170
Abstract
Since modern ambient assisted living solutions integrate a multitude of assisted-living functionalities, out of which some are safety critical, it is desirable that these systems are analyzed at their design stage to detect possible errors. To achieve this, one needs suitable architectures that [...] Read more.
Since modern ambient assisted living solutions integrate a multitude of assisted-living functionalities, out of which some are safety critical, it is desirable that these systems are analyzed at their design stage to detect possible errors. To achieve this, one needs suitable architectures that support the seamless design of the integrated assisted-living functions, as well as capabilities for the formal modeling and analysis of the architecture. In this paper, we attempt to address this need, by proposing a generic integrated ambient assisted living system architecture, consisting of sensors, data collection, local and cloud processing schemes, and an intelligent decision support system, which can be easily extended to suit specific architecture categories. Our solution is customizable, therefore, we show three instantiations of the generic model, as simple, intermediate, and complex configurations, respectively, and show how to analyze the first and third categories by model checking. Our approach starts by specifying the architecture, using an architecture description language, in our case, the Architecture Analysis and Design Language, which can also account for the probabilistic behavior of such systems, and captures the possibility of component failure. To enable formal analysis, we describe the semantics of the simple and complex architectures within the framework of timed automata. We show that the simple architecture is amenable to exhaustive model checking by employing the UPPAAL tool, whereas for the complex architecture we resort to statistical model checking for scalability reasons. In this case, we apply the statistical extension of UPPAAL, namely UPPAAL SMC. Our work paves the way for the development of formally assured future ambient assisted living solutions. Full article
(This article belongs to the Special Issue IoT Sensors in E-Health)
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