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• W2885871490 abstract "Wireless sensor networks have been considered as an effective solution to a wide range of applications due to their prominent characteristics concerning information retrieving and distributed processing. When visual information can be also retrieved by sensor nodes, applications acquire a more comprehensive perception of monitored environments, fostering the creation of wireless visual sensor networks. As such networks are being more often considered for critical monitoring and control applications, usually related to catastrophic situation prevention, security enhancement and crises management, fault tolerance becomes a major expected service for visual sensor networks. A way to address this issue is to evaluate the system dependability through quantitative attributes (e.g., reliability and availability), which require a proper modeling strategy to describe the system behavior. That way, in this paper, we propose a methodology to model and evaluate the dependability of wireless visual sensor networks using Fault Tree Analysis and Markov Chains. The proposed modeling strategy considers hardware, battery, link and coverage failures, besides considering routing protocols on the network communication behavior. The methodology is automated by a framework developed and integrated with the SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) tool. The achieved results show that this methodology is useful to compare different network implementations and the corresponding dependability, enabling the uncovering of potentially weak points in the network behavior." @default.
• W2885871490 created "2018-08-22" @default.
• W2885871490 creator A5011761859 @default.
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• W2885871490 date "2018-08-10" @default.
• W2885871490 modified "2023-10-16" @default.
• W2885871490 title "Automated Methodology for Dependability Evaluation of Wireless Visual Sensor Networks" @default.
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• W2885871490 doi "https://doi.org/10.3390/s18082629" @default.
• W2885871490 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6111997" @default.
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• W2885871490 hasPublicationYear "2018" @default.
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