The first step in the diagnosis of failure occurrences in discrete event systems is the verification of the system diagnosability. Several works have addressed this problem using either diagnosers or verifiers for both centralized and decentralized architectures. In this technical note, we propose a new algorithm to verify decentralized diagnosability of discrete event systems. The proposed algorithm requires polynomial time in the number of states and events of the system and has lower computational complexity than all other methods found in the literature. In addition, it can also be applied to the centralized case.Index Terms-Automata, computational complexity, diagnosability verification, discrete-event systems (DES), failure diagnosis.

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.