Runtime Monitoring of Stochastic Cyber-Physical Systems with Hybrid State

Sistla, A. Prasad; Žefran, Miloš; Feng, Yunli

doi:10.1007/978-3-642-29860-8_21

Cited by 18 publications

(28 citation statements)

References 25 publications

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“…Later, Falcone et al [10] showed that obligation properties form a strict subset of the set of monitorable properties in the sense of [51], but that less properties should be monitored in practice. Sistla et al [52] defined necessary and sufficient conditions for the monitorability of hybrid systems where an Extended Hidden Markov system is monitorable if there exists an arbitrarily-precise monitor stating verdicts on the system outputs. More recently, Rosu [53] defined monitorable properties as safety properties arguing that these can be specified by general (finite-state machine) monitors.…”

Section: Monitorability and Enforceabilitymentioning

confidence: 99%

Runtime enforcement of regular timed properties by suppressing and delaying events

Falcone

Jéron

Marchand

et al. 2016

Science of Computer Programming

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Runtime enforcement is a verification/validation technique aiming at correcting possibly incorrect executions of a system of interest. In this paper, we consider enforcement monitoring for systems where the physical time elapsing between actions matters. Executions are thus modelled as timed words (i.e., sequences of actions with dates). We consider runtime enforcement for timed specifications modelled as timed automata. Our enforcement mechanisms have the power of both delaying events to match timing constraints, and suppressing events when no delaying is appropriate, thus possibly allowing for longer executions. To ease their design and their correctness-proof, enforcement mechanisms are described at several levels: enforcement functions that specify the input-output behaviour in terms of transformations of timed words, constraints that should be satisfied by such functions, enforcement monitors that describe the operational behaviour of enforcement functions, and enforcement algorithms that describe the implementation of enforcement monitors. The feasibility of enforcement monitoring for timed properties is validated by prototyping the synthesis of enforcement monitors from timed automata.

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Section: Monitorability and Enforceabilitymentioning

confidence: 99%

Runtime enforcement of regular timed properties by suppressing and delaying events

Falcone

Jéron

Marchand

et al. 2016

Science of Computer Programming

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“…However, it does not consider time complexity of the monitoring algorithms. As opposed to these, our earlier works [24,25] and the current work, consider internal monitoring where the correctness property is specified on the computations of the system which are not directly observable.…”

Section: Related Workmentioning

confidence: 94%

“…We thus consider Hidden Markov Chains (HMC) to model such discrete state systems. In our earlier work [24,25], we addressed the problem of monitoring a system, modeled as a HMC H, when the correctness specification is given by a deterministic Streett automaton A on the computations of the system. There we considered accuracy measures of a monitor that capture its rates of false alarms and missed alarms.…”

Section: Introductionmentioning

confidence: 99%

Timely monitoring of partially observable stochastic systems

Sistla

Žefran

Feng

et al. 2014

Proceedings of the 17th International Conference on Hybrid Systems: Computation and Control

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Ensuring the correct behavior of cyber physical systems at run time is of critical importance for their safe deployment.Any malfunctioning of such systems should be detected in a timely manner for further actions. This paper addresses the issue of how quickly a monitor raises an alarm after the occurrence of a failure in cyber physical systems. Towards this end, it introduces a class of systems called exponentially converging monitorable systems. The paper shows that failures in these systems can be detected fast by employing the traditional threshold monitors. It shows that the expected failure detection time for exponentially converging monitorable systems has logarithmic relationship with the inverse of the chosen threshold value. The paper identifies well defined natural classes of these systems. Experimental results are presented that confirm the theoretical results on the relationship between the failure detection time and the chosen threshold values.

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“…Particle filtering (PF) has recently been applied to hybrid systems for monitoring and diagnosis purposes, and in particular to estimate the hidden hybrid discrete-continuous state from a set of available measurements [6,2,8,9]. In [6], PF is applied to a class of distributed hybrid systems with autonomous transitions, non-linear system dynamics, and non-Gaussian noise.…”

Section: Related Workmentioning

confidence: 99%

“…In [2], the authors present a PF-based method for discrete-time stochastic hybrid systems, where each particle has two components: a Euclidean component representing the continuous state and a discrete component representing the mode. Their approach combines exact conditional mode probabilities, given the observations, with Sistla et al use PF to investigate the effectiveness of algorithms for monitorability and strong monitorability of partially observable stochastic systems [8,9]. Familiarity with PF is assumed and no further details, except for the number of particles used, are provided.…”

Section: Related Workmentioning

confidence: 99%

Runtime Verification with Particle Filtering

Kalajdzic

Bartocci

Smolka

et al. 2013

Runtime Verification

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Abstract. We introduce Runtime Verification with Particle Filtering (RVPF), a powerful and versatile method for controlling the tradeoff between uncertainty and overhead in runtime verification. Overhead and accuracy are controlled by adjusting the frequency and duration of observation gaps, during which program events are not monitored, and by adjusting the number of particles used in the RVPF algorithm. We succinctly represent the program model, the program monitor, their interaction, and their observations as a dynamic Bayesian network (DBN). Our formulation of RVPF in terms of DBNs is essential for a proper formalization of peek events: low-cost observations of parts of the program state, which are performed probabilistically at the end of observation gaps. Peek events provide information that our algorithm uses to reduce the uncertainty in the monitor state after gaps.We estimate the internal state of the DBN using particle filtering (PF) with sequential importance resampling (SIR). PF uses a collection of conceptual particles (random samples) to estimate the probability distribution for the system's current state: the probability of a state is given by the sum of the importance weights of the particles in that state. After an observed event, each particle chooses a state transition to execute by sampling the DBN's joint transition probability distribution; particles are then redistributed among the states that best predicted the current observation. SIR exploits the DBN structure and the current observation to reduce the variance of the PF and increase its performance.We experimentally compare the overhead and accuracy of our RVPF algorithm with two previous approaches to runtime verification with state estimation: an exact algorithm based on the forward algorithm for HMMs, and an approximate version of that algorithm, which uses precomputation to reduce runtime overhead. Our results confim RVPF's versatility, showing how it can be used to control the tradeoff between execution time and memory usage while, at the same time, being the most accurate of the three algorithms.

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Runtime Monitoring of Stochastic Cyber-Physical Systems with Hybrid State

Cited by 18 publications

References 25 publications

Runtime enforcement of regular timed properties by suppressing and delaying events

Runtime enforcement of regular timed properties by suppressing and delaying events

Timely monitoring of partially observable stochastic systems

Runtime Verification with Particle Filtering

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