Monitoring with uncertainty

Bartocci, Ezio; Grosu, Radu

doi:10.4204/eptcs.124.1

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Given how likely it is that an event will participate in a violation of a given temporal property, the system decides to include it in the monitored stream. The aforementioned approaches all differ in the probabilistic formalism used to model the monitored system [52].…”

Section: Precisionmentioning

confidence: 99%

A survey of challenges for runtime verification from advanced application domains (beyond software)

Sánchez

Schneider

Ahrendt

et al. 2019

Form Methods Syst Des

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Runtime verification is an area of formal methods that studies the dynamic analysis of execution traces against formal specifications. Typically, the two main activities in runtime verification efforts are the process of creating monitors from specifications, and the algorithms for the evaluation of traces against the generated monitors. Other activities involve the instrumentation of the system to generate the trace and the communication between the system under analysis and the monitor.Most of the applications in runtime verification have been focused on the dynamic analysis of software, even though there are many more potential applications to other computational devices and target systems. In this paper we present a collection of challenges for runtime verification extracted from concrete application domains, focusing on the difficulties that must be overcome to tackle these specific challenges. The computational models that characterize these domains require to devise new techniques beyond the current state of the art in runtime verification.

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Section: Precisionmentioning

confidence: 99%

A survey of challenges for runtime verification from advanced application domains (beyond software)

Sánchez

Schneider

Ahrendt

et al. 2019

Form Methods Syst Des

Self Cite

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“…We believe that the use and decentralization of richer specification languages are desirable. For instance, we consider (i) using a counting semantics able to compute the number of steps needed to witness the satisfaction or violation of a specification [5] (ii) using techniques allowing to deal with uncertainty (e.g., in case of message loss) [7] (iii) using spatio-temporal specifications (e.g. [28]) to reason on physical locations in the house, and (iv) using a quantitative semantics possibly with time [4].…”

Section: Future Workmentioning

confidence: 99%

Bringing Runtime Verification Home -- A Case Study on the Hierarchical Monitoring of Smart Homes using Decentralized Specifications

El-Hokayem¹,

Falcone²

2018

Preprint

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We use runtime verification (RV) to check various properties in a smart apartment. The properties can be broken down into three types: behavioral correctness of the apartment sensors, detection of specific user activities (known as activities of daily living), and composition of properties of the previous types. The context of the smart apartment provides us with a complex system with a large number of components with two different hierarchies to group properties and sensors: geographically within the same room, floor or globally in the apartment, and logically following the different types of properties. We leverage a recent approach to decentralized RV of decentralized specifications, where monitors have their own specifications and communicate together to verify more general specifications. This allows us to re-use specifications, and combine them to: (1) scale beyond existing centralized RV techniques, and (2) greatly reduce computation and communication costs.Sensors and actuators are used to create "smart" environments which track the data across sensors and humanmachine interaction. One particular area of interest consists of homes (or apartments) equipped with a myriad of sensors and actuators, called smart homes [16]. Smart homes are capable of providing added services to users. These services rely on detecting the user behavior and the context of such activities [11], typically detecting activities of daily living (ADL) [41,13] from sensor information. Detecting ADL allows to optimize resource consumption (such as electricity [1]), improve the quality of life for the elderly [37] and users suffering from mild impairment [42].Relying on information from multiple sources and observing behavior is not just constrained to activities. It is also used with techniques that verify the correct behavior of systems. Runtime Verification (RV) [30,35,24,6] is a lightweight formal method which consists in verifying that a run of a system is correct wrt a specification. The specification formalizes the behavior of the system typically in logics (such as variants of Linear Temporal Logic, LTL [39]) or finite-state machines. Based on the provided specification, monitors are automatically synthesized to run alongside the system and verify whether or not the system execution complies with the specification. RV techniques have been used for instance in the context of automotive [15] and medical [36] systems. In both cases, RV is used to verify communication patterns between components and their adherence to the architecture and their formal specifications.While RV can be used to check that the devices in a smart home are performing as expected, we believe it can be extended to monitor ADL properties, and complex behavior on the activities themselves. We identify three classes of properties for applying RV to a smart home. The first class pertains to the system behavior. These properties are used to check the correct behavior of the sensors, and detect faulty sensors. Ensuring that the system is behaving correctly is what i...

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“…This means, events might have been disregarded as to conserve resources. This ranges from conceptual models on how to deal with uncertainty [8] to approaches on how to use the available information to overcome the obstacles of uncertainty [9,10].…”

Section: Uncertaintymentioning

confidence: 99%