Run-Time Probabilistic Model Checking for Failure Prediction: A Smart Lift Case Study

Xin, X., Keoh, S. L. , Sevegnani, M. and Saerbeck, M. (2023) Run-Time Probabilistic Model Checking for Failure Prediction: A Smart Lift Case Study. In: IEEE 8th World Forum on Internet of Things (WF-IoT2022), Yokohama, Japan, 31 Oct - 04 Nov, ISBN 9781665491532 (doi: 10.1109/WF-IoT54382.2022.10152177)

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Abstract

Modern smart systems are powered by cyber-physical systems integrating sensor networks with service-oriented architecture to automate their operations. Control algorithms deployed on smart systems are now driven by connected sensors with control decisions being made based on the sensor generated data. As sensors tend to be unreliable and prone to failures, this has resulted in the increase of system errors due to the wrong control decisions derived from the faulty sensor readings, thus affecting the performance, safety and quality of the operational tasks. Existing methodologies to evaluate and test such systems do not take into account the complexity and uncertainty exhibited by the underlying sensor networks, and hence not being able to dynamically verify the behaviour of the smart systems at run-time. This paper proposes a novel run-time verification framework combining sensor-level fault detection and system-level probabilistic model checking. This framework rigorously quantifies the trustworthiness of sensor readings, hence enabling formal reasoning for system failure prediction. We evaluated our approach on a passenger lift equipped with sensor networks to monitor its main components continuously. The results indicate that the proposed verification framework involving the quantified sensor's trustworthiness enhances the accuracy of the system failure prediction.

Item Type:Conference Proceedings
Additional Information:X. Xin is partially funded by the Singapore Economic Development Board (EDB) through the Industrial Postgraduate Programme (IPP) Grant. M. Sevegnani is supported by the EPSRC under PETRAS SRF grants MAGIC and FARM (EP/S035362/1).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Keoh, Dr Sye Loong and Sevegnani, Dr Michele
Authors: Xin, X., Keoh, S. L., Sevegnani, M., and Saerbeck, M.
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781665491532
Copyright Holders:Copyright © 2022 IEEE
First Published:First published in 2022 IEEE 8th World Forum on Internet of Things (WF-IoT)
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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