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Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

AuthorsCastaño, Fernando ; Strzelczak, Stanisław; Villalonga, Alberto; Haber, Rodolfo E.; Kossakowska, Joanna
KeywordsCyber-Physical Systems
Reliability assessment
LiDAR sensor
Driving assistance
Obstacle recognition
Reinforcement learning
Artificial Intelligence-based modelling
Issue Date27-Sep-2019
PublisherMolecular Diversity Preservation International
CitationRemote Sensing 11 (19): 2252 (2019)
AbstractNowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.
Publisher version (URL)https://doi.org/10.3390/rs11192252
Appears in Collections:(CAR) Artículos
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