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

Castaño Romero, Fernando and Strzelczak, Stanisław and Villalonga, Alberto and Haber Guerra, Rodolfo E. and Kossakowska, Joanna (2019). Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study. "Remote Sensing", v. 11 (n. 19); pp. 1-20. ISSN 2072-4292. https://doi.org/10.3390/rs11192252.

Description

Title: Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study
Author/s:
  • Castaño Romero, Fernando
  • Strzelczak, Stanisław
  • Villalonga, Alberto
  • Haber Guerra, Rodolfo E.
  • Kossakowska, Joanna
Item Type: Article
Título de Revista/Publicación: Remote Sensing
Date: September 2019
ISSN: 2072-4292
Volume: 11
Subjects:
Freetext Keywords: Cyber-Physical Systems; reliability assessment; Internet-of-Things; LiDAR sensor; drivingassistance; obstacle recognition; reinforcement learning; Artificial Intelligence-based modelling
Faculty: Centro de Automática y Robótica (CAR) UPM-CSIC
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Nowadays, 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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainDPI2017-86915-C3-1-RCOGDRIVEUnspecifiedNavegación de inspiración cognitiva para conducción autónoma
Horizon 2020826417Power2PowerINFINEON TECHNOLOGIES DRESDEN GMBH& CO KGThe next-generation silicon-based power solutions in mobility, industry and grid for sustainable decarbonisation in the next decade

More information

Item ID: 67179
DC Identifier: https://oa.upm.es/67179/
OAI Identifier: oai:oa.upm.es:67179
DOI: 10.3390/rs11192252
Official URL: https://www.mdpi.com/2072-4292/11/19/2252/htm
Deposited by: Memoria Investigacion
Deposited on: 26 Aug 2022 08:08
Last Modified: 26 Aug 2022 08:08
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