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

Fernando Castaño , Stanisław Strzelczak , Alberto Villalonga , Rodolfo E. Haber , Joanna Kossakowska

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.
Author Fernando Castaño
Fernando Castaño,,
-
, Stanisław Strzelczak (FPE / IOPS)
Stanisław Strzelczak,,
- The Institute of Organization of Production Systems
, Alberto Villalonga
Alberto Villalonga,,
-
, Rodolfo E. Haber
Rodolfo E. Haber,,
-
, Joanna Kossakowska (FPE / IoMP)
Joanna Kossakowska,,
- The Institute of Manufacturing Processes
Journal seriesRemote Sensing, ISSN 2072-4292
Issue year2019
Vol11
No19
Publication size in sheets112.6
ASJC Classification1900 General Earth and Planetary Sciences
DOIDOI:10.3390/rs11192252
Languageen angielski
Score (nominal)100
Score sourcejournalList
ScoreMinisterial score = 100.0, 10-06-2020, ArticleFromJournal
Publication indicators WoS Citations = 1; Scopus Citations = 10; Scopus SNIP (Source Normalised Impact per Paper): 2017 = 1.559; WoS Impact Factor: 2018 = 4.118 (2) - 2018=4.74 (5)
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