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

Autor: Stanisław Strzelczak, Rodolfo E. Haber, Alberto Villalonga, Joanna Kossakowska, Fernando Castaño
Přispěvatelé: European Commission, Ministerio de Ciencia, Innovación y Universidades (España), Polish National Agency for Academic Exchange, Castaño, Fernando [0000-0002-4801-9224], Villalonga, Alberto [0000-0002-9268-0175], Haber, Rodolfo E. [0000-0002-2881-0166], Kossakowska, Joanna [0000-0002-4519-6225], Castaño, Fernando, Villalonga, Alberto, Haber, Rodolfo E., Kossakowska, Joanna
Rok vydání: 2019
Předmět:
reinforcement learning
European level
Computer science
Science
Robótica e Informática Industrial
Automotive industry
Reliability assessment
02 engineering and technology
cyber-physical systems
LiDAR sensor
7. Clean energy
Obstacle recognition
lidar sensor
Reinforcement learning
0202 electrical engineering
electronic engineering
information engineering
internet-of-things
Reliability (statistics)
artificial intelligence-based modelling
Artificial Intelligence-based modelling
Cyber-Physical Systems
business.industry
driving assistance
Cyber-physical system
020206 networking & telecommunications
Lidar
Internet-of-Things
obstacle recognition
Key (cryptography)
Systems engineering
General Earth and Planetary Sciences
020201 artificial intelligence & image processing
Driving assistance
Internet of Things
business
reliability assessment
Zdroj: Digital.CSIC. Repositorio Institucional del CSIC
instname
Remote Sensing, ISSN 2072-4292, 2019-09, Vol. 11, No. 19
Archivo Digital UPM
Universidad Politécnica de Madrid
Remote Sensing, Vol 11, Iss 19, p 2252 (2019)
IEEE Transactions on Industrial Informatics
Remote Sensing
Volume 11
Issue 19
Pages: 2252
Popis: 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.
This work was partially supported by the project Power2Power: Providing next-generation silicon-based power solutions in transport and machinery for significant decarbonisation in the next decade, funded by the Electronic Component Systems for European Leadership (ECSEL-JU) Joint Undertaking and the Ministry of Science, Innovation and Universities (MICINN), under grant agreement No 826417. In addition, this work was also funded by the Spanish Ministry of Science, Innovation and Universities through the project COGDRIVE (DPI2017-86915-C3-1-R). Preparation of this publication was also partially co-financed by the Polish National Agency for Academic Exchange (NAWA) through the project: “Industry 4.0 in Production and Aeronautical Engineering (IPAE)”.
Databáze: OpenAIRE
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