- -

Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities

Show full item record

Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Blanes Noguera, F. (2020). Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities. Sensors. 20(1):1-18. https://doi.org/10.3390/s20010112

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/167838

Files in this item

Item Metadata

Title: Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities
Author: Poza-Lujan, Jose-Luis Posadas-Yagüe, Juan-Luis Simó Ten, José Enrique Blanes Noguera, Francisco
UPV Unit: Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors
Issued date:
Abstract:
[EN] Object recognition, which can be used in processes such as reconstruction of the environment map or the intelligent navigation of vehicles, is a necessary task in smart city environments. In this paper, we propose an ...[+]
Subjects: Smart environment , Smart sensors , Distributed architectures , Object detection , Information integration , Smart cities
Copyrigths: Reconocimiento (by)
Source:
Sensors. (eissn: 1424-8220 )
DOI: 10.3390/s20010112
Publisher:
MDPI AG
Publisher version: https://doi.org/10.3390/s20010112
Project ID:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-86520-C3-1-R/ES/SISTEMAS INFORMATICOS PREDECIBLES Y CONFIABLES PARA LA INDUSTRIA 4.0/
Thanks:
This research was funded by the Spanish Science and Innovation Ministry grant number MICINN: CICYT project PRECON-I4: "Predictable and dependable computer systems for Industry 4.0" TIN2017-86520-C3-1-R.
Type: Artículo

References

Munera, E., Poza-Lujan, J.-L., Posadas-Yagüe, J.-L., Simó-Ten, J.-E., & Noguera, J. (2015). Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems. Sensors, 15(8), 18080-18101. doi:10.3390/s150818080

Cao, J., Song, C., Peng, S., Xiao, F., & Song, S. (2019). Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles. Sensors, 19(18), 4021. doi:10.3390/s19184021

González García, C., Meana-Llorián, D., Pelayo G-Bustelo, B. C., Cueva Lovelle, J. M., & Garcia-Fernandez, N. (2017). Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes. Future Generation Computer Systems, 76, 301-313. doi:10.1016/j.future.2016.12.033 [+]
Munera, E., Poza-Lujan, J.-L., Posadas-Yagüe, J.-L., Simó-Ten, J.-E., & Noguera, J. (2015). Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems. Sensors, 15(8), 18080-18101. doi:10.3390/s150818080

Cao, J., Song, C., Peng, S., Xiao, F., & Song, S. (2019). Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles. Sensors, 19(18), 4021. doi:10.3390/s19184021

González García, C., Meana-Llorián, D., Pelayo G-Bustelo, B. C., Cueva Lovelle, J. M., & Garcia-Fernandez, N. (2017). Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes. Future Generation Computer Systems, 76, 301-313. doi:10.1016/j.future.2016.12.033

Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1-10. doi:10.1016/j.jii.2017.04.005

Li, S., Xu, L. D., & Zhao, S. (2014). The internet of things: a survey. Information Systems Frontiers, 17(2), 243-259. doi:10.1007/s10796-014-9492-7

Zdraveski, V., Mishev, K., Trajanov, D., & Kocarev, L. (2017). ISO-Standardized Smart City Platform Architecture and Dashboard. IEEE Pervasive Computing, 16(2), 35-43. doi:10.1109/mprv.2017.31

Dastjerdi, A. V., & Buyya, R. (2016). Fog Computing: Helping the Internet of Things Realize Its Potential. Computer, 49(8), 112-116. doi:10.1109/mc.2016.245

Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for Smart Cities. IEEE Internet of Things Journal, 1(1), 22-32. doi:10.1109/jiot.2014.2306328

Hancke, G., Silva, B., & Hancke, Jr., G. (2012). The Role of Advanced Sensing in Smart Cities. Sensors, 13(1), 393-425. doi:10.3390/s130100393

Chen, Y. (2016). Industrial information integration—A literature review 2006–2015. Journal of Industrial Information Integration, 2, 30-64. doi:10.1016/j.jii.2016.04.004

Lim, G. H., Suh, I. H., & Suh, H. (2011). Ontology-Based Unified Robot Knowledge for Service Robots in Indoor Environments. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 41(3), 492-509. doi:10.1109/tsmca.2010.2076404

Zhang, J. (2010). Multi-source remote sensing data fusion: status and trends. International Journal of Image and Data Fusion, 1(1), 5-24. doi:10.1080/19479830903561035

Deng, X., Jiang, Y., Yang, L. T., Lin, M., Yi, L., & Wang, M. (2019). Data fusion based coverage optimization in heterogeneous sensor networks: A survey. Information Fusion, 52, 90-105. doi:10.1016/j.inffus.2018.11.020

Jain, A. K., Duin, P. W., & Jianchang Mao. (2000). Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 4-37. doi:10.1109/34.824819

Eugster, P. T., Felber, P. A., Guerraoui, R., & Kermarrec, A.-M. (2003). The many faces of publish/subscribe. ACM Computing Surveys, 35(2), 114-131. doi:10.1145/857076.857078

Adam, M. S., Anisi, M. H., & Ali, I. (2020). Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions. Future Generation Computer Systems, 107, 909-923. doi:10.1016/j.future.2017.12.011

Gaur, A., Scotney, B., Parr, G., & McClean, S. (2015). Smart City Architecture and its Applications Based on IoT. Procedia Computer Science, 52, 1089-1094. doi:10.1016/j.procs.2015.05.122

Byers, C. C. (2017). Architectural Imperatives for Fog Computing: Use Cases, Requirements, and Architectural Techniques for Fog-Enabled IoT Networks. IEEE Communications Magazine, 55(8), 14-20. doi:10.1109/mcom.2017.1600885

Dautov, R., Distefano, S., Bruneo, D., Longo, F., Merlino, G., Puliafito, A., & Buyya, R. (2018). Metropolitan intelligent surveillance systems for urban areas by harnessing IoT and edge computing paradigms. Software: Practice and Experience, 48(8), 1475-1492. doi:10.1002/spe.2586

Rincon, J. A., Poza-Lujan, J.-L., Julian, V., Posadas-Yagüe, J.-L., & Carrascosa, C. (2016). Extending MAM5 Meta-Model and JaCalIV E Framework to Integrate Smart Devices from Real Environments. PLOS ONE, 11(2), e0149665. doi:10.1371/journal.pone.0149665

Pérez Tijero, H., & Gutiérrez, J. J. (2018). Desarrollo de Sistemas Distribuidos de Tiempo Real y de Criticidad Mixta a través del Estándar DDS. Revista Iberoamericana de Automática e Informática industrial, 15(4), 439. doi:10.4995/riai.2017.9000

Amurrio, A., Azketa, E., Gutiérrez, J. J., Aldea, M., & Parra, J. (2019). Una revisión de técnicas para la optimización del despliegue y planificación de sistemas de tiempo real distribuidos. Revista Iberoamericana de Automática e Informática industrial, 16(3), 249. doi:10.4995/riai.2019.10997

Turtlebot http://turtlebot.com

Chen, L., Wei, H., & Ferryman, J. (2013). A survey of human motion analysis using depth imagery. Pattern Recognition Letters, 34(15), 1995-2006. doi:10.1016/j.patrec.2013.02.006

Munera Sánchez, E., Muñoz Alcobendas, M., Blanes Noguera, J., Benet Gilabert, G., & Simó Ten, J. (2013). A Reliability-Based Particle Filter for Humanoid Robot Self-Localization in RoboCup Standard Platform League. Sensors, 13(11), 14954-14983. doi:10.3390/s131114954

Adams, R., & Bischof, L. (1994). Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6), 641-647. doi:10.1109/34.295913

Chow, J., Lichti, D., Hol, J., Bellusci, G., & Luinge, H. (2014). IMU and Multiple RGB-D Camera Fusion for Assisting Indoor Stop-and-Go 3D Terrestrial Laser Scanning. Robotics, 3(3), 247-280. doi:10.3390/robotics3030247

[-]

recommendations

 

This item appears in the following Collection(s)

Show full item record