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

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 architecture that integrates heterogeneously distributed information to recognize objects in intelligent environments. The architecture is based on the IoT/Industry 4.0 model to interconnect the devices, which are called smart resources. Smart resources can process local sensor data and offer information to other devices as a service. These other devices can be located in the same operating range (the edge), in the same intranet (the fog), or on the Internet (the cloud). Smart resources must have an intelligent layer in order to be able to process the information. A system with two smart resources equipped with different image sensors is implemented to validate the architecture. Our experiments show that the integration of information increases the certainty in the recognition of objects by 2-4%. Consequently, in intelligent environments, it seems appropriate to provide the devices with not only intelligence, but also capabilities to collaborate closely with other devices.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.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/s20010112S118201Munera, 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/s150818080Cao, 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/s19184021González 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.2016.12.033Lu, 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.2017.04.005Li, S., Xu, L. D., & Zhao, S. (2014). The internet of things: a survey. Information Systems Frontiers, 17(2), 243-259. doi:10.1007/s10796-014-9492-7Zdraveski, 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.2017.31Dastjerdi, A. V., & Buyya, R. (2016). Fog Computing: Helping the Internet of Things Realize Its Potential. Computer, 49(8), 112-116. doi:10.1109/mc.2016.245Zanella, 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.2014.2306328Hancke, G., Silva, B., & Hancke, Jr., G. (2012). The Role of Advanced Sensing in Smart Cities. Sensors, 13(1), 393-425. doi:10.3390/s130100393Chen, Y. (2016). Industrial information integration—A literature review 2006–2015. Journal of Industrial Information Integration, 2, 30-64. doi:10.1016/j.jii.2016.04.004Lim, 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.2010.2076404Zhang, J. (2010). Multi-source remote sensing data fusion: status and trends. International Journal of Image and Data Fusion, 1(1), 5-24. doi:10.1080/19479830903561035Deng, 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.2018.11.020Jain, 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:10.1109/34.824819Eugster, 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:10.1145/857076.857078Adam, 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.2017.12.011Gaur, 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.2015.05.122Byers, 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.2017.1600885Dautov, 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.2586Rincon, 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.0149665Pérez 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.2017.9000Amurrio, 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.2019.10997Turtlebot http://turtlebot.comChen, 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.2013.02.006Munera 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/s131114954Adams, R., & Bischof, L. (1994). Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6), 641-647. doi:10.1109/34.295913Chow, 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/robotics303024