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Usage of IoT edge approach for road quality analysis

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the paper, the authors present the analysis of implementation of IoT system for road quality analysis. The proposed system was prepared for edge processing, on device. It allows to reduce the amount of data sent to cloud computing aggregation subsystem, sending only 2.5% of the original data. Several algorithms for road quality analysis were implemented on a real device and tested under real conditions. The system was compared with the state-of-the-art offline processing approach and showed the same accuracy on a set of known road artefacts, while detecting 92% of the artefacts recognized by the original cloud computing processing system.
Słowa kluczowe
Rocznik
Strony
15--24
Opis fizyczny
Bibliogr. 18 poz., fig.
Twórcy
  • Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Department of Computer Science, Poland
  • Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Department of Computer Science, Poland
Bibliografia
  • [1] Astarita, V., Caruso, M. V., Danieli, G., Festa, D. C., Giofrè, V. P., Iuele, T., & Vaiana, R. (2012). A mobile application for road surface quality control: UNIquALroad. Procedia - Social and Behavioral Sciences, 54, 1135–1144. https://doi.org/10.1016/j.sbspro.2012.09.828
  • [2] Badurowicz, M., & Cieplak, T. (2019). Real-time road quality assessment using smartphones and cloud lambda architecture. MATEC Web of Conferences, 252, 03011. https://doi.org/10.1051/matecconf/201925203011
  • [3] Badurowicz, M., Cieplak, T., & Montusiewicz, J. (2016). The cloud computing stream analysis system for road artefacts detection. In P. Gaj, A. Kwiecień & P. Stera (Eds.), Computer Networks: 23rd International Conference, Proceedings (pp. 360–369). Springer International Publishing. https://doi.org/10.1007/978-3-319-39207-3_31
  • [4] Badurowicz, M., Montusiewicz, J., & Karczmarek, P. (2020). Detection of road artefacts using fuzzy adaptive thresholding. 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp.1–8). IEEE. https://doi.org/10.1109/FUZZ48607.2020.9177822
  • [5] Czerwinski, D., & Przylucki, S. (2016). Open-source microcontroller development board in wireless sensor networks classes. ICERI2016 Proceedings, 1, 2294–2300. https://doi.org/10.21125/iceri.2016.1504
  • [6] ESP32 Series Datasheet. (2023). Espressif Systems (Shanghal) https://www.espressif.com/sites/default/files/documentation/esp32_datasheet_en.pdf
  • [7] Generalna Dyrekcja Dróg Krajowych i Autostrad. (2022). Raport o stanie technicznym nawierzchni sieci dróg krajowych na koniec 2021 roku. https://www.gov.pl/web/gddkia/raport-o-stanie-technicznym-nawierzchni-sieci-drog-krajowych-na-koniec-2021-roku
  • [8] Gonzalez, L. C., Moreno, R.., Escalante, H. J., Martinez, F., & Carlos, M. R. (2017). Learning roadway surface disruption patterns using the bag of words representation. IEEE Transactions on Intelligent Transportation Systems (pp. 1–13). IEEE. https://doi.org/10.1109/TITS.2017.2662483
  • [9] Hart, M. (2022). TinyGPSPlus. https://github.com/mikalhart/TinyGPSPluskamami.pl. (2022). GY-GPS6MV2. https://kamami.pl/gps/563067-gy-gps6mv2-modul-gps-z-ukladem-u-blox-neo-6m.html
  • [10] Kono, A. (2020). MPU9250_asukiaaa. https://github.com/asukiaaa/MPU9250_asukiaaa
  • [11] Loprencipe, G., de Almeida Filho, F. G. V., de Oliveira, R. H., & Bruno, S. (2021). Validation of a low-cost pavement monitoring inertial-based system for urban road networks. Sensors, 21(9), 3127. https://doi.org/10.3390/s21093127
  • [12] Mednis, A., Strazdins, G., Zviedris, R., Kanonirs, G., & Selavo, L. (2011). Real time pothole detection using android smartphones with accelerometers. 2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS’11 (pp. 1-6). IEEE. https://doi.org/10.1109/DCOSS.2011.5982206
  • [13] Mohan, P., Padmanabhan, V. N., & Ramjee, R. (2008). TrafficSense : Rich monitoring of road and traffic conditions using mobile smartphones. In The 6th ACM Conference on Embedded Networked Sensor Systems (pp. 1–29). The ACM Digital Library. https://doi.org/MSR-TR-2008-59
  • [14] Nguyen, V. K., Renault, É., & Ha, V. H. (2019). Road anomaly detection using smartphone: a brief analysis. Mobile, Secure, and Programmable Networking. MSPN 2018. Lecture Notes in Computer Science (vol. 11005). Springer. https://doi.org/10.1007/978-3-030-03101-5_8
  • [15] Pérez, E., Araiza, J. C., Pozos, D., Bonilla, E., Hernández, J. C., & Cortes, J. A. (2021). Application for functionality and registration in the cloud of a microcontroller development board for IoT in AWS. Applied Computer Science, 17(2), 14–27. https://doi.org/10.23743/acs-2021-10
  • [16] Powiatowy Zarząd Dróg w Hrubieszowie. (2022). Ocena stanu technicznego dróg powiatowych powiatu hrubieszowskiego. https://www.starostwo.hrubieszow.pl/dat/attach/2022-04/31923_ad-10-ocena-stanu-technicznego-2021.pdf
  • [17] Singh, G., Bansal, D., Sofat, S., & Aggarwal, N. (2017). Smart patrolling: An efficient road surface monitoring using smartphone sensors and crowdsourcing. Pervasive and Mobile Computing, 40, 71–88. https://doi.org/10.1016/j.pmcj.2017.06.002
  • [18] Vamsee, K. K. M., Vimalkumar, K., Vinodhini, R. E., & Archanaa, R. (2017). An early detection-warning system to identify speed breakers and bumpy roads using sensors in smartphones. International Journal of Electrical and Computer Engineering, 7(3), 1377–1384. https://doi.org/10.11591/ijece.v7i3.pp1377-1384
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-262c1281-8ad3-431f-9699-1ac296319abf
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