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Submitted
May 14, 2024
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June 10, 2024
Published
June 10, 2024
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Abstract

Cloud storage services can create an object storage bucket to store our pictures, among them the Cloud Storage FUSE, Scaleway, S3 bucket, Firebase,  etc. intelligent IoT systems generate vast amounts of multi-source industrial data, which necessitate a large amount of storage and processing power to enable real-time data processing and analysis. Cloud computing can be intricately linked into intelligent IIoT systems due to its strong computational and storage capabilities. Cloud Storage for Object Detection using ESP32-CAM. Create a workable solution that supports distributed storage bucket and implement it in a real-world setting. Implement the entire system as an addition to the well-known IoT cloud storage and run multiple experiments to evaluate its functionality in scenarios with varying setups and system. The target objects that are used as data sets are the ESP8266, Wemos D1, and Arduino Uno. Figuring out the ideal parameters for training the FOMO (First Object, More Object) model and then putting it into practice. It was necessary to find a balance between learning rate and accuracy, on the other hand, to maintain the highest possible accuracy in the identification of the microcontroller object to minimise the number of false positive reports. Find the value learning rate effective to this object is 0.01 with F1 score 98.7% and accuracy score 89.58%.

Keywords

Cloud Storage, ESP32CAM, Object Detection, FOMO

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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Imron, I., Satria, B., Karim, S., & Ramadhani, F. (2024). Cloud Storage for Object Detection using ESP32-CAM. TEPIAN, 5(2), 50–57. https://doi.org/10.51967/tepian.v5i2.2994
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References

  1. Abdellatif, M. M., Elshabasy, N. H., Elashmawy, A. E., & AbdelRaheem, M. (2023). A low cost IoT-based Arabic license plate recognition model for smart parking systems. Ain Shams Engineering Journal, 14(6). https://doi.org/10.1016/j.asej.2023.102178
  2. Akshatha, P. S., & Dilip Kumar, S. M. (2023). MQTT and blockchain sharding: An approach to user-controlled data access with improved security and efficiency. Blockchain: Research and Applications, 4(4). https://doi.org/10.1016/j.bcra.2023.100158
  3. Alejandro, L. L., Gulpric, M. M., Lanon, C. J. F., MacAlalag, F. M. A., & Placio, R. M. A. (2023). ICFY (I Care For You): An IOT Based Fall Detection and Monitoring Device using ESP32-CAM and MPU 6050 Sensors. 2023 8th International Conference on Business and Industrial Research, ICBIR 2023 - Proceedings, 1009–1013. https://doi.org/10.1109/ICBIR57571.2023.10147586
  4. Bagchi, T., Mahapatra, A., Yadav, D., Mishra, D., Pandey, A., Chandrasekhar, P., & Kumar, A. (2022). Intelligent security system based on face recognition and IoT. Materials Today: Proceedings, 62, 2133–2137. https://doi.org/10.1016/j.matpr.2022.03.353
  5. Chen, F., Meng, F., Li, Z., Li, L., & Xiang, T. (2024). Public cloud object storage auditing: Design, implementation, and analysis. Journal of Parallel and Distributed Computing, 189. https://doi.org/10.1016/j.jpdc.2024.104870
  6. Elhattab, K., Abouelmehdi, K., & Elatar, S. (2023). New Model to Monitor Plant Growth Remotely using ESP32-CAM and Mobile Application. Proceedings - 10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023. https://doi.org/10.1109/WINCOM59760.2023.10322939
  7. Hammad, S. S., Iskandaryan, D., & Trilles, S. (2023). An unsupervised TinyML approach applied to the detection of urban noise anomalies under the smart cities environment. Internet of Things (Netherlands), 23. https://doi.org/10.1016/j.iot.2023.100848
  8. Hazarika, A., Poddar, S., Nasralla, M. M., & Rahaman, H. (2022). Area and energy efficient shift and accumulator unit for object detection in IoT applications. Alexandria Engineering Journal, 61(1), 795–809. https://doi.org/10.1016/j.aej.2021.04.099
  9. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. http://arxiv.org/abs/1704.04861
  10. Kaur, A., Jadli, A., Sadhu, A., Goyal, S., Mehra, A., & Rahul. (2021). Cloud Based Surveillance using ESP32 CAM. International Conference on Intelligent Technology, System and Service for Internet of Everything, ITSS-IoE 2021. https://doi.org/10.1109/ITSS-IoE53029.2021.9615334
  11. Kurdi, H., & Thayananthan, V. (2021). Authentication mechanisms for IoT system based on distributed MQTT brokers: Review and challenges. Procedia Computer Science, 194, 132–139. https://doi.org/10.1016/j.procs.2021.10.066
  12. Li, J., Wu, J., Jiang, L., & Li, J. (2024). Blockchain-based public auditing with deep reinforcement learning for cloud storage. Expert Systems with Applications, 242. https://doi.org/10.1016/j.eswa.2023.122764
  13. Liu, D., Ding, Y., Yu, G., Zhong, Z., & Song, Y. (2024). Privacy-preserving dynamic auditing for regenerating code-based storage in cloud-fog-assisted IIoT. Internet of Things (Netherlands), 25. https://doi.org/10.1016/j.iot.2024.101084
  14. Liu, Q., Zhang, X., Xue, J., Zhou, R., Wang, X., & Tang, W. (2023). Enabling blockchain-assisted certificateless public integrity checking for industrial cloud storage systems. Journal of Systems Architecture, 140. https://doi.org/10.1016/j.sysarc.2023.102898
  15. Liu, X., Zhang, T., Hu, N., Zhang, P., & Zhang, Y. (2020). The method of Internet of Things access and network communication based on MQTT. Computer Communications, 153, 169–176. https://doi.org/10.1016/j.comcom.2020.01.044
  16. Longo, E., & Redondi, A. E. C. (2023). Design and implementation of an advanced MQTT broker for distributed pub/sub scenarios. Computer Networks, 224. https://doi.org/10.1016/j.comnet.2023.109601
  17. Mirampalli, S., Wankar, R., & Srirama, S. N. (2024). Evaluating NiFi and MQTT based serverless data pipelines in fog computing environments. Future Generation Computer Systems, 150, 341–353. https://doi.org/10.1016/j.future.2023.09.014
  18. Novak, M., Doležal, P., Budík, O., Ptáček, L., Geyer, J., Davídková, M., & Prokýšek, M. (2024). Intelligent inspection probe for monitoring bark beetle activities using embedded IoT real-time object detection. In Engineering Science and Technology, an International Journal (Vol. 51). Elsevier B.V. https://doi.org/10.1016/j.jestch.2024.101637
  19. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. http://arxiv.org/abs/1801.04381
  20. Verma, K., Charan, G. S., Pande, A., Abdalla, Y. A., Marshiana, D., & Choubey, C. K. (2023). Internet Regulated ESP32 Cam Robot. 2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA