International Journal of Reciprocal Symmetry and Theoretical Physics

Peer Reviewed Article

Vol. 7 (2020)

Applying Convolutional Neural Networks for IoT Image Recognition

Submitted
1 November 2024
Published
15-05-2020

Abstract

This research uses edge computing to overcome real-time processing issues in Internet of Things (IoT) picture identification using Convolutional Neural Networks (CNNs). The main goals are to study lightweight CNN architectures, model compression, and edge computing's impact on latency and bandwidth. The article reviews CNN literature and its use in resource-constrained IoT situations using secondary data. Significant results show that lightweight models like MobileNet and EfficientNet are essential for efficient picture identification without losing accuracy, while edge computing decreases latency and improves real-time decision-making. Hardware accelerators help install complicated CNN models on IoT devices. Limitations include edge infrastructure reliance and data privacy issues. The report stresses the need for solid data protection regulations and energy-efficient hardware research. Policymakers should establish IoT data protection standards and accessible edge infrastructure to ensure fair deployment of CNN-based image recognition systems across varied geographies. This study helps explain CNN-IoT synergy, laying the groundwork for real-time image processing advances.

References

  1. Chowdhury, A. P., Kulkarni, P., Bojnordi, M. N. (2018). MB-CNN: Memristive Binary Convolutional Neural Networks for Embedded Mobile Devices. Journal of Low Power Electronics and Applications, 8(4), 38. https://doi.org/10.3390/jlpea8040038
  2. Ge, F., Wu, N., Xiao, H., Zhang, Y., Zhou, F. (2019). Compact Convolutional Neural Network Accelerator for IoT Endpoint SoC. Electronics, 8(5), 497. https://doi.org/10.3390/electronics8050497
  3. Han, Y., Wang, W. (2019). An End-to-End Deep Learning Image Compression Framework Based on Semantic Analysis. Applied Sciences, 9(17), 3580. https://doi.org/10.3390/app9173580
  4. Karanam, R. K., Natakam, V. M., Boinapalli, N. R., Sridharlakshmi, N. R. B., Allam, A. R., Gade, P. K., Venkata, S. G. N., Kommineni, H. P., & Manikyala, A. (2018). Neural Networks in Algorithmic Trading for Financial Markets. Asian Accounting and Auditing Advancement, 9(1), 115–126. https://4ajournal.com/article/view/95
  5. Kothapalli, S., Manikyala, A., Kommineni, H. P., Venkata, S. G. N., Gade, P. K., Allam, A. R., Sridharlakshmi, N. R. B., Boinapalli, N. R., Onteddu, A. R., & Kundavaram, R. R. (2019). Code Refactoring Strategies for DevOps: Improving Software Maintainability and Scalability. ABC Research Alert, 7(3), 193–204. https://doi.org/10.18034/ra.v7i3.663
  6. Meng, B., Liu, X., Wang, X. (2018). Human Action Recognition Based on Quaternion Spatial-temporal Convolutional Neural Network and LSTM in RGB Videos. Multimedia Tools and Applications, 77(20), 26901-26918. https://doi.org/10.1007/s11042-018-5893-9
  7. Mohammed, R., Addimulam, S., Mohammed, M. A., Karanam, R. K., Maddula, S. S., Pasam, P., & Natakam, V. M. (2017). Optimizing Web Performance: Front End Development Strategies for the Aviation Sector. International Journal of Reciprocal Symmetry and Theoretical Physics, 4, 38-45. https://upright.pub/index.php/ijrstp/article/view/142
  8. Nizamuddin, M., Natakam, V. M., Sachani, D. K., Vennapusa, S. C. R., Addimulam, S., & Mullangi, K. (2019). The Paradox of Retail Automation: How Self-Checkout Convenience Contrasts with Loyalty to Human Cashiers. Asian Journal of Humanity, Art and Literature, 6(2), 219-232. https://doi.org/10.18034/ajhal.v6i2.751
  9. Njima, W., Ahriz, I., Zayani, R., Terre, M., Bouallegue, R. (2019). Deep CNN for Indoor Localization in IoT-Sensor Systems. Sensors, 19(14). https://doi.org/10.3390/s19143127
  10. Rahman, K. (2017). Digital Platforms in Learning and Assessment: The Coming of Age of Artificial Intelligence in Medical Checkup. International Journal of Reciprocal Symmetry and Theoretical Physics, 4, 1-5. https://upright.pub/index.php/ijrstp/article/view/3
  11. Rodriguez, M., Mohammed, M. A., Mohammed, R., Pasam, P., Karanam, R. K., Vennapusa, S. C. R., & Boinapalli, N. R. (2019). Oracle EBS and Digital Transformation: Aligning Technology with Business Goals. Technology & Management Review, 4, 49-63. https://upright.pub/index.php/tmr/article/view/151
  12. Shin, M., Paik, W., Kim, B., Hwang, S. (2019). An IoT Platform with Monitoring Robot Applying CNN-Based Context-Aware Learning. Sensors, 19(11). https://doi.org/10.3390/s19112525
  13. Sowmya, B. J., Srinivasa, K. G., Kumar, D., Shetty, C. (2016). Efficient Image Denoising for Effective Digitization using Image Processing Techniques and Neural Networks. International Journal of Applied Evolutionary Computation, 7(4), 77-93. https://doi.org/10.4018/IJAEC.2016100105
  14. Takahashi, A., Hayakawa, Y., Oonuma, T., Kobayashi, H., Chiba, S. (2017). Feature Extraction of Video Using Artificial Neural Network. International Journal of Cognitive Informatics & Natural Intelligence, 11(2), 25-40. https://doi.org/10.4018/IJCINI.2017040102
  15. Thompson, C. R., Talla, R. R., Gummadi, J. C. S., Kamisetty, A (2019). Reinforcement Learning Techniques for Autonomous Robotics. Asian Journal of Applied Science and Engineering, 8(1), 85-96. https://ajase.net/article/view/94
  16. Weng, Y., Xia, C. (2019). A New Deep Learning-Based Handwritten Character Recognition System on Mobile Computing Devices. Mobile Networks and Applications, 1-10. https://doi.org/10.1007/s11036-019-01243-5
  17. Zou, D., Feng, L., Feng, S., Fu, P., Li, J. (2019). An FPGA-Based CNN Accelerator Integrating Depthwise Separable Convolution. Electronics, 8(3), 281. https://doi.org/10.3390/electronics8030281

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