Journal of ISMAC
IRO Journals
Volume 3 — Issue 3 — September 2021

Design of Deep Learning Algorithm for IoT Application by Image based Recognition

https://doi.org/10.36548/jismac.2021.3.008
Pages: 276-290
PDF
I Jeena Jacob
Department of Computer Science and Engineering, GITAM University
P Ebby Darney
Department of Electrical and Electronics Engineering, SCAD College of Engineering and Technology
PDF

How to Cite

Jacob, I Jeena, and P Ebby Darney. 2021. “Design of Deep Learning Algorithm for IoT Application by Image Based Recognition”. Journal of ISMAC 3 (3): 276-90. https://doi.org/10.36548/jismac.2021.3.008.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

— IoT
— deep learning algorithm
Published: 27-08-2021

Abstract

The Internet of Things (IoT) is an ecosystem comprised of multiple devices and connections, a large number of users, and a massive amount of data. Deep learning is especially suited for these scenarios due to its appropriateness for "big data" difficulties and future concerns. Nonetheless, guaranteeing security and privacy has emerged as a critical challenge for IoT administration. In many recent cases, deep learning algorithms have proven to be increasingly efficient in performing security assessments for IoT devices without resorting to handcrafted rules. This research work integrates principal component analysis (PCA) for feature extraction with superior performance. Besides, the primary objective of this research work is to gather a comprehensive survey data on the types of IoT deployments, along with security and privacy challenges with good recognition rate. The deep learning method is performed through PCA feature extraction for improving the accuracy of the process. Our other primary goal in this study paper is to achieve a high recognition rate for IoT based image recognition. The CNN approach was trained and evaluated on the IoT image dataset for performance evaluation using multiple methodologies. The initial step would be to investigate the application of deep learning for IoT image acquisition. Additionally, when it comes to IoT image registering, the usefulness of the deep learning method has been evaluated for increasing the appropriateness of image recognition with good testing accuracy. The research discoveries on the application of deep learning in the Internet of Things (IoT) system are summarized in an image-based identification method that introduces a variety of appropriate criteria.

References

  1. Deng, Z., Cao, Y., Zhou, X., Yi, Y., & You, I. (2020). Toward efficient image recognition in sensor-based iot: a weight initialization optimizing method for cnn based on rgb influence proportion. Sensors, 20(10), 2866.
  2. Patil, Prachu J., Ritika V. Zalke, Kalyani R. Tumasare, Bhavana A. Shiwankar, Shivani R. Singh, and Shailesh Sakhare. "IoT Protocol for Accident Spotting with Medical Facility." Journal of Artificial Intelligence 3, no. 02 (2021): 140-150.
  3. Hexiang, L. I. (2017). Design of iot vision based image automatic identification system for tourist attraction abnormalities. Modern Electronics Technique, 40(4), 124-131.
  4. Chen, Joy Iong-Zong, and Kong-Long Lai. "Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert." Journal of Artificial Intelligence 3, no. 02 (2021): 101-112.
  5. Santos, P. C., De Lima, J. P. C., De Moura, R. F., Ahmed, H., Alves, M. A. Z., & Beck, A. C. S., et al. (2019). A technologically agnostic framework for cyber-physical and iot processing-in-memory-based systems simulation. Microprocessors and microsystems, 69(SEP.), 101-111.
  6. Haoxiang, Wang, and S. Smys. "Big Data Analysis and Perturbation using Data Mining Algorithm." Journal of Soft Computing Paradigm (JSCP) 3, no. 01 (2021): 19-28.
  7. Zhong, F., Chen, Z., Ning, Z., Min, G., & Hu, Y. (2018). Heterogeneous visual features integration for image recognition optimization in internet of things. Journal of computational ence, 28(SEP.), 466-475.
  8. Bashar, Abul. "AGRICULTURAL MACHINE AUTOMATION USING IOT THROUGH ANDROID." Journal of Electrical Engineering and Automation 1, no. 2 (2019): 83-92.
  9. Yang, A., Zhang, C., Chen, Y., Zhuansun, Y., & Liu, H. (2020). Security and privacy of smart home systems based on the internet of things and stereo matching algorithms. IEEE Internet of Things Journal, 7(4), 2521-2530.
  10. Chen, Dr Joy Iong Zong, and Kong-Long Lai. "Internet of Things (IoT) Authentication and Access Control by Hybrid Deep Learning Method-A Study." Journal of Soft Computing Paradigm (JSCP) 2, no. 04 (2020): 236-245.
  11. Leroux, S., Bohez, S., De Coninck, E., Van Molle, P., Vankeirsbilck, B., & Verbelen, T., et al. (2019). Multi-fidelity deep neural networks for adaptive inference in the internet of multimedia things. Future generation computer systems, 97(AUG.), 355-360.
  12. Smys, S., and Haoxiang Wang. "Security Enhancement in Smart Vehicle Using Blockchain-based Architectural Framework." Journal of Artificial Intelligence 3, no. 02 (2021): 90-100.
  13. Liu, D., Yang, C., Li, S., Chen, X., Ren, J., & Liu, R., et al. (2019). Fitcnn: a cloud-assisted and low-cost framework for updating cnns on iot devices. Future generation computer systems, 91(FEB.), 277-289.
  14. Hamdan, Yasir Babiker. "Construction of Statistical SVM based Recognition Model for Handwritten Character Recognition." Journal of Information Technology 3, no. 02 (2021): 92-107.
  15. Lu, Y., Wu, S., Fang, Z., Xiong, N., Yoon, S., & Park, D. S. (2017). Exploring finger vein based personal authentication for secure iot. Future Generation Computer Systems, 77(dec.), 149-160.
  16. Sungheetha, Akey, and Rajesh Sharma. "Design an Early Detection and Classification for Diabetic Retinopathy by Deep Feature Extraction based Convolution Neural Network." Journal of Trends in Computer Science and Smart technology (TCSST) 3, no. 02 (2021): 81-94.
  17. R. Minerva, A. Biru, and D. Rotondi, “Towards a definition of the internet of things (IoT),” IEEE Internet Initiative, vol. 1, no. 1, pp. 1–86, 2015.
  18. L. Bondi, L. Baroffio, and D. Guera, “Etc. first steps toward camera model identification with convolutional neural networks,” IEEE Signal Processing Letters, vol. 24, no. 3, pp. 259–263, 2016.
  19. S. Qi, Z. Huang, and Y. Li, “Etc. audio recording device identification based on deep learning,” in Proceedings of the 2016 IEEE International Conference on Signal and Image Processing (ICSIP), pp. 426–431, IEEE, Singapore, 2016.
  20. J. Yu, A. Hu, and F. Zhou, “Etc. Radio frequency fingerprint identification based on denoising autoencoders,” in Proceedings of the 2019 international conference on wireless and mobile computing, networking and communications (WiMob), pp. 1–6, IEEE, Barcelona, Spain, 2019.
  21. J. Bassey, D. Adesina, and X. Li, “Etc. Intrusion detection for IoT devices based on RF fingerprinting using deep learning,” in Proceedings of the 2019 fourth international conference on fog and mobile edge computing (FMEC), pp. 98–104, IEEE, Rome, Italy, 2019.
  22. H. Gao, W. Huang, and Y. Duan, “+e cloud-edge based dynamic reconfiguration to service workflow for mobile ecommerce environments: a QoS prediction perspective,” ACM Transactions on Internet Technology, 2020.
  23. R. Shire, S. Shiaeles, and K. Bendiab, “Etc. malware squid: a novel iot malware traffic analysis framework using convolutional neural network and binary visualisation,” Smart Spaces, and Next Generation Networks and Systems, p. 6576, Springer, Cham, Switzerland, 2019.
  24. Y. Meidan, M. Bohadana, Y. Mathov et al., “N-BaIoT-Network-Based detection of IoT botnet attacks using deep autoencoders,” IEEE Pervasive Computing, vol. 17, no. 3, pp. 12–22, 2018.
  25. Hsia, C. H., Yen, S. C., & Jang, J. H. (2019). An intelligent iot-based vision system for nighttime vehicle detection and energy saving. Sensors & Materials, 31(6(1)), 1803-1814.
  26. Tripathi, Milan. "Analysis of Convolutional Neural Network based Image Classification Techniques." Journal of Innovative Image Processing (JIIP) 3, no. 02 (2021): 100-117.
  27. Rao, Anushree Janardhan, Chaithra Bekal, Y. R. Manoj, R. Rakshitha, and N. Poornima. "Automatic Detection of Crop Diseases and Smart Irrigation Using IoT and Image Processing." In Innovative Data Communication Technologies and Application, pp. 363-374. Springer, Singapore, 2021.
  28. Durairaj, M., and J. Hirudhaya Mary Asha. "The Internet of Things (IoT) Routing Security—A Study." In International Conference on Communication, Computing and Electronics Systems, pp. 603-612. Springer, Singapore, 2020.
  29. Shanmugapriya, T., K. Kousalya, J. Rajeshkumar, and M. Nandhini. "Wireless Sensor Networks Security Issues, Attacks and Challenges: A Survey." In International conference on Computer Networks, Big data and IoT, pp. 1-12. Springer, Cham, 2019.
  30. Yadav, Vicky, and Rejo Mathew. "Analysis and Review of Cloud Based Encryption Methods." In International conference on Computer Networks, Big data and IoT, pp. 169-176. Springer, Cham, 2019.
  31. Paul, Vedant, and Rejo Mathew. "Data Storage Security Issues in Cloud Computing." In International conference on Computer Networks, Big data and IoT, pp. 177-187. Springer, Cham, 2019.
  32. Rathore, Tapendra Singh, and Rejo Mathew. "Data Storage Security Issues and Solutions in Cloud Computing." In International conference on Computer Networks, Big data and IoT, pp. 205-212. Springer, Cham, 2019.
  33. Haoxiang, Wang, and S. Smys. "Overview of Configuring Adaptive Activation Functions for Deep Neural Networks-A Comparative Study." Journal of Ubiquitous Computing and Communication Technologies (UCCT) 3, no. 01 (2021): 10-22.