2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 18 , Issue 1 , PP: 66-75, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Salwa Mohammed Nejrs 1 , Azmi Shawkat Abdulbaqi 2
Doi: https://doi.org/10.54216/FPA.180106
The collected information from the environment in WSN continuously sends from one node to another until it reaches the main collector or server, where processing is done. The transferred data volume will be greater when the network grows. Medical images will also contribute to network traffic. To alleviate this challenge, this research has developed an interlayer transmission protocol for WSNs. This protocol uses the construction of medical images with pixel-based data. In the analysis, a gray-scale medical image 512x512 in size, provided by Brain, is utilized. The image was compressed by the protocol from 256 KB to 192 KB with a percentage of 25%. As a result, the structural similarity index measure showed the SSIM at 51.1365, while the PSNR is at 0.9976; therefore, the quality of the medical image remains unchanged. The protocol uses the AES encryption method for strong data protection to improve security during transmission. Results show that this protocol reduces data transmission in WSNs by 12.5 to 25% without affecting the integrity of the medical image, which is indicative of the efficiency of the protocol in enhancing network performance while ensuring data safety.
Cover Image (Cov_Img) Discrete Cosine Transformation (DCT) , Practical Linear Algebra Technique (PLAT) , Normalized Cross-Correlation (NCC) Discrete Wavelet Transformation (DWT)
M. R. Jomaa, H. Mathkour, Y. Bazi, and M. S. Islam, "End-to-end deep learning fusion of fingerprint and electrocardiogram signals for presentation attack detection," Sensors, vol. 20, p. 2085, 2020. DOI: https://doi.org/10.3390/s20072085.
2. D. Yaman, F. I. Eyiokur, and H. K. Ekenel, "Multimodal soft biometrics: combining ear and face biometrics for age and gender classification," Multimedia Tools and Applications, vol. 81, pp. 22695–22713, 2022. DOI: https://doi.org/10.1007/s11042-021-10630-8.
3. M. A. M. El-Bendary, H. Kasban, A. Haggag, et al., "Investigating nodes and personal authentications utilizing multimodal biometrics for medical application of WBANs security," Multimedia Tools and Applications, vol. 79, pp. 24507–24535, 2020. DOI: https://doi.org/10.1007/s11042-020-08926-2.
4. M. Singhal and K. Shinghal, "Recent advances in online signature verification and face recognition," Test Engineering and Management, vol. 82, pp. 11371–11377, 2020.
5. S. B. Jadhav, N. K. Deshmukh, and V. T. Humbe, "HDL-PI: hybrid deep learning technique for person identification using multimodal fingerprint, iris, and face biometric features," Multimedia Tools and Applications, 2022. DOI: https://doi.org/10.1007/s11042-022-14241-9.
6. S. R. Saeed, et al., "The study of deep learning for automotive logo recognition and classification," Periodicals of Engineering and Natural Sciences (PEN), vol. 11, no. 3, p. 255, 2023. DOI: https://doi.org/10.21533/pen.v11i3.3641.
7. V. C. Kagawade and S. A. Angadi, "VISA: a multimodal database of face and iris traits," Multimedia Tools and Applications, vol. 80, pp. 21615–21650, 2021. DOI: https://doi.org/10.1007/s11042-021-10650-4.
8. A. Arora and R. Miri, "Cryptography and Tay-Grey wolf optimization-based multimodal biometrics for effective security," Multimedia Tools and Applications, vol. 81, pp. 44021–44043, 2022. DOI: https://doi.org/10.1007/s11042-022-11993-2.
9. S. Chandra and V. Kumar, "A novel approach to validate online signatures using dynamic features based on locally weighted learning," Multimedia Tools and Applications, vol. 81, pp. 40959–40976, 2022. DOI: https://doi.org/10.1007/s11042-022-13159-6.
10. K. Bibi, S. Naz, and A. Rehman, "Biometric signature authentication using machine learning techniques: Current trends, challenges, and opportunities," Multimedia Tools and Applications, vol. 79, pp. 289–340, 2020. DOI: https://doi.org/10.1007/s11042-019-08022-0.
11. T. Dhieb, H. Boubaker, S. Njah, et al., "A novel biometric system for signature verification based on score level fusion approach," Multimedia Tools and Applications, vol. 81, pp. 7817–7845, 2022. DOI: https://doi.org/10.1007/s11042-022-12140-7.
12. M. Leghari, S. Memon, L. D. Dhomeja, A. H. Jalbani, and A. A. Chandio, "Deep feature fusion of fingerprint and online signature for multimodal biometrics," Computers, vol. 10, p. 21, 2021.
13. P. S. Chanukya and T. K. Thivakaran, "Multimodal biometric cryptosystem for human authentication using fingerprint and ear," Multimedia Tools and Applications, vol. 79, pp. 659–673, 2020.
14. S. Minaee, A. Abdolrashidi, H. Su, M. Bennamoun, and D. Zhang, "Biometric recognition using deep learning: A survey," arXiv, arXiv: 1912.00271, 2019.
15. W. Kong, D. Zhang, and W. Li, "Palmprint feature extraction using 2-D Gabor filters," Pattern Recognition, vol. 36, no. 10, pp. 2339–2347, 2003.
16. W. Kong and D. Zhang, "Competitive coding scheme for palmprint verification," Proceedings of the 17th International Conference on Pattern Recognition (ICPR), 2004.
17. —, "Sequential Modified Haar Wavelet Energy," 2008 International Conference on Signal Processing, Communications and Networking, 2008.
18. L. Zhang, L. Zhang, D. Zhang, and H. Zhu, "Online finger-knuckle-print verification for personal authentication," Pattern Recognition, vol. 43, no. 7, pp. 2560–2571, 2010.
19. L. Zhang and D. Zhang, "Characterization of palmprints by wavelet signatures via directional context modeling," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 34, no. 3, pp. 1335–1347, 2004.
20. P. Recobos Rodriguez and J. Landa Silva, "Biometric identification by dermatoglyphics," Proceedings of the 3rd IEEE International Conference on Image Processing.
21. I. Fatima, N. K. Mehra, and N. K. Nishchal, "Optical image encryption using equal modulus decomposition and multiple diffractive imaging," Journal of Optics, vol. 18, no. 8, p. 085701, 2016.
22. A. S. Abdulbaqi, A. M. Salman, and S. B. Tambe, "Privacy-preserving data mining techniques in big data: Balancing security and usability," SHIFRA, pp. 1–10, 2023. DOI: https://doi.org/10.70470/SHIFRA/2023/001.
23. I. H. Abdalla and R. F. Yaser, "WSN recruitments for encrypted medical image transmission securely," Journal of Discrete Mathematical Sciences and Cryptography, vol. 26, no. 7, pp. 1981–1990, 2023. DOI: https://doi.org/10.47974/jdmsc-1838.
24. F. H. Al-Rubbiay, A. Y. Youssef, and S. D. Mahmood, "Medical image authentication and restoration based on mCloud computing: Towards reliant medical digitization era," in Doctoral Symposium on Computational Intelligence, Singapore: Springer, 2023, pp. 487–500.
25. S. D. Mahmood, F. Drira, H. F. Mahdi, Y. Aribi, and A. M. Alimi, "Chaotic model-based blind watermarking with LSB technique for digital fundus image authentication," in 2023 International Conference on Cyberworlds (CW), IEEE, 2023, pp. 395–402.
