We have carefully addressed the reviewers’ suggestions by enhancing the security analysis of the PQCAIE scheme, optimizing the authentication protocol for better efficiency in IoT environments, and clarifying the implementation details with additional experimental results. These changes improve the robustness and practical applicability of the proposed solution in e-health systems.
We have carefully addressed the reviewers’ suggestions by enhancing the security analysis of the PQCAIE scheme, optimizing the authentication protocol for better efficiency in IoT environments, and clarifying the implementation details with additional experimental results. These changes improve the robustness and practical applicability of the proposed solution in e-health systems.
We have carefully addressed the reviewers’ suggestions by enhancing the security analysis of the PQCAIE scheme, optimizing the authentication protocol for better efficiency in IoT environments, and clarifying the implementation details with additional experimental results. These changes improve the robustness and practical applicability of the proposed solution in e-health systems.
Thank you for your valuable feedback and the opportunity to submit our work. We understand the concerns raised and will use the suggestions to significantly improve our research. We hope to address these issues thoroughly and consider resubmission in the future.
We have carefully addressed the reviewers’ suggestions by enhancing the security analysis of the PQCAIE scheme, optimizing the authentication protocol for better efficiency in IoT environments, and clarifying the implementation details with additional experimental results. These changes improve the robustness and practical applicability of the proposed solution in e-health systems.
TinyWolf — Efficient On-device TinyML Training for IoT using Enhanced Grey Wolf Optimization presents a lightweight, adaptive training framework tailored for resource-constrained IoT devices. By enhancing Grey Wolf Optimization, we achieve faster convergence and improved model accuracy directly on edge devices.
Thank you for your valuable feedback and the opportunity to submit our work. We understand the concerns raised and will use the suggestions to significantly improve our research. We hope to address these issues thoroughly and consider resubmission in the future.
We have carefully addressed the reviewers’ suggestions by enhancing the security analysis of the PQCAIE scheme, optimizing the authentication protocol for better efficiency in IoT environments, and clarifying the implementation details with additional experimental results. These changes improve the robustness and practical applicability of the proposed solution in e-health systems.