Lightweight DWT Steganography with ECC-ChaCha20 for Secure Medical IoT Systems

The rapid integration of the Internet of Things (IoT) into healthcare systems has raised concerns about the confidentiality and integrity of patient data transmitted over these networks. Although traditional cryptographic solutions, such as Rivest-Shamir-Adleman (RSA) and Advanced Encryption Standard (AES), are secure, they often introduce significant computational and energy overheads that are unsuitable for constrained Internet of Things (IoT) devices. This paper proposes a lightweight stego-crypto framework that combines ChaCha20 encryption and Elliptic Curve Cryptography (ECC) for efficient key management with a 2D Discrete Wavelet Transform (DWT)-based steganographic technique. The proposed model embeds encrypted diagnostic text within medical images, ensuring secure and imperceptible transmission of sensitive information. Extensive evaluations on grayscale medical images demonstrate superior performance across various metrics, including Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Bit Error Rate (BER), Correlation, and system-level metrics such as CPU, memory, and power usage. Robustness against common image perturbations such as Gaussian noise, salt-and-pepper noise, JPEG compression, blurring, rotation, and translation is assessed using Bit Error Rate (BER) and payload correlation. Compared to RSA-AES and other conventional stego-crypto systems, our approach achieves a better trade-off between security, imperceptibility, and computational efficiency, making it highly suitable for real-time healthcare applications over IoT networks.