Adaptive Bit-Plane Scrambling for Secure Image Transmission Using Entropy-Based Chaotic Permutations in IoT Networks
S. Saradha, Kavya N · 2025
The exponential growth of Internet of Things (IoT) devices has significantly increased the demand for effective and secure image encryption techniques to safeguard sensitive information during transmission. Traditional encryption methods, such as Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA), provide robust security but impose significant computational overhead, making them unsuitable for resource-constrained IoT environments. To address these limitations, this paper proposes an Adaptive Bit-Plane Scrambling (ABPS) technique that integrates entropy-based adaptation, chaotic map-based permutations, and key-dependent diffusion to enhance security and efficiency. The ABPS method selectively encrypts bit-planes based on their entropy levels, ensuring that higher bit-planes receive more intensive encryption. The use of the Henon map for generating chaotic sequences introduces a high degree of key sensitivity, thereby strengthening resistance to brute-force and differential attacks. Performance evaluation results demonstrate that the proposed ABPS method achieves an entropy of 7.99 bits, NPCR of 99.63 %, UACI of 50.07 %, and a PSNR of$361.20\ \text{dB}$, significantly outperforming traditional methods. The encryption and decryption times of ABPS, although higher than those of full-image scrambling and transform-based encryption, remain within acceptable limits for real-time applications. These results validate that ABPS offers a promising solution for secure and efficient image encryption in IoT networks and other resource-constrained environments.