The Internet of Medical Things (IoMT) increasingly relies on continuous sensing and cloud-assisted diagnostics, yet its devices operate under stringent resource constraints that limit the deployment of conventional cryptographic mechanisms. To address this challenge, we propose a lightweight encryption framework that integrates the National Institute of Standards and Technology (NIST)-standardized ASCON-v1.2 authenticated encryption scheme with two optimization strategies: Hypercube Optimal Search (HOS) and Modified Wild Geese (MWG). The framework systematically explores ASCON’s implementation-level parameter space and identifies configurations that preserve security margins while reducing computational cost, latency, and energy consumption on constrained IoMT hardware. HOS performs multidimensional search and contraction over feasible parameter regions, whereas MWG provides adaptive local refinement to optimise performance under varying device conditions. An experimental evaluation of publicly available medical image datasets demonstrates that the proposed HOS + MWG + ASCON pipeline enhances diffusion and randomness characteristics while reducing execution time compared to baseline ASCON configurations. Quantitative assessments using Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), entropy, correlation coefficients (CC), and Bit Error Rate (BER) confirm the robustness of the system against statistical, differential, and structural inference attacks. Hardware tests on an ESP32-S3 platform further validate the framework’s energy efficiency and reliability under realistic IoMT constraints. Overall, this work presents a scalable, computationally efficient, and security-preserving encryption solution tailored for real-time IoMT deployments, where confidentiality, latency, and energy efficiency are all critical.
Rasheed et al. (2026) studied this question.