Medical Internet of Things (MIoT) enables real-time interaction among medical devices, intelligent healthcare systems, and patient monitoring applications. Medical video transmission is particularly important for telemedicine, remote diagnosis, and surgical assistance, where both data security and transmission efficiency are critical. However, existing approaches primarily focus on either strengthening encryption mechanisms or improving network performance independently, often resulting in increased computational overhead, latency, and reduced adaptability in dynamic MIoT environments. To address these limitations, this paper proposes a novel secure medical video transmission framework, termed Video Transmission using Lemur-Optimized Chaotic Key Encryption (VIDEO-LOCK). To address these challenges, this paper proposes a novel secure VIDEO transmission using a Lemur-optimized logistic Chaotic Key encryption (VIDEO-LOCK) approach. Medical videos are acquired from ultrasound devices and converted into images using Apache Spark, ensuring fast, high-quality transmission. A Lemur-optimized logistic Chaotic Key (LOCK) method is proposed for encrypting images, where the process of key generation is facilitated by Lemur optimization. A hybrid Deep Q-Network and Extreme Learning Machine (DQEL) algorithm is used to improve Quality of Service (QoS) through the selection of the optimal transmission path. Experimental results demonstrate that VIDEO-LOCK achieves superior security, transmission efficiency, and computational performance compared with existing methods. The proposed framework reduces encryption time by 2.75%, 4.25%, and 9.50% compared with CloudSec, EiMOL, and DLEDNet, respectively.
Kalidoss et al. (Fri,) studied this question.