Performance analysis of video data transmission for telemedicine applications with 5G enabled Internet of Things.

• The authors proposed a novel KNN classifier-based H.265 protocol with a single buffer model in this paper. Multiple sensors are placed at the transmitter and receiver base stations to exchange data efficiently and accurately. • The data transmission performance is measured using collision error, propagation error, sensing error, and visual security with encryption. • The proposed novel MS-KNN classifier-based single buffer technique with H.265 protocol performs better than the existing LE-based approach. The Hidden Markov Model-based queuing technique with CD, VS, and SP estimates the sensor performance. • There is a significant improvement for the proposed MS-KNN-based single buffer method in the parameters of sensing error, propagation error, Busy receiver error, and collision error are 2%, 3%, 3%, and 5%, respectively, at 200 meters distance when compared to LE-single buffer technique. • Similarly, significant improvement in received packet ratio at 10 dBm, 20 dBm and 30 dBm is 4%, 2% and 2%, respectively. Hence, the proposed single buffer method performs better than the existing double buffer method in the case of sensory errors and received packet ratio at various levels. Fifth Generation (5G) communication access technology has been implemented to provide highly reliable and efficient video data streaming in telemedicine applications. The Internet of Things (IoT) advancement enhances the 5G network for smart healthcare services and applications. The existing research work focused only on Lagrangian Encoder (LE) based video compression technique with H.265 Protocol for video data transmission in 5G networks for telemedicine applications. This paper proposes a novel KNN classifier-based H.265 protocol with a single buffer model incorporated with multiple sensors for telemedicine applications. The proposed multiple sensors are placed at the transmitter and receiver base stations to exchange data efficiently and accurately between transmitter and receiver devices. The data transmission performance is measured using collision error, propagation error, sensing error, and visual security with encryption for the proposed and existing methods. The performance of the proposed model is compared with the existing LE-based single buffer and identifies the proposed KNN classifier-based single buffer with a multi-sensor technique that performs better. [Display omitted] A buffer with the sensor is placed at the receiver device to sense the data availability in the buffer using energy consumption level. If the energy consumption falls below the threshold level or half, the sensor sends the request notification to the Transmitter sensor. [ABSTRACT FROM AUTHOR]