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

Abstract

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.