Multi-objective fuzzy-based RPL routing for enhanced QoS in IoMT video data transmission

• Published in: International Journal of Advanced Technology and Engineering Exploration Vol. 11; no. 116; p. 992
• Main Authors: Harisha, K S, Parameshachari, B D
• Format: Journal Article
• Language: English
• Published: Bhopal Accent Social and Welfare Society 01.07.2024
• Subjects: Data transmission; Effectiveness; Energy consumption; Internet of Things; IP (Internet Protocol); Multimedia; Performance evaluation; Performance measurement; Protocol; Quality of service architectures; Routing (telecommunications); Signal to noise ratio; Video broadcasting; Video data; Video transmission; Wireless sensor networks
• ISSN: 2394-5443; 2394-7454
• DOI: 10.19101/IJATEE.2023.10102528

The internet of multimedia things (IoMT) is an emerging field experiencing significant global growth and popularity. It supports the implementation of various multimedia content-based applications, notably wireless multimedia sensor networks (WMSNs). The standard internet protocol version 6 (IPv6) routing protocol for low-power and lossy networks (RPL) is utilized for broadcasting both scalar internet of things (IoT) and IoMT data traffic. This research introduced an efficient fuzzy-based routing strategy for IoMT applications, aimed at selecting the optimal routing path for video broadcasts. The study emphasizes the quality of experience (QoE) and quality of service (QoS) during video data transmission. The performance of the proposed multi-objective fuzzy-based routing is evaluated against existing approaches, focusing on energy consumption, delay, and throughput. The efficacy of the proposed RPL protocol, along with other state-of-the-art RPL protocols, is assessed across varying node counts. At 20 nodes, the proposed RPL protocol demonstrates superior performance, achieving an energy consumption of 0.089 mJ, a network throughput of 0.79 Kbps, and a delay of 0.090 seconds. Other performance metrics, such as the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR), are recorded at 97.23 and 35.45, respectively, highlighting the effectiveness of the proposed methodology.