Ghazal, T M, Islam, Shayla, Budati, Anil Kumar, Safie, Nurhizam, Hasan, Mohammad Kamrul and Bahar, Nurhidayah (2023) Optimized Visual Internet of Things for Video Streaming Enhancement in 5G Sensor Network Devices. Sensors, 23 (11). p. 5072. ISSN 1424-8220
23/11/5072 - Published Version
Download (329kB)
Abstract
The global expansion of the Visual Internet of Things (VIoT)’s deployment with multiple devices and sensor interconnections has been widespread. Frame collusion and buffering delays are the primary artifacts in the broad area of VIoT networking applications due to significant packet loss and network congestion. Numerous studies have been carried out on the impact of packet loss on Quality of Experience (QoE) for a wide range of applications. In this paper, a lossy video transmission framework for the VIoT considering the KNN classifier merged with the H.265 protocols. The performance of the proposed framework was assessed while considering the congestion of encrypted static images transmitted to the wireless sensor networks. The performance analysis of the proposed KNN-H.265 protocol is compared with the existing traditional H.265 and H.264 protocols. The analysis suggests that the traditional H.264 and H.265 protocols cause video conversation packet drops. The performance of the proposed protocol is estimated with the parameters of frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) on MATLAB 2018a simulation software. The proposed model gives 4% and 6% better PSNR values than the existing two methods and better throughput.
Affiliation: | Skyline University College |
---|---|
SUC Author(s): | Ghazal, T M ORCID: https://orcid.org/0000-0003-0672-7924 |
All Author(s): | Ghazal, T M, Islam, Shayla, Budati, Anil Kumar, Safie, Nurhizam, Hasan, Mohammad Kamrul and Bahar, Nurhidayah |
Item Type: | Article |
Uncontrolled Keywords: | Visual Internet of Things; visual sensor; video streaming; video compression; 5G networks |
Subjects: | B Information Technology > BP Internet of Things B Information Technology > BW Computer Networks |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Mosys Team |
Date Deposited: | 25 Dec 2023 13:43 |
Last Modified: | 25 Dec 2023 13:43 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/716 |
Publisher URL: | https://doi.org/10.3390/s23115072 |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/17524 |
Related URLs: |
|
Actions (login required)
Statistics for this ePrint Item |