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Optimized Visual Internet of Things for Video Streaming Enhancement in 5G Sensor Network Devices.

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Summary

This study introduces a new framework for Visual Internet of Things (VIoT) video transmission, enhancing Quality of Experience (QoE) by reducing packet loss and improving throughput using KNN-H.265 protocols.

Keywords:
5G networksVisual Internet of Thingsvideo compressionvideo streamingvisual sensor

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Networking

Background:

  • Visual Internet of Things (VIoT) deployment faces challenges like frame collusion and buffering delays due to packet loss and network congestion.
  • Existing research highlights the impact of packet loss on Quality of Experience (QoE) across various applications.

Purpose of the Study:

  • To propose a novel lossy video transmission framework for VIoT systems.
  • To address packet loss and network congestion issues in VIoT networking applications.
  • To enhance the Quality of Experience (QoE) in VIoT video communications.

Main Methods:

  • A new framework integrating the K-Nearest Neighbors (KNN) classifier with H.265 video compression protocols was developed.
  • The framework was tested for transmitting encrypted static images over wireless sensor networks under congested conditions.
  • Performance was evaluated using metrics like frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) in MATLAB 2018a.

Main Results:

  • The proposed KNN-H.265 protocol demonstrated superior performance compared to traditional H.264 and H.265 protocols.
  • The new framework achieved 4% and 6% higher Peak Signal to Noise Ratio (PSNR) values than existing methods.
  • Improved throughput was observed with the proposed VIoT video transmission framework.

Conclusions:

  • The proposed KNN-H.265 framework effectively mitigates packet drops in VIoT video conversations.
  • This approach offers a significant improvement in video transmission quality and efficiency for VIoT applications.
  • The study provides a viable solution for enhancing QoE in congested VIoT networks.