A Next Generation Real Time Frame work for Drone Video Decoding Leveraging IoT-Enabled Communication Network

Authors

  • Mohammad Ibrar-Ul Haque Department of Information & Systems Engineering Sir Syed University of Engineering & Technology Karachi, Pakistan https://orcid.org/0000-0002-0877-7805
  • Maria Fatima Department of Electronic Engineering Sir Syed University of Engineering & Technology Karachi, Pakistan
  • Faiza Waqas Department of Electrical Engineering Sir Syed University of Engineering & Technology Karachi, Pakistan https://orcid.org/0009-0003-7324-4858
  • Sidra Fatima Department of Computer Engineering Sir Syed University of Engineering & Technology Karachi, Pakistan https://orcid.org/0000-0001-8533-2343
  • Mohu-ud-din Bukhari Department of Information & Systems Engineering Concordia University Material, Canada
  • Manzar Ahmed Department of Electrical Engineering Sir Syed University of Engineering & Technology Karachi, Pakistan https://orcid.org/0000-0002-4524-3060

DOI:

https://doi.org/10.21015/vtcs.v13i2.2266

Abstract

The new data processing systems required reliable. Fast and low latency data services for smart cities operations and unmanned vehicle systems for quick and fast decisions. At present the low latency and speed data for video decoding for real time are required for intelligent decisions. In this research work we present video decoding model based on for decoding data in real time. This proposed model is based on hybrid Edge-Fog-Cloud orchestration layer that perform decoding task in real time according to the network congestion and this technique ensure data integrity, traceable task distribution and protect the data from tempering by using IoT backbone secured by blockchain technology. To reduce the risk of end-to-end latency and packet loss in worst conditions a novel Temporal-Spatial Predictive Decoding (TSPD) method is used. The AI model deep reinforcement learning is used for fast decisions. After analyzing it can be concluded that a 47.8% improvement in decoding throughput, a 62% reduction in jitter and 38% improvement QoE. This shows satisfactory performance from proposed model. By optimizing energy-latency and combining decentralized system with IoT-driven communication for autonomous aerial system can be used in future 6G network.

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Published

2025-12-21

How to Cite

Haque, M. I.-U., Fatima , M., Waqas, F., Fatima , S., Bukhari , M.- ud- din, & Ahmed, M. (2025). A Next Generation Real Time Frame work for Drone Video Decoding Leveraging IoT-Enabled Communication Network. VAWKUM Transactions on Computer Sciences, 13(2), 205–219. https://doi.org/10.21015/vtcs.v13i2.2266

Issue

Vol. 13 No. 2 (2025): July-December

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Articles

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