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Integrating Deep Learning-Based IoT and Fog Computing with Software-Defined Networking for Detecting Weapons in Video

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This study integrates deep learning with Software-Defined Networking (SDN) to enhance Quality of Service (QoS) for delay-sensitive Internet of Things (IoT) applications. The system improves network performance by prioritizing critical data like weapon detection, reducing bandwidth usage.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Engineering

Background:

  • The proliferation of multimedia traffic from Internet of Things (IoT) and remote applications necessitates improved Quality of Service (QoS).
  • Existing network architectures struggle to adapt to the dynamic demands of delay-sensitive applications, particularly in real-time surveillance.
  • The COVID-19 pandemic accelerated the need for efficient remote multimedia solutions, highlighting network limitations.

Purpose of the Study:

  • To investigate the integration of deep learning (DL) techniques with Software-Defined Network (SDN) architecture.
  • To enhance QoS for delay-sensitive applications within Internet of Things (IoT) environments.
  • To develop an intelligent edge-based system for real-time weapon detection and efficient data transmission.

Main Methods:

  • Trained and evaluated multiple DL models for weapon detection in real-time video surveillance.
  • Deployed the highest-performing DL model within an AI model at the edge for frame extraction.
  • Utilized SDN for dynamic network programming to provide differentiated QoS based on traffic type, load, and destination.
  • Conducted performance evaluation using the mininet emulator.

Main Results:

  • Achieved significant performance improvements in delay, throughput, and bandwidth requirements.
  • Demonstrated up to 75.0% improvement in average throughput.
  • Showcased up to 14.7% improvement in mean jitter and up to 32.5% reduction in packet loss.
  • Successfully reduced bandwidth requirements by offloading non-critical traffic.

Conclusions:

  • The integration of DL and SDN effectively supports delay-sensitive applications in IoT environments.
  • The proposed edge AI model enables rapid threat detection and reduces network load.
  • Dynamic network programming based on traffic characteristics optimizes QoS and network resource utilization.