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An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things.

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This study introduces an AIoT and SDN model for mobile edges, optimizing multimedia sensor networks. The solution enhances data delivery and reduces delays for real-time applications.

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Network edges and IoT are crucial for scalable multimedia applications.
  • Existing solutions struggle with high multimedia traffic, dynamic conditions, and bandwidth constraints.
  • Current methods often increase data loss and delivery delay in uncertain network environments.

Purpose of the Study:

  • To present an optimization model for mobile edges in multimedia sensor networks using AIoT.
  • To enhance real-time data collection efficiency and reduce resource consumption.
  • To improve network communication predictability by integrating Software-Defined Networking (SDN) and mobile edges.

Main Methods:

  • Utilized Artificial Intelligence of Things (AIoT) for multi-hop networking and resource management.
  • Implemented SDN for direct association with mobile edges, enabling load balancing and centralized management.
  • Ensured secure multimedia traffic transmission through subkey sharing.

Main Results:

  • Achieved an average 35% improvement in delivery rate.
  • Reduced processing delay by an average of 29%.
  • Decreased network overheads by 41%, packet drop ratio by 39%, and packet retransmission by 34% compared to existing solutions.

Conclusions:

  • The proposed AIoT and SDN model effectively optimizes mobile edges for multimedia sensor networks.
  • The solution significantly enhances network performance metrics, including delivery rate and delay.
  • Demonstrated a robust approach for managing high multimedia traffic under dynamic network conditions.