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Adaptive AI-enhanced computation offloading with machine learning for QoE optimization and energy-efficient mobile

Dinesh Kumar Nishad1, Vandna Rani Verma2, Pushkar Rajput2

  • 1Department of Electrical Engineering, Dr. Shakuntala Misra National Rehabilitation University, Lucknow, India. dineshnishad@rediffmail.com.

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Summary
This summary is machine-generated.

This study presents an Adaptive AI-enhanced offloading (AAEO) framework for Mobile Edge Computing (MEC) systems. The AAEO framework significantly improves Quality of Experience (QoE) and energy efficiency in dynamic multi-user environments.

Keywords:
Artificial intelligenceEnergy efficiencyMachine learningMobile edge computingQuality of experience

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Telecommunications Engineering

Background:

  • Mobile Edge Computing (MEC) systems require efficient computation offloading strategies.
  • Dynamic multi-user environments pose challenges for optimizing Quality of Experience (QoE) and energy efficiency.

Purpose of the Study:

  • To introduce a novel Adaptive AI-enhanced offloading (AAEO) framework for MEC systems.
  • To address the limitations of single-algorithm solutions in dynamic MEC environments.

Main Methods:

  • Integration of deep reinforcement learning, evolutionary algorithms, and federated learning.
  • Development of a hybrid architecture for dynamic offloading strategy adjustment.
  • Extensive simulations using MATLAB with varying numbers of mobile users and edge servers.

Main Results:

  • Achieved up to 35% improvement in QoE and 40% reduction in energy consumption.
  • Maintained stable task completion times with only a 12% increase under maximum user load.
  • Demonstrated a 98% threat detection rate with sub-100 ms response times and a 99.8% task completion rate.

Conclusions:

  • The proposed hybrid AI approach effectively addresses complex MEC challenges in heterogeneous environments.
  • The AAEO framework offers superior performance in QoE, energy efficiency, and reliability.
  • Real-time adaptation capabilities are crucial for next-generation MEC systems.