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Adaptive and intelligent customized deep Q-network for energy-efficient task offloading in mobile edge computing

J Anand1, B Karthikeyan2

  • 1School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.

Scientific Reports
|February 7, 2026
PubMed
Summary

A new AI framework, AICDQN, optimizes task offloading in edge-cloud systems. It reduces delay and task drops while improving energy efficiency for latency-sensitive IoT applications.

Keywords:
Deep reinforcement learning (DRL)Edge-cloud computingEnergy-efficient resource managementGRU-LSTM predictionQueue-aware schedulingTask offloading

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Edge-cloud computing is expanding, increasing demands for efficient task offloading.
  • Latency-sensitive Internet of Things (IoT) applications require intelligent scheduling in dynamic environments.

Purpose of the Study:

  • To introduce a novel reinforcement learning framework, Adaptive and Intelligent Customized Deep Q-Network (AICDQN), for priority-aware task scheduling.
  • To enhance real-time decision-making in mobile edge computing systems.

Main Methods:

  • Formulated task offloading as a Markov Decision Process (MDP).
  • Integrated a hybrid Gated Recurrent Unit-Long Short-Term Memory (GRU-LSTM) for workload prediction.
  • Employed a Dynamic Dueling Double Deep Q-Network agent for offloading decisions across local, edge, and cloud tiers.
  • Modeled compute nodes using priority-aware queuing systems (M/M/1, M/M/c, M/M/∞).
  • Implemented a dynamic priority scoring function and an energy-aware scheduling policy.

Main Results:

  • AICDQN achieved up to 33.39% reduction in delay.
  • Demonstrated a 57.74% improvement in energy efficiency.
  • Reduced task drop rate by 81.25% compared to existing algorithms.
  • Outperformed Deep Deterministic Policy Gradient (DDPG), Distributed Dynamic Task Offloading (DDTO-DRL), Potential Game based Offloading Algorithm (PGOA), and User-Level Online Offloading Framework (ULOOF).

Conclusions:

  • AICDQN provides a scalable and adaptive solution for edge-cloud task offloading.
  • The framework effectively handles real-time, priority-aware, and energy-constrained scheduling.
  • Validated the efficacy of the hybrid GRU-LSTM prediction and Dueling Double DQN agent.