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Updated: Nov 15, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Published on: September 8, 2023

936

Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing.

Shuran Sheng1, Peng Chen2, Zhimin Chen3

  • 1School of Information Science and Engineering, Southeast University, Nanjing 210096, China.

Sensors (Basel, Switzerland)
|March 6, 2021
PubMed
Summary
This summary is machine-generated.

Edge computing task scheduling is optimized using deep reinforcement learning (DRL). This approach maximizes task satisfaction by intelligently assigning tasks to virtual machines, outperforming existing methods.

Keywords:
Internet of Things (IoT)deep reinforcement learning (DRL)edge computingmarkov decision process (MDP)task scheduling

Related Experiment Videos

Last Updated: Nov 15, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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Published on: September 8, 2023

936

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Edge computing (EC) enables low-latency services for Internet of Things (IoT) applications.
  • Limited edge server capacity presents challenges for scheduling resource-intensive application tasks.
  • Efficient task scheduling is crucial for maximizing service quality in EC environments.

Purpose of the Study:

  • To address the challenge of task scheduling in edge computing environments.
  • To maximize the long-term task satisfaction degree (LTSD) for diverse IoT applications.
  • To develop an intelligent scheduling algorithm considering task diversity and resource heterogeneity.

Main Methods:

  • Formulated the task scheduling problem as a Markov decision process (MDP).
  • Leveraged deep reinforcement learning (DRL), specifically a policy-based REINFORCE algorithm.
  • Utilized a fully-connected neural network (FCN) for feature extraction in task scheduling and resource allocation.

Main Results:

  • The proposed DRL-based algorithm effectively handles both task execution order and VM assignment.
  • Simulation results demonstrate superior performance compared to existing methods.
  • Achieved higher average task satisfaction degree and success ratio.

Conclusions:

  • Deep reinforcement learning offers a powerful solution for complex task scheduling in edge computing.
  • The developed algorithm enhances the efficiency and reliability of edge computing services.
  • This work provides a significant advancement in optimizing resource allocation for IoT applications on edge servers.