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Intelligent Rapid Adaptive Offloading Algorithm for Computational Services in Dynamic Internet of Things System.

Xuejing Li1, Yajuan Qin2, Huachun Zhou3

  • 1School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China. lixuejing@bjtu.edu.cn.

Sensors (Basel, Switzerland)
|August 7, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an intelligent rapid adaptive offloading (IRAO) algorithm for Internet of Things (IoT) systems. The IRAO algorithm enhances computational performance and fairness using machine learning and edge computing.

Keywords:
Internet of thingsdeep neural networkedge computingoffloading policyresource allocation

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • Massive Internet of Things (IoT) devices face computational limitations due to restricted resources.
  • Multi-access edge computing (MEC) offers a solution by offloading computations to nearby edge servers.
  • There is a need for efficient algorithms to manage offloading and resource allocation in dynamic IoT environments.

Purpose of the Study:

  • To propose an intelligent rapid adaptive offloading (IRAO) algorithm for dynamic IoT systems.
  • To enhance overall computational performance and ensure fairness among multiple participants.
  • To address joint optimization problems in offloading policy and resource allocation.

Main Methods:

  • An adaptive learning-based framework integrating offloading decision-making, radio resource slicing, and parameter updating.
  • A deep neural network (DNN) with experience replay and asynchronous sampling for rapid offloading policy estimation.
  • Extensive simulations to evaluate the algorithm's trade-offs between accuracy and efficiency.

Main Results:

  • The IRAO algorithm demonstrates superior performance compared to existing methods.
  • Achieved improvements in scalability, effectiveness, and efficiency for IoT systems.
  • Validated the effectiveness of the DNN-based estimation and adaptive learning framework.

Conclusions:

  • The proposed IRAO algorithm effectively addresses computational limitations in IoT devices.
  • The integration of MEC and machine learning provides a scalable and efficient solution.
  • The IRAO algorithm offers a promising approach for optimizing dynamic IoT systems.