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Related Experiment Video

Updated: Nov 15, 2025

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

Published on: September 8, 2023

936

Advanced Deep Learning for Resource Allocation and Security Aware Data Offloading in Industrial Mobile Edge

Ibrahim A Elgendy1,2, Ammar Muthanna3,4, Mohammad Hammoudeh5

  • 1Department of Computer Science and Technology, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.

Big Data
|March 3, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a deep reinforcement learning model for resource allocation and data offloading in industrial Internet of Things (IoT) devices. It significantly reduces energy consumption and computation delay, enhancing efficiency and security.

Keywords:
5Gcomputation offloadingdeep reinforcement learningmobile edge computingsecurity

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Last Updated: Nov 15, 2025

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

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

Published on: September 8, 2023

936

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • The Internet of Things (IoT) enables continuous environmental monitoring and data collection, driving a shift towards mobile edge computing for improved latency, security, and bandwidth.
  • Industrial IoT devices face constraints in computation and radio resources, necessitating efficient resource management strategies.

Purpose of the Study:

  • To propose an advanced deep reinforcement resource allocation and security-aware data offloading model for industrial IoT.
  • To address the challenges of constrained resources and optimize for reduced energy consumption and computation delay.

Main Methods:

  • Formulated the resource allocation and data offloading problem as an optimization problem.
  • Developed a deep learning optimization approach to solve the non-deterministic polynomial-time-hard problem.
  • Integrated a 128-bit Advanced Encryption Standard (AES) for data security.

Main Results:

  • The proposed model reduced offloading overhead (energy and time) by up to 64.7% compared to local execution.
  • Outperformed full offloading by up to 13.2% by selectively offloading tasks.
  • Demonstrated adaptability and scalability for numerous mobile devices.

Conclusions:

  • The developed model offers an effective solution for resource-constrained industrial IoT environments.
  • Achieves significant reductions in energy consumption and computation delay while ensuring data security.
  • Provides a scalable and adaptable framework for mobile edge computing in IoT applications.