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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Joint Optimization of Multi-User Partial Offloading Strategy and Resource Allocation Strategy in D2D-Enabled MEC.

Dongping Yong1,2, Ran Liu1,3, Xiaolin Jia1,2

  • 1Mobile Internet of Things and Radio Frequency Identification Technology Key Laboratory of Mianyang (MIOT&RFID), Mianyang 621010, China.

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Summary
This summary is machine-generated.

This study introduces an enhanced particle swarm optimization-Genetic Algorithm (EPSO-GA) to optimize mobile edge computing (MEC) task offloading and power allocation. The EPSO-GA significantly reduces average completion delay and energy consumption for latency-sensitive applications.

Keywords:
D2D communicationshelpers selectionoffloading strategypower allocation

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Communications

Background:

  • Mobile devices face limitations in computing power and energy for demanding applications.
  • Mobile Edge Computing (MEC) addresses these limitations by offloading tasks to edge servers.
  • Device-to-Device (D2D) technology integration enhances MEC network capabilities.

Purpose of the Study:

  • To develop an optimized subtask offloading and transmitting power allocation strategy for D2D-enabled MEC networks.
  • To minimize the weighted sum of average completion delay and average energy consumption.
  • To address a mixed integer nonlinear optimization problem in mobile edge computing.

Main Methods:

  • Proposed an Enhanced Particle Swarm Optimization (EPSO) algorithm for transmit power allocation.
  • Utilized a Genetic Algorithm (GA) for subtask offloading strategy optimization.
  • Developed an alternate optimization algorithm (EPSO-GA) for joint optimization of both strategies.

Main Results:

  • The EPSO-GA algorithm demonstrated superior performance compared to other algorithms.
  • Achieved reductions in average completion delay, average energy consumption, and overall average cost.
  • The EPSO-GA maintained the lowest average cost across varying delay and energy consumption weight coefficients.

Conclusions:

  • The joint optimization strategy using EPSO-GA effectively balances task offloading and power allocation in MEC networks.
  • EPSO-GA offers a robust solution for improving efficiency in computing-intensive and latency-sensitive mobile applications.
  • This approach provides significant performance gains in terms of delay, energy efficiency, and cost.