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An LSTM-Method-Based Availability Prediction for Optimized Offloading in Mobile Edges.

Chaoxiong Cui1, Ming Zhao2, Kelvin Wong3

  • 1School of Computer Science and Engineering, Central South University, Changsha 410000, China. cuichaoxiong@csu.edu.cn.

Sensors (Basel, Switzerland)
|October 18, 2019
PubMed
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This study introduces an offloading optimization algorithm for mobile edge computing (MEC) that predicts base station availability. The algorithm improves energy savings and reduces task completion time in mobile environments.

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • Mobile edge computing (MEC) enhances vehicle terminal (VT) computation by offloading tasks to MEC-enabled base stations (MEC-BS).
  • Intermittent connectivity between VTs and MEC-BS due to vehicle mobility and MEC-BS capacity limits performance.
  • Cognitive computing can predict MEC-BS availability to improve mobile environment performance.

Purpose of the Study:

  • To propose an offloading optimization algorithm for MEC based on availability prediction.
  • To enhance MEC-BS availability prediction accuracy using Empirical Mode Decomposition (EMD) and Long Short-Term Memory (LSTM).
  • To minimize overall cost by considering MEC availability, completion time, and energy consumption.

Main Methods:

Keywords:
cognitive computingcomputation offloadingdeep learningmobile edge computingmobilityoptimization

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  • Developed an offloading optimization algorithm leveraging availability prediction.
  • Integrated Empirical Mode Decomposition (EMD) and LSTM for improved MEC-BS availability prediction.
  • Utilized a game-theoretic approach for optimal offloading decisions, considering admission control and mobility.

    • Main Results:

    • The proposed algorithm effectively minimizes overall costs by balancing MEC availability, task completion time, and energy consumption.
    • Availability prediction accuracy was significantly improved using the combined EMD and LSTM approach.
    • Experimental results demonstrated superior energy savings and reduced completion times compared to existing algorithms.

    Conclusions:

    • The proposed availability prediction-based offloading optimization algorithm enhances MEC performance in mobile environments.
    • Accurate prediction of MEC-BS availability is crucial for efficient resource management and task offloading.
    • The integration of EMD and LSTM offers a promising direction for improving predictive capabilities in dynamic mobile networks.