Deployment and Allocation Strategy for MEC Nodes in Complex Multi-Terminal Scenarios.

Area of Science:

• Computer Science
• Distributed Computing
• Network Engineering

Background:

• Mobile Edge Computing (MEC) offers a solution to the computing and communication limitations of Internet of Things (IoT) applications by leveraging edge resources.
• Optimal deployment of edge nodes is critical for system performance in multi-terminal end-edge-cloud architectures.

Purpose of the Study:

• To develop an evaluation model for edge node deployment and allocation in MEC systems.
• To address challenges posed by spatial location, power supply, and terminal urgency requirements.

Main Methods:

• An evaluation model incorporating reward, energy consumption, and cost factors was developed.
• A genetic algorithm was employed to determine optimal edge node deployment and allocation strategies.
• The proposed method was compared against k-means and ant colony algorithms.

Main Results:

• The genetic algorithm-based strategies demonstrated effective evaluation results within problem constraints.
• Comparison tests with varying attributes confirmed the proposed method's robust performance.

Conclusions:

• The proposed evaluation model and genetic algorithm provide an effective approach for optimizing edge node deployment and allocation in MEC for IoT.
• The method outperforms traditional algorithms like k-means and ant colony in achieving efficient system performance under defined constraints.