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Efficiently allocating users to edge servers in dynamic Mobile Edge Computing (MEC) environments is challenged by fluctuating demands and diverse user preferences. The paper presents FuNEUA (Fuzzy Neural Network based Edge User Allocation), merging fuzzy logic decision-making with neural networks’ learning capabilities. By incorporating self-attention mechanism, FuNEUA captures and integrates contextual relationships between users and edge servers, enhancing context-awareness in allocation. Based on fuzzy logic, FuNEUA facilitates localized load balancing optimization among edge servers situated near users. Experiments on real-world datasets demonstrate that FuNEUA outperforms baseline methods across multiple performance metrics.

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FuNEUA: An Intelligent Edge User Allocation Approach Based on Fuzzy Neural Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Shangzhen Zeng,
  • Ningjiang Chen

摘要

Efficiently allocating users to edge servers in dynamic Mobile Edge Computing (MEC) environments is challenged by fluctuating demands and diverse user preferences. The paper presents FuNEUA (Fuzzy Neural Network based Edge User Allocation), merging fuzzy logic decision-making with neural networks’ learning capabilities. By incorporating self-attention mechanism, FuNEUA captures and integrates contextual relationships between users and edge servers, enhancing context-awareness in allocation. Based on fuzzy logic, FuNEUA facilitates localized load balancing optimization among edge servers situated near users. Experiments on real-world datasets demonstrate that FuNEUA outperforms baseline methods across multiple performance metrics.