FGATT: A Robust Framework for Wireless Data Imputation Using Fuzzy Graph Attention Networks and Transformer Encoders

TL;DR

FGATT is a novel framework combining fuzzy graph attention networks and transformers to improve wireless data imputation accuracy and robustness, especially with high missing data rates.

Contribution

The paper introduces FGATT, integrating fuzzy graph attention and transformer encoders with a self-adaptive graph construction for dynamic wireless data imputation.

Findings

Outperforms state-of-the-art imputation methods

Demonstrates high robustness in scenarios with substantial missing data

Effective in wireless sensor networks and IoT applications

Abstract

Missing data is a pervasive challenge in wireless networks and many other domains, often compromising the performance of machine learning and deep learning models. To address this, we propose a novel framework, FGATT, that combines the Fuzzy Graph Attention Network (FGAT) with the Transformer encoder to perform robust and accurate data imputation. FGAT leverages fuzzy rough sets and graph attention mechanisms to capture spatial dependencies dynamically, even in scenarios where predefined spatial information is unavailable. The Transformer encoder is employed to model temporal dependencies, utilizing its self-attention mechanism to focus on significant time-series patterns. A self-adaptive graph construction method is introduced to enable dynamic connectivity learning, ensuring the framework's applicability to a wide range of wireless datasets. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in imputation accuracy and robustness, particularly in scenarios with substantial missing data. The proposed model is well-suited for applications in wireless sensor networks and IoT environments, where data integrity is critical.