Impact of Network Data Complexity on Machine Learning Performance for Real-Time Iot Systems

This paper explores the relationship between network data complexity and machine learning (ML) performance, focusing on distributed and real-time IoT systems. Using intrinsic dimensionality (ID) to measure structural complexity, we analyze 20 datasets (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 0}$</tex> for network and IoT systems and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 0}$</tex> for non-network systems) and show that network datasets have lower ID values, indicating simpler structures that correlate with improved ML performance. We identify optimal algorithms for different ID ranges, offering practical guidance for selecting ML models tailored to network data. Additionally, we find that Euclidean distance outperforms Hamming distance for complexity measurement across both categories of data, though its higher computational cost should be considered for real-time IoT applications. These findings provide valuable insights for selecting efficient ML algorithms and metrics, supporting scalable and time-sensitive IoT systems.