Adaptive Latency-Aware Predictive Framework for Mobile Edge Systems Using Dynamic Feature Reduction and Intelligent Scheduling

Abstract

Low-latency and resource-efficient predictive analytics are essential for mobile edge computing applications, including smartphone-based human activity recognition (HAR). This study presents the Adaptive Latency Prediction and Scheduling (ALPS) framework, which aims to minimize inference latency, hardware energy consumption, and computational overhead while maintaining predictive accuracy. The ALPS framework comprises three primary modules: an adaptive principal component analysis (PCA) mechanism for real-time feature reduction, a lightweight ridge classifier optimized with an L regularization loss 2 function for rapid multi-class activity prediction, and a dynamic task scheduler that minimizes a combined latency-energy cost function to allocate processing tasks across mobile, edge, and cloud layers. Evaluation on the high-dimensional UCI-HAR smartphone dataset (10,299 samples, 561 features) demonstrated that the adaptive feature reduction module reduced the feature space to 68 principal components, resulting in an 87.9% reduction in dimensionality while maintaining 95% cumulative explained variance. Relative to conventional standalone models and isolated optimization baselines, ALPS achieved a 32% reduction in average end-to-end latency (120 ms), a 25% decrease in energy consumption (180 J), and a classification accuracy of 96.4% across six physical activities. The primary contribution of this study is the unified integration of adaptive data compression and distributed infrastructure scheduling into a scalable and energy-efficient pipeline for real-time edge intelligence.

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