With the rapid growth of wearable devices and the Internet of Things (IoT), users increasingly rely on smartwatches and fitness trackers for services in connected environments. These devices collect sensitive physiological and behavioral data, requiring reliable user authentication to ensure secure access by authorized users. However, few-shot scenarios with significant intra-class variability and limited training data make it challenging for models to capture complex feature interactions. In this paper, we propose a Ratio-Enhanced Feature Interaction (RE-FI) framework designed for wearable running data. RE-FI systematically constructs pairwise product and ratio-based cross features to explicitly model second-order interactions and applies information gain–based feature selection to retain the most discriminative features. We provide theoretical justification for these design choices across linear models, tree-based classifiers, and deep neural networks. We validate RE-FI on real-world running data using nine representative classifiers. Results show that RE-FI consistently outperforms the baseline feature set across all classifiers, achieving an average accuracy improvement of 4.2% and up to 6.2% in F1-score. In extreme few-shot settings with only five samples per user, RE-FI maintains robust performance, with Random Forest achieving an average accuracy of 86%. Overall, this work offers a computationally simple feature-interaction solution that is compatible with diverse supervised models on session-level wearable statistical features and provides a principled approach for wearable security and personalized health monitoring in IoT applications.

Technologies such as intelligent reflective surface (IRS)-assisted free-space optics (FSO) systems, are capable of providing connectivity with varied bandwidth through flexibly allocating resources on a planer panel. This flexibility provides new opportunities in handling inter-rack traffic that is often unevenly distributed in space and time. In this paper, we explore a new type of resource allocation scheme by taking full advantage of this flexibility. The key ideas include: (1) dynamically divide the resource on the planar panel into two planes, such that the small and large flows can be handled separately; (2) in the plane serving small flows, use multi-hop routing with variable bandwidth connections to handle skewed traffic between source-destination pairs; (3) in the plane serving large flows, use direct links with variable bandwidth to schedule unevenly distributed demand. In essence, through flexible resource allocation in and between the two planes, this scheme is able to make more efficient use of resources. We theoretically analyze the capacities of the proposed scheme, where the effectiveness conditions and performance bounds for dynamic resource adaptation are derived and the traffic scheduling with flexible bandwidth in two planes is proved to be able to achieve constant factor approximation to the ideally optimal performance. We then propose a set of lightweight algorithms with performance guarantees in each of two planes, collectively calledFlexAlloc, to realize the flexibility and adaptivity of the proposed scheme, and discuss an example of this scheme in the context of IRS-assisted FSO systems. Our simulation results show thatFlexAllocexhibits significant superiority over existing schemes for unevenly distributed traffic at low computational complexity, and can reduce the average flow completion time by 56-81%.