Ragini Gupta

Ragini Gupta, Naman Raina, Bo Chen et al.

Modern IoT deployments for environmental sensing produce high volume spatiotemporal data to support downstream tasks such as forecasting, typically powered by machine learning models. While existing filtering and strategic deployment techniques optimize collected data volume at the edge, they overlook how variations in sampling frequencies and spatial coverage affect downstream model performance. In many forecasting models, incorporating data from additional sensors denoise predictions by providing broader spatial contexts. This interplay between sampling frequency, spatial coverage and different forecasting model architectures remain underexplored. This work presents a systematic study of forecasting models - classical models (VAR), neural networks (GRU, Transformer), spatio-temporal graph neural networks (STGNNs), and time series foundation models (TSFMs: Chronos Moirai, TimesFM) under varying spatial sensor nodes density and sampling intervals using real-world temperature data in a wireless sensor network. Our results show that STGNNs are effective when sensor deployments are sparse and sampling rate is moderate, leveraging spatial correlations via encoded graph structure to compensate for limited coverage. In contrast, TSFMs perform competitively at high frequencies but degrade when spatial coverage from neighboring sensors is reduced. Crucially, the multivariate TSFM Moirai outperforms all models by natively learning cross-sensor dependencies. These findings offer actionable insights for building efficient forecasting pipelines in spatio-temporal systems. All code for model configurations, training, dataset, and logs are open-sourced for reproducibility: https://github.com/UIUC-MONET-Projects/Benchmarking-Spatiotemporal-Forecast-Models

Ragini Gupta, Shinan Liu, Ruixiao Zhang et al.

Machine learning has shown promise in network intrusion detection systems, yet its performance often degrades due to concept drift and imbalanced data. These challenges are compounded by the labor-intensive process of labeling network traffic, especially when dealing with evolving and rare attack types, which makes preparing the right data for adaptation difficult. To address these issues, we propose a generative active adaptation framework that minimizes labeling effort while enhancing model robustness. Our approach employs density-aware dataset prior selection to identify the most informative samples for annotation, and leverages deep generative models to conditionally synthesize diverse samples, thereby augmenting the training set and mitigating the effects of concept drift. We evaluate our end-to-end framework \NetGuard on both simulated IDS data and a real-world ISP dataset, demonstrating significant improvements in intrusion detection performance. Our method boosts the overall F1-score from 0.60 (without adaptation) to 0.86. Rare attacks such as Infiltration, Web Attack, and FTP-BruteForce, which originally achieved F1 scores of 0.001, 0.04, and 0.00, improve to 0.30, 0.50, and 0.71, respectively, with generative active adaptation in the CIC-IDS 2018 dataset. Our framework effectively enhances rare attack detection while reducing labeling costs, making it a scalable and practical solution for intrusion detection.

Ragini Gupta, Lingzhi Zhao, Jiaxi Li et al.

In IoT based distributed network of cameras, real-time multi-camera video analytics is challenged by high bandwidth demands and redundant visual data, creating a fundamental tension where reducing data saves network overhead but can degrade model performance, and vice versa. We present STAC, a cross-cameras surveillance system that leverages spatio-temporal associations for efficient object tracking under constrained network conditions. STAC integrates multi-resolution feature learning, ensuring robustness under variable networked system level optimizations such as frame filtering, FFmpeg-based compression, and Region-of-Interest (RoI) masking, to eliminate redundant content across distributed video streams while preserving downstream model accuracy for object identification and tracking. Evaluated on NVIDIA's AICity Challenge dataset, STAC achieves a 76\% improvement in tracking accuracy and an 8.6x reduction in inference latency over a standard multi-object multi-camera tracking baseline (using YOLOv4 and DeepSORT). Furthermore, 29\% of redundant frames are filtered, significantly reducing data volume without compromising inference quality.