7 papers
Ray-Traced Augmentation for Signal Strength Based Localization
Jihoon Og, Ningze Sun, Ioanis Nikolaidis +1
Indoor localization based on Wi-Fi typically relies on extensive collection of real-world received signal strength (RSS) fingerprints, making deployment costly and time-consuming.…
Performance Guarantees for Data Freshness in Resource-Constrained Adversarial IoT Systems
Aresh Dadlani, Muthukrishnan Senthil Kumar, Omid Ardakanian +1
Timely updates are critical for real-time monitoring and control applications powered by the Internet of Things (IoT). As these systems scale, they become increasingly vulnerable t…
CLOAK: Contrastive Guidance for Latent Diffusion-Based Data Obfuscation
Xin Yang, Omid Ardakanian
Data obfuscation is a promising technique for mitigating attribute inference attacks by semi-trusted parties with access to time-series data emitted by sensors. Recent advances lev…
Budgeted Indirect Adversarial Attack on Graph-Based Anomaly Detection in Sensor Networks
Sanju Xaviar, Omid Ardakanian
Graph Neural Networks (GNNs) have emerged as powerful models for anomaly detection in sensor networks, particularly when analyzing multivariate time series. In this work, we introd…
Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis
Amirhossein Sohrabbeig, Omid Ardakanian, Petr Musilek
Long-term forecasting of multivariate urban data poses a significant challenge due to the complex spatiotemporal dependencies inherent in such datasets. This paper presents DST, a…
PrivDiffuser: Privacy-Guided Diffusion Model for Data Obfuscation in Sensor Networks
Xin Yang, Omid Ardakanian
Sensor data collected by Internet of Things (IoT) devices can reveal sensitive personal information about individuals, raising significant privacy concerns when shared with semi-tr…