6 papers
ARMOR: Adaptive Resilience Against Model Poisoning Attacks in Continual Federated Learning for Mobile Indoor Localization
Danish Gufran, Akhil Singampalli, Sudeep Pasricha
Indoor localization has become increasingly essential for applications ranging from asset tracking to delivering personalized services. Federated learning (FL) offers a privacy-pre…
GATE: Graph Attention Neural Networks with Real-Time Edge Construction for Robust Indoor Localization using Mobile Embedded Devices
Danish Gufran, Sudeep Pasricha
Accurate indoor localization is crucial for enabling spatial context in smart environments and navigation systems. Wi-Fi Received Signal Strength (RSS) fingerprinting is a widely u…
Towards Explainable Indoor Localization: Interpreting Neural Network Learning on Wi-Fi Fingerprints Using Logic Gates
Danish Gufran, Sudeep Pasricha
Indoor localization using deep learning (DL) has demonstrated strong accuracy in mapping Wi-Fi RSS fingerprints to physical locations; however, most existing DL frameworks function…
DAILOC: Domain-Incremental Learning for Indoor Localization using Smartphones
Akhil Singampalli, Danish Gufran, Sudeep Pasricha
Wi-Fi fingerprinting-based indoor localization faces significant challenges in real-world deployments due to domain shifts arising from device heterogeneity and temporal variations…
SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization
Akhil Singampalli, Danish Gufran, Sudeep Pasricha
Machine learning (ML) based indoor localization solutions are critical for many emerging applications, yet their efficacy is often compromised by hardware/software variations acros…
SENTINEL: Securing Indoor Localization against Adversarial Attacks with Capsule Neural Networks
Danish Gufran, Pooja Anandathirtha, Sudeep Pasricha
With the increasing demand for edge device powered location-based services in indoor environments, Wi-Fi received signal strength (RSS) fingerprinting has become popular, given the…