arXiv:2407. 13303v2 Announce Type: replace Abstract: Conventional large-scale indoor localization based on Wi-Fi RSSI fingerprinting faces issues of time-consuming and labor-intensive labeled data collection, limited generalization of a model trained under a supervised learning (SL) framework due to its inability to leverage unlabeled data, and model performance degradation in dynamic scenarios with environmental variations.
By Sihao Li, Zhe Tang, Kyeong Soo Kim, Jeremy S. Smith
arXiv:2606. 11490v1 Announce Type: new Abstract: Indoor localization from wireless measurements remains challenging in large-scale deployments due to substantial variation in building geometry, the set of detectable access points (APs), and the heterogeneity of received signals.
By Lei Chu, Yuning Zhang, Omer Gokalp Serbetci, Anushka Katiyar, Bassel Abou Ali Modad, Andreas F. Molisch
arXiv:2606. 18734v1 Announce Type: cross Abstract: Accurate, site-specific channel information is crucial for optimizing next-generation wireless networks.
By Ye Xue, Yiheng Wang, Xinhua Shao, Qi Yan, Shutao Zhang, Tsung-Hui Chang
arXiv:2606. 02974v1 Announce Type: new Abstract: Human Activity Recognition (HAR) using WiFi signals has emerged as a transformative technology for smart homes, healthcare monitoring, security systems, and ambient assisted living.
By Maheen Arshad, Qindeel E Zahra, Muhammad Khuram Shahzad
The rapid proliferation of Internet of things (IoT) devices has significantly expanded the cyber-attack surface, necessitating robust and privacy-preserving intrusion detection systems (IDS). However, centralized learning approaches often suffer from severe performance degradation due to high-dimensional traffic data, extreme class imbalance, and highly non-independent and identically distributed (non-IID) data across heterogeneous edge devices.
arXiv:2607. 04698v1 Announce Type: new Abstract: The rapid proliferation of Internet of things (IoT) devices has significantly expanded the cyber-attack surface, necessitating robust and privacy-preserving intrusion detection systems (IDS).
By Mohammad Ansarimehr, Somayeh Changiz, Ehsan Baghishani, Ali Mousavi
arXiv:2608. 09285v1 Announce Type: cross Abstract: Learning-based wireless localizers often fail to utilize geometric information about the propagation environment, limiting their ability to exploit non-line-of-sight (NLoS) propagation and generalize across scenes.
By Chenghong Bian, Chaozheng Wen, Hongze Chen, Jun Zhang
arXiv:2604. 22896v2 Announce Type: replace-cross Abstract: Indoor positioning is an essential technology for a wide range of applications in GNSS-denied environments, including indoor navigation and IoT systems.
By Helge Ros\'e, Konstantin Klipp, Tom Koubek, Bernd Sch\"aufele, Ilja Radusch
arXiv:2607. 10822v1 Announce Type: new Abstract: Indoor spatial understanding remains a fundamental challenge for intelligent systems operating in physical environments.
By Curtis Shull, Merrick Green, Roy Rucker
arXiv:2608. 01426v1 Announce Type: new Abstract: Federated learning (FL) enables distributed optimization and learning across decentralized edge devices while preserving data privacy, but its performance is fundamentally constrained by heterogeneous data distributions, limited communication resources, and energy availability.
By Furkan Bagci, Busra Tegin, Mohammad Kazemi, Tolga M. Duman
arXiv:2606. 11914v1 Announce Type: cross Abstract: CSI-based localization with spatially distributed antenna arrays exposes a basic resource trade-off.
By Rodrigo Oliver, Ricardo Vazquez Alvarez, Alejandro Lancho, Stefano Rini
arXiv:2607. 08978v1 Announce Type: new Abstract: Distributed IoT systems generate multivariate time-series streams for monitoring physical assets, servers, and embedded sensing platforms.
By Tung-Anh Nguyen, Van-Phuc Bui, Anh Tuyen Le, Kim Hue Ta, Minh Thuy Le, J. Andrew Zhang, Xiaojing Huang