Towards Pervasive WLAN Localization Leveraging Collaborative Mobile Sites: A Multi-Agent Deep Reinforcement Learning Approach
Nie, Wendi Zhang, Yuanyi Lam, Kam-Yiu Lee, Victor CS Wei, Min Duan, Yaoxin Liu, Kai Xue, Chun Jason Gui, Guan
Nie, Wendi
Zhang, Yuanyi
Lam, Kam-Yiu
Lee, Victor CS
Wei, Min
Duan, Yaoxin
Liu, Kai
Xue, Chun Jason
Gui, Guan
Supervisor
Department
Computer Science
Embargo End Date
Type
Journal article
Date
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Indoor location-based services (LBS) have witnessed rapid growth in applications such as user tracking, healthcare monitoring, and smart facility management, driving the critical need for efficient and pervasive indoor localization. Traditional WiFi fingerprinting methods face significant challenges: multi-site localization (MSL) relies on densely deployed static WiFi sites, incurring high infrastructure costs and conflicting with the Integrated Sensing and Communication (ISAC) paradigm; single-site localization (SSL) requires complex hardware; and single mobile site localization (SMSL) suffers from poor real-time performance due to long traversal paths. To address these limitations, this paper proposes a Multi-Agent Deep Reinforcement Learning-based Collaborative Indoor Localization (MADRL-CIL) framework. MADRL-CIL leverages multiple collaborative mobile sites to dynamically acquire Received Signal Strength (RSS) fingerprints. By modeling each mobile site as an agent, the framework formulates the path selection and fingerprint acquisition task as a Multi-Agent Deep Reinforcement Learning (MADRL) problem under a Centralized Training with Decentralized Execution (CTDE) paradigm, facilitating effective collaboration among multiple mobile sites to optimize localization accuracy while minimizing localization time. Additionally, a Multi-Site Fingerprint Matching (MS-FM) model is specifically designed to process collaboratively collected RSS fingerprints, enabling fine-grained localization accuracy. Experimental evaluations in a real-world indoor environment demonstrate that MADRL-CIL achieves localization accuracy comparable to dense multi-static site deployments while provides good real-time performance.
Citation
W. Nie, Y. Zhang, K.-Y. Lam, V.C.S. Lee, M. Wei, Y. Duan , et al., "Towards Pervasive WLAN Localization Leveraging Collaborative Mobile Sites: A Multi-Agent Deep Reinforcement Learning Approach," IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp. 1-16, 2026, https://doi.org/10.1109/tmc.2026.3677256.
Source
IEEE Transactions on Mobile Computing
Conference
Keywords
46 Information and Computing Sciences, 4602 Artificial Intelligence, 4605 Data Management and Data Science, 4606 Distributed Computing and Systems Software, 4608 Human-Centred Computing
Subjects
Source
Publisher
IEEE