Advances in Deep Learning for Network-Based Localization and Navigation
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: 20 June 2026 | Viewed by 115
Special Issue Editors
Interests: satellite navigation receiver; beidou + 5G navigation communication integration; drone cluster positioning and navigation
Special Issue Information
Dear Colleagues,
The field of network-based localization and navigation has witnessed remarkable progress with the advent of deep learning techniques. From indoor positioning systems to outdoor navigation in complex urban environments, deep learning models have demonstrated superior capabilities in improving accuracy, robustness, and adaptability. These advancements are enabling a wide range of applications, including autonomous vehicles, augmented reality, smart cities, and IoT-based tracking systems.
However, challenges remain in achieving real-time high-precision localization in dynamic and heterogeneous environments. Factors such as signal interference, sparse data availability, and computational constraints pose significant hurdles. Additionally, the integration of multi-modal sensor data, scalability across diverse network infrastructures, and energy-efficient learning algorithms are critical areas requiring further innovation.
The goal of this Special Issue is to showcase cutting-edge research on deep learning methodologies for network-based localization and navigation. We welcome contributions that address theoretical foundations, algorithmic improvements, and practical implementations. Topics of interest include, but are not limited to, the following:
- Deep learning models for RF-based localization (Wi-Fi, 5G, LoRa, etc.);
- Neural networks for sensor fusion in multi-modal navigation systems;
- Self-supervised and unsupervised learning for localization in sparse data scenarios;
- Real-time and lightweight deep learning solutions for edge devices;
- Robustness and adversarial defense in positioning systems;
- Federated and distributed learning for privacy-preserving localization;
- Applications in autonomous systems, IoT, and smart environments;
- Distributed cooperative localization for UAV swarms.
We encourage submissions from both academia and industry, including interdisciplinary approaches that bridge machine learning, wireless communication, and robotics. High-quality original research articles, reviews, and case studies are all welcome. Please join us in advancing the field of deep learning for localization and navigation.
Dr. Enwen Hu
Dr. Lu Yang
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning
- network navigation
- positioning systems
- distribute cooperative localization
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