Machine Learning for Indoor Localization and Navigation
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 97
Special Issue Editor
Special Issue Information
Dear Colleagues,
As digitalization and automation continue to extend their reach, the need for accurate indoor location information has become increasingly important. In recent years, numerous researchers have explored ways to overcome the limitations of traditional indoor localization using machine learning algorithms. There are vast opportunities for machine learning to enhance indoor positioning, including RF SLAM, visual SLAM, fusion algorithms, BLE and UWB signal processing, PDR/INS, seamless tracking, and industrial localization, among others, which remain areas that require further study.
This Special Issue aims to bring together machine learning applications for indoor localization and navigation, presenting theoretical ideas, practical recommendations, experimental designs, data analysis, and real-world applications.
Potential topics include but are not limited to the following:
- Supervised/semi-supervised/unsupervised/self-supervised learning for indoor localization and navigation;
- Optimal path planning and navigation;
- BLE/UWB/5G/LTE fingerprint, magnetic fingerprint localization;
- Ultrasound, optical, light, millimeter wave, device-free localization;
- IMU-based (PDR, INS) localization and navigation;
- Visual/lidar/radar/RF SLAM;
- Fused positioning algorithm;
- Hybrid, cooperative systems;
- Indoor/outdoor seamless tracking;
- Crowdsourcing localization and navigation;
- Industrial applications for localization and navigation;
- Reinforcement learning for service robot navigation;
- Quantum machine learning for localization;
- Federated learning for privacy-preserving localization;
- Adaptive learning localization systems;
- Context-aware localization;
- Localization robustness to environmental changes;
- AI-Driven calibration and self-optimization of localization hardware and algorithms;
- Augmented Reality (AR) integration;
- Localization in multi-level buildings and in challenging environments;
- Energy-efficient localization;
- Human-Machine Interaction and feedback systems;
- Integration of localization approaches with IoT networks.
Dr. Jaehyun Yoo
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning algorithms
- localization
- navigation
