TY - JOUR
T1 - RSSI Fingerprint-Based Indoor Localization Solutions Using Machine Learning Algorithms
T2 - A Comprehensive Review
AU - Zholamanov, Batyrbek
AU - Saymbetov, Ahmet
AU - Nurgaliyev, Madiyar
AU - Bolatbek, Askhat
AU - Dosymbetova, Gulbakhar
AU - Kuttybay, Nurzhigit
AU - Orynbassar, Sayat
AU - Kapparova, Ainur
AU - Koshkarbay, Nursultan
AU - Beyca, Ömer Faruk
N1 - Publisher Copyright:
© 2025 by the authors.
PY - 2025/10
Y1 - 2025/10
N2 - Highlights: What are the main findings? The review consolidates recent advances in RSSI fingerprint-based indoor localization, providing a complete view from technology choice to ML/DL model application. It systematically classifies radiomap generation and data preprocessing methods, compares algorithm performance, and identifies unresolved technical bottlenecks. What is the implication of the main finding? The structured analysis offers a ready-to-use roadmap for researchers, helping to design efficient and adaptable localization systems. By mapping challenges to potential solutions, the review supports targeted innovation and faster adoption of RSSI-based positioning in diverse real-world scenarios. With the development of technologies and the growing need for accurate positioning inside buildings, the localization method based on Received Signal Strength Indicator (RSSI) fingerprinting is becoming increasingly popular. Its popularity is explained by the relative simplicity of implementation, low cost and the ability to use existing wireless infrastructure. This review article covers all the key aspects of building such systems: from the wireless communication technology and the creation of a radiomap to data preprocessing methods and model training using machine learning (ML) and deep learning (DL) algorithms. Specific recommendations are provided for each stage that can be useful for both researchers and practicing engineers. Particular attention is paid to such important issues as RSSI signal instability, the impact of multipath propagation, differences between devices and system scalability issues. In conclusion, the review highlights the most promising areas for further research. For smart cities, the approaches and recommendations presented in the review contribute to the development of urban services by combining indoor positioning systems with IoT platforms for automation, transport and energy management.
AB - Highlights: What are the main findings? The review consolidates recent advances in RSSI fingerprint-based indoor localization, providing a complete view from technology choice to ML/DL model application. It systematically classifies radiomap generation and data preprocessing methods, compares algorithm performance, and identifies unresolved technical bottlenecks. What is the implication of the main finding? The structured analysis offers a ready-to-use roadmap for researchers, helping to design efficient and adaptable localization systems. By mapping challenges to potential solutions, the review supports targeted innovation and faster adoption of RSSI-based positioning in diverse real-world scenarios. With the development of technologies and the growing need for accurate positioning inside buildings, the localization method based on Received Signal Strength Indicator (RSSI) fingerprinting is becoming increasingly popular. Its popularity is explained by the relative simplicity of implementation, low cost and the ability to use existing wireless infrastructure. This review article covers all the key aspects of building such systems: from the wireless communication technology and the creation of a radiomap to data preprocessing methods and model training using machine learning (ML) and deep learning (DL) algorithms. Specific recommendations are provided for each stage that can be useful for both researchers and practicing engineers. Particular attention is paid to such important issues as RSSI signal instability, the impact of multipath propagation, differences between devices and system scalability issues. In conclusion, the review highlights the most promising areas for further research. For smart cities, the approaches and recommendations presented in the review contribute to the development of urban services by combining indoor positioning systems with IoT platforms for automation, transport and energy management.
KW - RSSI
KW - deep learning
KW - fingerprint
KW - indoor positioning
KW - localization
KW - machine learning
UR - https://www.scopus.com/pages/publications/105020199172
U2 - 10.3390/smartcities8050153
DO - 10.3390/smartcities8050153
M3 - Review article
AN - SCOPUS:105020199172
SN - 2624-6511
VL - 8
JO - Smart Cities
JF - Smart Cities
IS - 5
M1 - 153
ER -
