Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Oussama Kerdjidj
,
Yassine Himeur
* ORCID logo ,
Shahab Saquib Sohail
,
Abbes Amira
,
Fodil Fadli
ORCID logo ,
Shadi Atalla
ORCID logo ,
Wathiq Mansoor
ORCID logo ,
Abigail Copiaco
,
Mohammad Daradkeh
ORCID logo ,
Amjad Gawanmeh
ORCID logo ,
Sami Miniaoui
,
Diana Wasfi Dawoud
Version 1 : Received: 29 June 2023 / Approved: 30 June 2023 / Online: 30 June 2023 (12:00:46 CEST)

How to cite: Kerdjidj, O.; Himeur, Y.; Sohail, S.S.; Amira, A.; Fadli, F.; Atalla, S.; Mansoor, W.; Copiaco, A.; Daradkeh, M.; Gawanmeh, A.; Miniaoui, S.; Dawoud, D.W. Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning. Preprints 2023, 2023062249. https://doi.org/10.20944/preprints202306.2249.v1 Kerdjidj, O.; Himeur, Y.; Sohail, S.S.; Amira, A.; Fadli, F.; Atalla, S.; Mansoor, W.; Copiaco, A.; Daradkeh, M.; Gawanmeh, A.; Miniaoui, S.; Dawoud, D.W. Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning. Preprints 2023, 2023062249. https://doi.org/10.20944/preprints202306.2249.v1

Abstract

Indoor localization (IL) is a significant topic of study with several practical applications. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL and its real-life applications. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, The survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of industries, such as evacuation-egress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

Keywords

Indoor localization; Wireless signal techniques; Computer vision techniques; Deep and transfer learning; Hybrid techniques

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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