Article: Comprehensive guide to indoor localisation: exploring wireless technologies Journal: International Journal of Mobile Network Design and Innovation (IJMNDI) 2025 Vol.11 No.3 pp.143 - 151 Abstract: It's getting more and more important in school and business to be able to find your way around inside buildings as network technology and smart phones become more common. Wi-Fi technology has a lot of potential for use because it is widely available in general open spaces. The majority of the currently available methods of predicting locations based on a collection of annotated Wi-Fi observations make use of trilateration or machine learning techniques. In order to instruct the models, a mixed learning strategy must be developed. This approach combines controlled, unsupervised, and semi - supervised learning methodologies to maximise the value of the gathered data. Extensive trials indicate that our method allows the models to acquire useful information from unlabelled data with gradual gains. Furthermore, it has the potential to achieve impressive performance in terms of localisation and navigation in an indoor environment that is complicated and contains obstacles. Inderscience Publishers - linking academia, business and industry through research

Title: Comprehensive guide to indoor localisation: exploring wireless technologies

Authors: Sankarsan Panda; B. Bharathi; K.B. Aruna; K. PeriyarSelvam

Addresses: Acharya Shri Mahapragya Institute of Excellence (ASMIE), Mahapragya Nagar, Asind, Bhilwara, Rajasthan, 311301, India ' Department of Artificial Intelligence and Data Science, V.S.B. Engineering College, Karur, NH-67, Covai Road, Karudayampalayam Post, Karur, 639111, Tamil Nadu, India ' Department of Computer Science and Engineering, S.A. Engineering College, Poonamallee-Avadi Main Road, Thiruverkadu, Chennai, Tamil Nadu, 600077, India ' GRT Institute of Engineering and Technology, GRT Mahalakshmi Nagar, Chennai-Tirupathi Highway, Tiruttani, Tiruvallur District, 631209, Tamil Nadu, India

Abstract: It's getting more and more important in school and business to be able to find your way around inside buildings as network technology and smart phones become more common. Wi-Fi technology has a lot of potential for use because it is widely available in general open spaces. The majority of the currently available methods of predicting locations based on a collection of annotated Wi-Fi observations make use of trilateration or machine learning techniques. In order to instruct the models, a mixed learning strategy must be developed. This approach combines controlled, unsupervised, and semi - supervised learning methodologies to maximise the value of the gathered data. Extensive trials indicate that our method allows the models to acquire useful information from unlabelled data with gradual gains. Furthermore, it has the potential to achieve impressive performance in terms of localisation and navigation in an indoor environment that is complicated and contains obstacles.

Keywords: indoor localisation; wireless technology; decode model for indoor localisation; indoor navigation task; HPO; hyperparameter optimisation.

DOI: 10.1504/IJMNDI.2025.146748

International Journal of Mobile Network Design and Innovation, 2025 Vol.11 No.3, pp.143 - 151

Received: 20 Dec 2024
Accepted: 24 Feb 2025
Published online: 16 Jun 2025 *

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Title: Comprehensive guide to indoor localisation: exploring wireless technologies

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