Hybrid Wi-Fi and Machine Learning Enabled Indoor Positioning Framework for Smart Campuses and Smart Cities

Sana Hafez; Mohammed Ghazal; Taimur Hassan; Eqab Almajali; Abir Jaafar Hussain; Jawad Yousaf

doi:10.1007/978-3-032-19981-2_1

Hybrid Wi-Fi and Machine Learning Enabled Indoor Positioning Framework for Smart Campuses and Smart Cities

Sana Hafez
, Mohammed Ghazal
, Taimur Hassan
, Eqab Almajali
, Abir Jaafar Hussain
, Jawad Yousaf^*

^*Corresponding author for this work

Abu Dhabi University
Liwa University
University of Sharjah

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

Indoor localization and navigation continue to present significant research challenges due to the unreliability of Global Positioning System (GPS) in indoor environments. In this paper, we present a hybrid IoT-enabled indoor positioning framework that amalgamates Wi-Fi fingerprinting, machine learning-driven coordinate regression, and adaptive path planning. The Received Signal Strength Indicator (RSSI) fingerprints access points, a Large Language Model (LLM)-based agent for data cleaning, and a Random Forest classifier and regressor are used to determine the room ID and the exact coordinates of the user on the floor, respectively. A Kalman filter is employed to mitigate noise and environmental instabilities, while Dijkstra’s algorithm is implemented for obstacle-aware indoor navigation. The framework is deployed in a university campus, achieving a Mean Average Error (MAE) of 0.37 m in coordinate estimation, and over 99% accuracy in room classification. The proposed system is scalable and cost-effective, which makes it a promising solution for smart campuses and smart cities in indoor settings like hospitals, airports, and malls.

Original language	English
Title of host publication	Internet of Things, Smart Spaces, and Next Generation Networks and Systems
Subtitle of host publication	25th International Conference, NEW2AN 2025, and 18th Conference, ruSMART 2025, Abu Dhabi, United Arab Emirates, November 10–12, 2025, Proceedings, Part II
Editors	Yevgeni Koucheryavy, Neeraj Kumar, Ammar Muthanna, Sachin Sharma
Publisher	Springer, Cham
Number of pages	12
ISBN (Electronic)	9783032199812
ISBN (Print)	9783032199805 (pbk)
DOIs	https://doi.org/10.1007/978-3-032-19981-2_1
Publication status	Published - 2 Aug 2026
Event	25th International Conference on Next Generation Wired/Wireless Networks and Systems, NEW2AN 2025 and the 18th Conference on Internet of Things and Smart Spaces, ruSMART 2025 - Abu Dhabi, United Arab Emirates Duration: 10 Nov 2025 → 12 Nov 2025

Publication series

Name	Lecture Notes in Computer Science (LNCS)
Volume	16462
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Next Generation Wired/Wireless Networks and Systems, NEW2AN 2025 and the 18th Conference on Internet of Things and Smart Spaces, ruSMART 2025
Country/Territory	United Arab Emirates
City	Abu Dhabi
Period	10/11/25 → 12/11/25

Bibliographical note

Copyright © 2026 The Author(s), under exclusive license to Springer Nature Switzerland AG. This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use [ https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms ] but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at:
https://doi.org/10.1007/978-3-032-19981-2_1

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Indoor Localization
IoT
Large Language Model (LLM)
Machine Learning
Navigation
Path Planning
Wi-Fi Fingerprinting

Access to Document

10.1007/978-3-032-19981-2_1

Cite this

Hafez, S., Ghazal, M., Hassan, T., Almajali, E., Hussain, A. J., & Yousaf, J. (2026). Hybrid Wi-Fi and Machine Learning Enabled Indoor Positioning Framework for Smart Campuses and Smart Cities. In Y. Koucheryavy, N. Kumar, A. Muthanna, & S. Sharma (Eds.), Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 25th International Conference, NEW2AN 2025, and 18th Conference, ruSMART 2025, Abu Dhabi, United Arab Emirates, November 10–12, 2025, Proceedings, Part II (Lecture Notes in Computer Science (LNCS); Vol. 16462). Springer, Cham. https://doi.org/10.1007/978-3-032-19981-2_1

Hafez, Sana ; Ghazal, Mohammed ; Hassan, Taimur et al. / Hybrid Wi-Fi and Machine Learning Enabled Indoor Positioning Framework for Smart Campuses and Smart Cities. Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 25th International Conference, NEW2AN 2025, and 18th Conference, ruSMART 2025, Abu Dhabi, United Arab Emirates, November 10–12, 2025, Proceedings, Part II. editor / Yevgeni Koucheryavy ; Neeraj Kumar ; Ammar Muthanna ; Sachin Sharma. Springer, Cham, 2026. (Lecture Notes in Computer Science (LNCS)).

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abstract = "Indoor localization and navigation continue to present significant research challenges due to the unreliability of Global Positioning System (GPS) in indoor environments. In this paper, we present a hybrid IoT-enabled indoor positioning framework that amalgamates Wi-Fi fingerprinting, machine learning-driven coordinate regression, and adaptive path planning. The Received Signal Strength Indicator (RSSI) fingerprints access points, a Large Language Model (LLM)-based agent for data cleaning, and a Random Forest classifier and regressor are used to determine the room ID and the exact coordinates of the user on the floor, respectively. A Kalman filter is employed to mitigate noise and environmental instabilities, while Dijkstra{\textquoteright}s algorithm is implemented for obstacle-aware indoor navigation. The framework is deployed in a university campus, achieving a Mean Average Error (MAE) of 0.37 m in coordinate estimation, and over 99\% accuracy in room classification. The proposed system is scalable and cost-effective, which makes it a promising solution for smart campuses and smart cities in indoor settings like hospitals, airports, and malls.",

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Hafez, S, Ghazal, M, Hassan, T, Almajali, E, Hussain, AJ & Yousaf, J 2026, Hybrid Wi-Fi and Machine Learning Enabled Indoor Positioning Framework for Smart Campuses and Smart Cities. in Y Koucheryavy, N Kumar, A Muthanna & S Sharma (eds), Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 25th International Conference, NEW2AN 2025, and 18th Conference, ruSMART 2025, Abu Dhabi, United Arab Emirates, November 10–12, 2025, Proceedings, Part II. Lecture Notes in Computer Science (LNCS), vol. 16462, Springer, Cham, 25th International Conference on Next Generation Wired/Wireless Networks and Systems, NEW2AN 2025 and the 18th Conference on Internet of Things and Smart Spaces, ruSMART 2025, Abu Dhabi, United Arab Emirates, 10/11/25. https://doi.org/10.1007/978-3-032-19981-2_1

Hybrid Wi-Fi and Machine Learning Enabled Indoor Positioning Framework for Smart Campuses and Smart Cities. / Hafez, Sana; Ghazal, Mohammed; Hassan, Taimur et al.
Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 25th International Conference, NEW2AN 2025, and 18th Conference, ruSMART 2025, Abu Dhabi, United Arab Emirates, November 10–12, 2025, Proceedings, Part II. ed. / Yevgeni Koucheryavy; Neeraj Kumar; Ammar Muthanna; Sachin Sharma. Springer, Cham, 2026. (Lecture Notes in Computer Science (LNCS); Vol. 16462).

Research output: Chapter in Book/Published conference output › Conference publication

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AU - Hafez, Sana

AU - Ghazal, Mohammed

AU - Hassan, Taimur

AU - Almajali, Eqab

AU - Hussain, Abir Jaafar

AU - Yousaf, Jawad

N1 - Copyright © 2026 The Author(s), under exclusive license to Springer Nature Switzerland AG. This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use [ https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms ] but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-032-19981-2_1

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N2 - Indoor localization and navigation continue to present significant research challenges due to the unreliability of Global Positioning System (GPS) in indoor environments. In this paper, we present a hybrid IoT-enabled indoor positioning framework that amalgamates Wi-Fi fingerprinting, machine learning-driven coordinate regression, and adaptive path planning. The Received Signal Strength Indicator (RSSI) fingerprints access points, a Large Language Model (LLM)-based agent for data cleaning, and a Random Forest classifier and regressor are used to determine the room ID and the exact coordinates of the user on the floor, respectively. A Kalman filter is employed to mitigate noise and environmental instabilities, while Dijkstra’s algorithm is implemented for obstacle-aware indoor navigation. The framework is deployed in a university campus, achieving a Mean Average Error (MAE) of 0.37 m in coordinate estimation, and over 99% accuracy in room classification. The proposed system is scalable and cost-effective, which makes it a promising solution for smart campuses and smart cities in indoor settings like hospitals, airports, and malls.

AB - Indoor localization and navigation continue to present significant research challenges due to the unreliability of Global Positioning System (GPS) in indoor environments. In this paper, we present a hybrid IoT-enabled indoor positioning framework that amalgamates Wi-Fi fingerprinting, machine learning-driven coordinate regression, and adaptive path planning. The Received Signal Strength Indicator (RSSI) fingerprints access points, a Large Language Model (LLM)-based agent for data cleaning, and a Random Forest classifier and regressor are used to determine the room ID and the exact coordinates of the user on the floor, respectively. A Kalman filter is employed to mitigate noise and environmental instabilities, while Dijkstra’s algorithm is implemented for obstacle-aware indoor navigation. The framework is deployed in a university campus, achieving a Mean Average Error (MAE) of 0.37 m in coordinate estimation, and over 99% accuracy in room classification. The proposed system is scalable and cost-effective, which makes it a promising solution for smart campuses and smart cities in indoor settings like hospitals, airports, and malls.

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KW - Navigation

KW - Path Planning

KW - Wi-Fi Fingerprinting

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UR - https://www.scopus.com/pages/publications/105046295794

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Hafez S, Ghazal M, Hassan T, Almajali E, Hussain AJ, Yousaf J. Hybrid Wi-Fi and Machine Learning Enabled Indoor Positioning Framework for Smart Campuses and Smart Cities. In Koucheryavy Y, Kumar N, Muthanna A, Sharma S, editors, Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 25th International Conference, NEW2AN 2025, and 18th Conference, ruSMART 2025, Abu Dhabi, United Arab Emirates, November 10–12, 2025, Proceedings, Part II. Springer, Cham. 2026. (Lecture Notes in Computer Science (LNCS)). Epub 2026 Aug 2. doi: 10.1007/978-3-032-19981-2_1