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Optimizing Mobile Apps for Device-Free Indoor Localization in Dynamic Environments Using Analytical Geometry and Machine Learning
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Author(s): Ravikumar Ch (Sreenidhi University, Hyderabad, India), Vasepalli Kamakshamma (Srinivasa Ramanujan Institute of Technology, India), P. Radhika (TKR College of Engineering, India), Isha Batra (Lovely Professional University, India), Arun Malik (Lovely Professional University, India)and Kalvog Prakasha Chary (CVR College of Engineering, India)
Copyright: 2026
Pages: 18
Source title:
Analyzing Mobile Apps Using Smart Assessment Methodology
Source Author(s)/Editor(s): Basheer Riskhan (Albukhary International University, Malaysia), Khalid Hussain (Albukhary International University, Malaysia)and Halawati Abd Jalil Safuan (Albukhary International University, Malaysia)
DOI: 10.4018/979-8-3693-6925-8.ch002
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Abstract
Device-free indoor localization is pivotal in smart environments, enabling precise positioning for applications like smart homes, healthcare, and IoT systems. This chapter presents an advanced localization framework optimized for mobile apps, integrating analytical geometry with machine learning techniques to overcome the challenges of dynamic indoor environments. Using Received Signal Strength Indication (RSSI) data from strategically placed Wi-Fi modules, the system identifies precise locations without requiring individuals to carry devices. The methodology includes systematic data collection, preprocessing, and training of Random Forest models, ensuring robustness against environmental changes such as moving obstacles and varying occupancy. Experimental results highlight significant improvements in accuracy and responsiveness compared to conventional approaches, establishing the proposed method as a viable solution for real-world indoor localization scenarios. This work offers a scalable and efficient pathway for mobile applications in dynamic IoT ecosystems.
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