Abstract
Localization in indoor environment is one of the major area of research in the present era. With advancement of technology and extensive use of smartphone applications the requirement for development of fast reliable location based service is needed. RSSI fingerprinting from WiFi sources is a popular procedure for localization in indoor environment although a reliable, ubiquitous end-to-end solution based on machine learning is still at bay. In this work, such a real-time framework for indoor positioning is developed and the design of the implemented prototype is also discussed. The pre-trained models are stored at a local server where the test data collected by the smartphones are analyzed in real-time for location prediction. The work has also addressed the class imbalance problem, where a pre-processing procedure is applied before the positioning. An in depth analysis of the accuracy parameter is estimated. Around 1.5 m precision could be observed which is sufficient for indoor positioning of users.
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Panja, A.K. et al. (2021). Designing a Framework for Real-Time WiFi-Based Indoor Positioning. In: Banerjee, S., Mandal, J.K. (eds) Advances in Smart Communication Technology and Information Processing. Lecture Notes in Networks and Systems, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-15-9433-5_8
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DOI: https://doi.org/10.1007/978-981-15-9433-5_8
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