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Title REAL TIME OBJECT LOCALIZATION IN COMPLEX INDOOR ENVIRONMENT USING HYBRID WIRLESS TECHNOLOGIES

Author(s) Ali Raza

Abstract Positioning refers to locating the actual position of an object with respect to some coordinates, i.e. two-dimensional (x, y), with reference to some existing known place. Positioning is further divided into two more categories, like indoor and outdoor positioning. For outdoor localization, the Global Positioning System (GPS) is already an existing solution that is not suitable for indoor environments due to different obstacles such as line of sight (LOS). In the case of indoor environments where the existing solutions are still not up to the mark. Indoor environments have complex and different obstacles like furniture, the presence of human objects, wireless equipment, light, and other physical obstacles that attenuate and degrade the received (RSS) signal strength, due to which position estimation accuracy is affected. To address this problem, different scholars used various technologies such as Bluetooth, wireless local area networks (WLAN), and ZigBee together with traditional and trigonometric methods as well as machine learning techniques to minimize the error and improve position estimation accuracy. In this research, we have proposed a real-time positioning system based on hybrid wireless technologies using existing machine learning models such as support vector machine (SVM), random forest (RF), and logistic regression (LR) that are applied to the Miskolc hybrid indoor localization dataset based on three wireless technologies: magnetometer, wireless local area networks (WLAN), and Bluetooth. Based on our simulation results using hybrid technologies, machine learning models give accuracies of 83.7%, 93.5%, and 98.7% with an error rate of 0.162m, 0.065 m, and 0.012m for logistic regression, support vector machine, and random forest, respectively. Experimental results and literature survey also validate that random forest (RF) achieved a high accuracy of 98.7% and a lower error rate of 0.012m as compared to the other machine learning models, showing a remarkable improvement compared to previous approaches.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Computer Science

Language English

Publication Date 2023-07-20

Subject Computer Science

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