Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms

• Published in: Journal of Electrical Systems and Information Technology Vol. 11; no. 1; pp. 10 - 20
• Main Authors: Maduranga, M. W. P., Tilwari, Valmik, Abeysekera, Ruvan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2024; Springer Nature B.V; SpringerOpen
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Data collection; Decision trees; Design; Determinants; Electrical Engineering; Electrical Machines and Networks; Engineering; Indoor localization; Information Systems and Communication Service; Internet of Things; Linear equations; Localization; Location based services; Machine learning; Mathematical models; Mechanical Engineering; Neural networks; Polynomials; Power Electronics; Received signal strength indicator; Regression; Sensors; Signal strength; Support vector machines; Wireless access points; Internet of Things; Received signal strength indicator; Artificial intelligence; Indoor localization; Machine learning
• ISSN: 2314-7172; 2314-7172
• DOI: 10.1186/s43067-024-00138-0

With the rapid advancement of the Internet of Things and the popularization of mobile Internet-based applications, the location-based service (LBS) has attracted much attention from commercial developers and researchers. Received signal strength indicator (RSSI)-based indoor localization technology has irreplaceable advantages for many LBS applications. However, due to multipath fading, noise, and the limited dynamic range of the RSSI measurements, precise localization based on a path-loss model and multiliterate becomes highly challenging. Therefore, this study proposes a machine learning (ML)-based improved RSSI-based indoor localization approach in which RSSI data is first augmented and then classified using ML algorithms. In addition, we implement an experimental testbed to collect the RSSI value based on Wi-Fi using various reference and target nodes. The received RSSI measurements undergo pre-processing using pseudo-linear solution techniques for closed-form solutions, approximating the original system of nonlinear RSSI measurement equations with a system of linear equations. Finally, the RSSI measurement are trained using ML models such as linear regression, polynomial regression, support vector regression, random forest regression, and decision tree regression. Consequently, the experimental results express in terms of root mean square error and coefficient of determinant compared with various machine learning models with hyper-parameter tuning.