A novel feature based ensemble learning model for indoor localization of smartphone users. (January 2022)

• Record Type: Journal Article
• Authors: Panja, Ayan Kumar; Karim, Syed Fahim; Neogy, Sarmistha; Chowdhury, Chandreyee
• Is Part Of: Engineering applications of artificial intelligence. Volume 107(2022)
• Publication Date: 2022-01
• Subjects: RSS -- Sustainability -- Feature selection -- Metaheuristics -- Ensemble learning -- Indoor localization; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104538
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: For WiFi-based indoor localization, optimal selection of features leads to the increased perceptibility of the localization procedure. It is essential to capture the important sets of Access Points (APs) that best defines the floor map for the positioning process. To maintain sustainable localization, the selection of APs enables scaling the solution and reducing the maintenance cost. In the present work, our contribution is twofold- the power of Particle Swarm Optimization is utilized for the selection of important APs. Then, a feature-based ensemble model is designed for the selected subsets of APs to retain the generality of localization performance. The base learners capture the different ambiance in the training and testing process. Extensive experimentation was carried out using the collected dataset from multiple smartphone devices. The proposed feature selection and training pipeline has also been tested with two popular benchmark datasets- UJIIndoorLoc and JUIndoorLoc. Results indicate that the proposed feature-based ensemble model could achieve 86%–96% accuracy with around 50%–65% reduction in APs for the datasets. The mean absolute error (MAE) indicates the distance between the predicted and actual location points. It is found to be 2.68 m, that is, neighboring location points, which is quite acceptable for user localization in indoor spaces.