SMOTE for Wi-Fi Fingerprint Construction in Indoor Positioning Systems

Abstract

Wi-Fi fingerprinting techniques for indoor positioning systems (IPS) have been extensively studied due to its high precision and reliability. However, the offline site surveys to collect the updated fingerprints are costly, labourious and time-consuming. Significant efforts have been made to reduce the time-consuming site surveys, such as the use of interpolation techniques and Generative Adversarial Network (GAN) deep learning approaches. A drawback of using GAN is the determination of training sufficiency, whereas for the interpolation, the accuracy of the generated fingerprints can be inadequate. In this paper, a novel fingerprint map construction technique based on the Synthetic Minority Over-sampling Technique (SMOTE) algorithm is proposed to generate synthetic fingerprints in areas that are difficult to reach, or are not regularly visited during offline site surveys. This leads to an imbalanced dataset issue where certain regions are populated with more data points while certain regions are underpopulated. To simulate this situation, a dataset was first augmented to simulate an imbalanced dataset with a minority class and it is rebalanced using SMOTE algorithm. Experimental results show that the proposed scheme can achieve similar accuracy and Root Mean Square Error (RMSE) as the original dataset without SMOTE being applied. Although the accuracy deteriorates as more synthetic data is produced, it remains within an acceptable range of 0.64%. As a result, we can overcome the imbalanced datasets problems for IPS and build a fingerprint database with fewer data points using SMOTE-generated synthetic data to reduce the cost of data collection.