MagWi: Benchmark Dataset for Long Term Magnetic Field And Wi-Fi Data Involving Heterogeneous Smartphones, Multiple Orientations, Spatial Diversity and Multi-floor Buildings

The wide use of mobile devices introduced several new services for the consumer market which are collectively called location-based services, the name being indicative of the significance of the consumer position. Consequently, a rich variety of positioning technologies have been adopted to provide and enhance user location information. The mass deployment of Wi-Fi access points (APs) and the ubiquity of the magnetic field data make them attractive candidates for indoor positioning. Additionally, the availability of embedded magnetic and Wi-Fi sensors in smartphones helps to achieve positioning without additional infrastructure. Even though Wi-Fi and magnetic field data offer complementary characteristics for enhancing positioning accuracy, several challenges for these technologies remain unresolved. However, the lack of publicly available datasets for the magnetic field and Wi-Fi makes it very difficult to extensively investigate these characteristics. Also, the proposed approaches cannot be tested on common benchmark datasets to analyze the results of the state-of-the-art approaches. To resolve these issues, this study presents a dataset that comprises the magnetic field, Wi-Fi, and the data from the inertial measurement unit (IMU) sensors of the smartphone including accelerometer, gyroscope, and barometer. First, the important characteristics of both the Wi-Fi and the magnetic field that require further investigation are highlighted, and later the data are collected. The data are collected over a longer period spanning approximately five years involving five different smartphones used by four different users, both female, and males. Different path geometries are followed in different multi-floor buildings which are physically separated, comprising both small and large areas. Besides, three different orientations of the smartphone are considered for data collection covering corridors, halls, and laboratories. The data from the stairs help to test ‘stairs up’ and ‘stairs down’ events and approaches aiming at multi-floor positioning can be tested with the provided dataset.