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#APPLICATION CAPTURE SCREENSHOT PATH ALREADY THERE BLUETOOTH#

This study aims at analyzing various public benchmarks for magnetic field positioning and highlights their pros and cons for evaluation positioning algorithms. While several benchmarks have been introduced over time, the selection criteria of a benchmark are not properly defined, which leads to positioning results that lack generalization. Magnetic field positioning is an attractive solution for indoors yet lack of public benchmarks and selection of suitable benchmarks are among the big challenges. However, special emphasis is put on infrastructureless approaches like Wi-Fi and magnetic field-based positioning, as they do not require additional infrastructure.

#APPLICATION CAPTURE SCREENSHOT PATH ALREADY THERE BLUETOOTH#

Besides Wi-Fi positioning, a rich variety of technologies have been introduced or adopted for indoor positioning such as ultrawideband, infrared, radio frequency identification, Bluetooth beacons, pedestrian dead reckoning, and magnetic field, etc. Concerning the importance of precise location information, many sensors are embedded into modern smartphones. The wide proliferation of smartphones and the availability of fast-speed internet have initiated several location-based services. Indoor positioning and localization have been regarded as some of the most widely researched areas during the last decade.

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. Besides, three different orientations of the smartphone are considered for data collection covering corridors, halls, and laboratories. Different path geometries are followed in different multi-floor buildings which are physically separated, comprising both small and large areas. 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. 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. 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. Also, the proposed approaches can not be tested on common benchmark datasets to analyze the results of the state-of-the-art approaches. However, the lack of publicly available datasets for the magnetic field and Wi-Fi makes it very difficult to extensively investigate these characteristics. Even though Wi-Fi and magnetic field data offer complementary characteristics for enhancing positioning accuracy, several challenges for these technologies remain unresolved.

Additionally, the availability of embedded magnetic and Wi-Fi sensors in smartphones helps to achieve positioning without additional infrastructure. The mass deployment of Wi-Fi access points (APs) and the ubiquity of the magnetic field data make them attractive candidates for indoor positioning. Consequently, a rich variety of positioning technologies have been adopted to provide and enhance user location information. 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.

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