Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Miscellaneous <s> Although indoor localization is a key topic for mobile computing, it is still very difficult for the mobile sensing community to compare state-of-art localization algorithms due to the scarcity of databases. Thus, a multi-building and multi-floor localization database based on WLAN fingerprinting is presented in this work, being its public access granted for the research community. The here proposed database not only is the biggest database in the literature but it is also the first publicly available database. Among other comprehensively described features, full raw information taken by more than 20 users and by means of 25 devices is provided. <s> BIB001 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Miscellaneous <s> Calibrations for Wi-Fi Indoor Positioning Systems (IPS) are expensive and time-consuming. Recalibrations are necessary periodically to compensate the drifts of radio map. Many crowdsourcing-based approaches have been proposed to calibrate the radio map strategically with user-contributed data. However, these approaches still suffer from several drawbacks. Calibration-on-demand is desirable to reduce the maintenance cost and to enhance the reliability of the Wi-Fi IPS. Wi-Fi IPS degrades only when the radio map changes significantly and lastingly. Such changes are caused by radio map anomalous events. Examples of these events include changes in indoor environment, existence of spurious data from the sensing system and unexpected failure in utility infrastructures. This paper presents a theoretical and experimental study of a crowdsourcing online Wi-Fi radio map anomalous event detection system. Detection of these events will enhance the operation intelligence of Wi-Fi IPS and enable high-level context-aware services. This system is consisted of an outlier detector for abnormal signal identification and an event discriminator for determination of event occurrence. Real world smartphone data and simulated data are used to test the performance of the system. Initial results show that the system is able to detect over 92% of simulated events and 87.5% of real world events with a low false alarm rate. <s> BIB002 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Miscellaneous <s> Indoor localization is a key topic for mobile computing. However, it is still very difficult for the mobile sensing community to compare state-of-art Indoor Positioning Systems due to the scarcity of publicly available databases. Magnetic field-based methods are becoming an important trend in this research field. Here, we present UJIIndoorLoc-Mag database, which can be used to compare magnetic field-based indoor localization methods. It consists of 270 continuous samples for training and 11 for testing. Each sample comprises a set of discrete captures taken along a corridor with a period of 0.1 seconds. In total, there are 40,159 discrete captures, where each one contains features obtained from the magnetometer, the accelerometer and the orientation sensor of the device. The accuracy results obtained using two baseline methods are also presented to show the suitability of the presented database for further comparisons. <s> BIB003 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Miscellaneous <s> This demonstration presents a user-friendly web-based platform that supports intuitive remote benchmarking of different indoor localization solutions. It reduces the barriers for experimental evaluation and fair comparison of their performance across a set of testing environments. The platform is aimed at addressing the limitations in the current praxis of publishing indoor localization research evaluated only in local, potentially biased environments. To this end, it provides a holistic support for the benchmarking process offering: (i) multiple pre-collected raw data-traces from different RF technologies, (ii) high-level interface to control remote wireless testbed facilities, and (iii) a set of tools for creating, storing, comparing and visualizing the performance results of multiple indoor localization solutions. <s> BIB004 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Miscellaneous <s> This paper presents the analysis and discussion of the off-site localization competition track, which took place during the Seventh International Conference on Indoor Positioning and Indoor Navigation (IPIN 2016). Five international teams proposed different strategies for smartphone-based indoor positioning using the same reference data. The competitors were provided with several smartphone-collected signal datasets, some of which were used for training (known trajectories), and others for evaluating (unknown trajectories). The competition permits a coherent evaluation method of the competitors' estimations, where inside information to fine-tune their systems is not offered, and thus provides, in our opinion, a good starting point to introduce a fair comparison between the smartphone-based systems found in the literature. The methodology, experience, feedback from competitors and future working lines are described. <s> BIB005
To examine the effects of the positioning approach, experimental work was necessary. To save on costs and time, there are lots of resources available online. Collecting data is always the first step of indoor positioning, which is important but cost intensive. Thanks to the researchers, there are lots of public databases available on RSS: The uiuc/uim dataset: a set of Bluetooth and Wi-Fi traces collected by 28 Google Android phone users (staff, faculties, grads, and undergrads at the University of Illinois) for three weeks in March 2010. Each UIM experimental phone contained Bluetooth and a Wi-Fi scanners to capture both Bluetooth MAC addresses and Wi-Fi access point MAC addresses in the proximity of the phone [85] . The kth/rss dataset: This dataset contains the RSS (Radio Signal Strength) data collected with a mobile robot in two environments: indoor (KTH) and outdoor (Dortmund). RSSI metric was used to collect the RSS data in terms of dBm. The mobile robot location was recorded using its odometry (dead reckoning)  . Smartphone Dataset for Human Activity Recognition (HAR) in Ambient Assisted Living (AAL) Data Set: This dataset was used to improve the accuracy of our HAR algorithms applied in a social connectedness experiment in the domain of Ambient Assisted Living. The dataset was collected from the in-built accelerometer and gyroscope of a smart phone worn around the waists of the participants. The data was collected from 30 participants within the age range of 22-79 years. Each activity (standing, sitting, laying, walking, walking upstairs, walking downstairs) was performed for 60 s, and the tri-axial linear acceleration and tri-axial angular velocity were collected at a constant rate of 50 Hz BIB001 . UJIIndoorLoc Data Set: The UJIIndoorLoc database covers three buildings of Universitat Jaume I with four or more floors and almost 110.000 m 2 . It was created in 2013 by means of more than 20 different users and 25 Android devices. The database consists of 19,937 training/reference records and 1111 validation/test records. The 529 attributes contain the Wi-Fi fingerprint, the coordinates where it was taken, and other useful information BIB003 . UJIIndoorLoc-Mag Data Set: The UJIIndoorLoc-Mag database can be used to compare magnetic field-based indoor localization methods. It consists of 270 continuous samples for training and 11 for testing. Each sample comprises a set of discrete captures taken along a corridor (or an intersection) with a period of 0.1 s. In total, there are almost 40,000 discrete captures, where each one contains features obtained from the magnetometer, the accelerometer, and the orientation sensor of the device  . There BIB005 , and the EVARILOS project and competition BIB004 . Besides,  BIB002 introduced the benchmarking tools for indoor positioning solutions.