Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Received Signal Strength-Based Positioning Technology (RSS) <s> The continuing proliferation of handheld computing devices offers a new platform for mobile computing applications that could enrich our experience of the world around us. Yet many questions about realizing this vision remain open. We have been investigating these questions at the University of California, San Diego, through the ActiveCampus Project. ActiveCampus explores technologies that can enrich the learning community in the midst of these changes. It explores wireless context-aware computing as a means to enhance the "learning community" experience of a large urban university. We have tested two applications: ActiveClass supports classroom activities, such as asking questions during a lecture, and ActiveCampus Explorer supports context-aware activities, such as instant messaging and location-aware maps annotated with dynamic hyperlinked information. <s> BIB001 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Received Signal Strength-Based Positioning Technology (RSS) <s> Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented. <s> BIB002 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Received Signal Strength-Based Positioning Technology (RSS) <s> Wi-Fi- and smartphone-based positioning technologies are playing a more and more important role in location-based service industries due to the rapid development of the smartphone market. However, the low positioning accuracy of these technologies is still an issue for indoor positioning. To address this problem, a new method for improving the indoor positioning accuracy was developed. The new method initially used the nearest neighbour (NN) algorithm of the fingerprinting method to identify the initial position estimate of the smartphone user. Then two distance correction values in two roughly perpendicular directions were calculated by the path loss model based on the two signal strength indicator values observed. The systematic error from the path loss model were eliminated by differencing two model-derived distances from the same access point. The new method was tested and the results compared and assessed against that of the commercial Ekahau RTLS system and the NN algorithm. The preliminary results ... <s> BIB003
The RSS-based positioning technology utilizes the strength of the received signal to obtain the users' position. This method does not require an additional positioning device. This method has attracted immense attention due to its high accuracy, low-cost, and low-power consumption since it was first developed. In general, there are three kinds of the method BIB002 : trilateration, similarity perception, and scene analysis, as shown in Figure 2 . (1) Trilateration Trilateration is a typical form of positioning technology which has been extensively studied and applied in the process of indoor positioning technology development. Researchers combined a triangular positioning method with the TSARS positioning technology, which has also reached a certain effect. Due to the limitations of the above-mentioned techniques and the advantages of signal strength (RSS), the triangular positioning technology based on RSS has been widely studied. The trilateration method uses three or more APs to send signals received by the mobile devices and converts them into spatial distance which is used as the radius of circles (whose center is AP) and the intersection of the three circles is the measured user location, as shown in Figure 3 . Since the complexity of indoor space can have a great impact on the RF signal, when one converts the signal strength to spatial distance, it can inevitably produce errors. To reduce the error, researchers have suggested a variety of methods to assist trilateration. Among them, the path loss model to estimate the space position of APs is the most widely used. There are two kinds of indoor path loss model  : the empirical path loss model, and the classic logarithmic model. Based on these two types, researchers have greatly improved the accuracy of trilateration in combination with other methods. In 2013, Bai et al. BIB003 proposed a method combined with the KNN (K-Nearest Neighbor) algorithm and fingerprint positioning technology; the triangular positioning method was based on the classic path loss model where they found that the positioning accuracy of their method increased 0.5 m compared with the Ekahau real-time positioning system BIB003 . The Active Campus system Griswold et al. BIB001 (1) Trilateration Trilateration is a typical form of positioning technology which has been extensively studied and applied in the process of indoor positioning technology development. Researchers combined a triangular positioning method with the TSARS positioning technology, which has also reached a certain effect. Due to the limitations of the above-mentioned techniques and the advantages of signal strength (RSS), the triangular positioning technology based on RSS has been widely studied. The trilateration method uses three or more APs to send signals received by the mobile devices and converts them into spatial distance which is used as the radius of circles (whose center is AP) and the intersection of the three circles is the measured user location, as shown in Figure 3 . (1) Trilateration Trilateration is a typical form of positioning technology which has been extensively studied and applied in the process of indoor positioning technology development. Researchers combined a triangular positioning method with the TSARS positioning technology, which has also reached a certain effect. Due to the limitations of the above-mentioned techniques and the advantages of signal strength (RSS), the triangular positioning technology based on RSS has been widely studied. The trilateration method uses three or more APs to send signals received by the mobile devices and converts them into spatial distance which is used as the radius of circles (whose center is AP) and the intersection of the three circles is the measured user location, as shown in Figure 3 . Since the complexity of indoor space can have a great impact on the RF signal, when one converts the signal strength to spatial distance, it can inevitably produce errors. To reduce the error, researchers have suggested a variety of methods to assist trilateration. Among them, the path loss model to estimate the space position of APs is the most widely used. There are two kinds of indoor path loss model  : the empirical path loss model, and the classic logarithmic model. Based on these two types, researchers have greatly improved the accuracy of trilateration in combination with other methods. In 2013, Bai et al. BIB003 proposed a method combined with the KNN (K-Nearest Neighbor) algorithm and fingerprint positioning technology; the triangular positioning method was based on the classic path loss model where they found that the positioning accuracy of their method increased 0.5 m compared with the Ekahau real-time positioning system BIB003 . The Active Campus system Griswold et al. BIB001 Since the complexity of indoor space can have a great impact on the RF signal, when one converts the signal strength to spatial distance, it can inevitably produce errors. To reduce the error, researchers have suggested a variety of methods to assist trilateration. Among them, the path loss model to estimate the space position of APs is the most widely used. There are two kinds of indoor path loss model  : the empirical path loss model, and the classic logarithmic model. Based on these two types, researchers have greatly improved the accuracy of trilateration in combination with other methods. In 2013, Bai et al. BIB003 proposed a method combined with the KNN (K-Nearest Neighbor) algorithm and fingerprint positioning technology; the triangular positioning method was based on the classic path loss model where they found that the positioning accuracy of their method increased 0.5 m compared with the Ekahau real-time positioning system BIB003 . The Active Campus system Griswold et al. BIB001 Due to the need to obtain precise AP positions and the use of the accurate path loss model, the triangular positioning method is less practical, difficult to locate, and has low positioning accuracy. (2) Approximate Perception The approximation perception (AP) method is a relatively simple method  , which uses the strongest base station as the positioning criterion. When the user communicates with a wireless device via Wi-Fi, the AP (which the device is connected to) will be viewed as user position. Since this method depends heavily on AP positions and has low positioning accuracy, researchers used an antenna cluster at a known position to improve positioning accuracy BIB002 . In addition to the antenna cluster, Cell-ID-which is generally used in the cellular mobile network-can be used to locate by enhancing the spatial property and strength of a Wi-Fi signal. Cell-ID divides indoor space into cells with a unique ID through signal strength received from the base station. Approximate perception is simple, but the method heavily relies on the base station location and coverage areas. In addition, this method has a low accuracy of around 100 m.