Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Introduction <s> The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy. <s> BIB001 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Introduction <s> In this paper, a new indoor positioning system based on incident angle measurement of infrared lights has been suggested. Though there have been various researches on indoor positioning methods using vision sensor or ultrasonic sensor, they have not only advantages, but also disadvantages. To minimize the disadvantages they have, this new method using incident angle of infrared light has been invented. In a new positioning system, there are three infrared emitters on fixed known positions. An incident angle sensor measures the angle differences between each two emitters. Measured angle differences determine a position. This method is available only inside the triangle which is composed of three emitters. Mathematical problems to determine the position with angle differences and position information of emitters has been solved. To solve the non-linear equations without prior position information, iterative linearization process has been used. Simulations and experiments have been implemented to show the performance of this new positioning system. The results of simulation were good. Since there existed problems of noise and signal conditioning, the experimented has been implemented in limited area. But the results were acceptable. This new positioning method can be applied to any indoor systems that need absolute position informations. <s> BIB002 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Introduction <s> We present the design and implementation of the Horus WLAN location determination system. The design of the Horus system aims at satisfying two goals: high accuracy and low computational requirements. The Horus system identifies different causes for the wireless channel variations and addresses them to achieve its high accuracy. It uses location-clustering techniques to reduce the computational requirements of the algorithm. The lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients.We discuss the different components of the Horus system and its implementation under two different operating systems and evaluate the performance of the Horus system on two testbeds. Our results show that the Horus system achieves its goal. It has an error of less than 0.6 meter on the average and its computational requirements are more than an order of magnitude better than other WLAN location determination systems. Moreover, the techniques developed in the context of the Horus system are general and can be applied to other WLAN location determination systems to enhance their accuracy. We also report lessons learned from experimenting with the Horus system and provide directions for future work. <s> BIB003 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Introduction <s> To verify the validity of our previously reported autonomous indoor localization system in an actual environment, we implemented it in a wireless sensor network based on the ZigBee standard. The system automatically estimates the distance between sensor nodes by measuring the RSSI (received signal strength indicator) at an appropriate number of sensor nodes. Through experiments, we clarified the validity of our data collection and position estimation techniques. The results show that when the deployment density of sensor nodes was set to 0.27 nodes/ , the position estimation error was reduced to 1.5-2 m. <s> BIB004 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Introduction <s> Ultrasonic location systems are a popular solution for the provision of fine-grained indoor positioning data. Applications include enhanced routing for wireless networks, computer-aided navigation, and location-sensitive device behavior. However, current ultrasonic location systems suffer from limitations due to their use of narrowband transducers, This paper investigates the use of broadband ultrasound for indoor positioning systems. Broadband ultrasonic transmitter and receiver units have been developed and characterized. The utilization of these units to construct two positioning systems with different architectures serves to highlight and affirm the concrete, practical benefits of broadband ultrasound for locating people and devices indoors. <s> BIB005 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Introduction <s> This paper explores the possibility to design an indoor ultra wideband (UWB) ranging and positioning system using the received signal strength (RSS). Due to the extremely large bandwidths, the effects of small scale fading are reduced to the level where the knowledge of the path loss model (PLM) can be employed for accurate and reliable distance estimation. This approach enables trilateration based position estimation while significantly reducing the synchronization effort. A limited number of measurements is necessary in order to calibrate the PLM parameters but no extensive database of measurements is required, such as in fingerprinting methods. Based on simulated UWB channels, the effects of uncertainties in the PLM parameters on the estimated distance are characterized. Data from a UWB measurement campaign in indoor line-of-sight (LOS) scenarios are used to verify the performance of such a system. <s> BIB006 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Introduction <s> Advances in mobile technologies and devices has changed the way users interact with devices and other users. These new interaction methods and services are offered by the help of intelligent sensing capabilities, using context, location and motion sensors. However, indoor location sensing is mostly achieved by utilizing radio signal (Wi-Fi, Bluetooth, GSM etc.) and nearest neighbor identification. The most common algorithm adopted for Received Signal Strength (RSS)-based location sensing is K Nearest Neighbor (KNN), which calculates K nearest neighboring points to mobile users (MUs). Accordingly, in this paper, we aim to improve the KNN algorithm by enhancing the neighboring point selection by applying k-means clustering approach. In the proposed method, k-means clustering algorithm groups nearest neighbors according to their distance to mobile user. Then the closest group to the mobile user is used to calculate the MU's location. The evaluation results indicate that the performance of clustered KNN is closely tied to the number of clusters, number of neighbors to be clustered and the initiation of the center points in k-mean algorithm. <s> BIB007 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Introduction <s> This paper discusses low-cost 3D indoor positioning with Bluetooth smart device and least square methods. 3D indoor location has become more and more attractive and it hasn't been well resolved. Almost each smart phone has a Bluetooth component and it can be used for indoor positioning and navigation in the nature of things. Least square algorithms are the powerful tools for linear and nonlinear parameters estimation. Various linear and nonlinear least square methods and their theoretical basics and application performance for indoor positioning have been studied. Simulation and hardware experiments results prove that nonlinear least square method is suitable for parameters estimation of Bluetooth signal propagation, and generalized least square method has better performance than total least square methods. Simulation and hardware experiments results also show that proposed method has the advantages of low cost, lost power consumption, perfect availability and high location accuracy. <s> BIB008 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Introduction <s> Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. Considering user movements in a building, originally separated RSS fingerprints are geographically connected by user moving paths of locations where they are recorded, and they consequently form a high dimension fingerprint space, in which the distances among fingerprints are preserved. The fingerprint space is then automatically mapped to the floor plan in a stress-free form, which results in fingerprints labeled with physical locations. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1,600 m $^2$ , and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey. <s> BIB009 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Introduction <s> In the past decade, the research community has been dedicating considerable effort into indoor positioning systems based on Wi-Fi fingerprinting techniques, mainly due to their capability to exploit existing infrastructures. Crowdsourcing approaches, also known as organic, have been proposed recently to address the problem of creating and maintaining the corresponding radio maps. In these organic systems, the users of the system build the radio map themselves while using it to estimate their own position/location. However, most of these collaborative methods, proposed by several authors, assume that all the users are honest and committed to contribute to a good quality radio map. In this paper we assess the quality of a radio map built collaboratively and propose a method to classify the credibility of individual contributions and the reputation of individual users. Experimental results are presented for an organic indoor location system that has been used by more than one hundred users over a period of around 12 months. <s> BIB010 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Introduction <s> Abstract. Over the last decade, harnessing the commercial potential of smart mobile devices in indoor environments has spurred interest in indoor mapping and navigation. Users experience indoor environments differently. For this reason navigational models have to be designed to adapt to a user’s personality, and to reflect as many cognitive maps as possible. This paper presents an extension of a previously proposed framework. In this extension the notion of placement is accounted for, thereby enabling one aspect of the ‘personalised indoor experience’. In the paper, firstly referential expressions are used as a tool to discuss the different ways of thinking of placement within indoor spaces. Next, placement is expressed in terms of the concept of Position, Location, Place and Area. Finally, the previously proposed framework is extended to include these concepts of placement. An example is provided of the use of the extended framework. Notable characteristics of the framework are: (1) Sub-spaces, resources and agents can simultaneously possess different types of placement, e.g., a person in a room can have an xyz position and a location defined by the room number. While these entities can simultaneously have different forms of placement, only one is dominant. (2) Sub-spaces, resources and agents are capable of possessing modifiers that alter their access and usage. (3) Sub-spaces inherit the modifiers of the resources or agents contained in them. (4) Unlike conventional navigational models which treat resources and obstacles as different types of entities, in the proposed framework there are only resources and whether a resource is an obstacle is determined by a modifier that determines whether a user can access the resource. The power of the framework is that it blends the geometry and topology of space, the influence of human activity within sub-spaces together with the different notions of placement in a way that is simple and yet very flexible. <s> BIB011 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Introduction <s> A growing number of sensors on smart mobile devices has led to rapid development of various mobile applications using location-based or context-aware services. Typically, outdoor localization techniques have relied on GPS or on cellular infrastructure support. While GPS gives high positioning accuracy, it can quickly deplete the battery on the device. On the other hand, base station based localization has low accuracy. In search of alternative techniques for outdoor localization, several approaches have explored the use of data gathered from other available sensors, like accelerometer, microphone, compass, and even daily patterns of usage, to identify unique signatures that can locate a device. Signatures, or fingerprints of an area, are hidden cues existing around a user's environment. However, under different operating scenarios, fingerprint-based localization techniques have variable performance in terms of accuracy, latency of detection, battery usage. The main contribution of this survey is to present a classification of existing fingerprint-based localization approaches which intelligently sense and match different clues from the environment for location identification. We describe how each fingerprinting technique works, followed by a review of the merits and demerits of the systems built based on these techniques. We conclude by identifying several improvements and application domain for fingerprinting based localization. <s> BIB012
The wide application of location-based services requires a high standard of positioning technology. Although outdoor positioning technology has matured, research into high accuracy positioning technology has gradually transferred from the outdoors to indoor BIB011 . This is mainly due to two reasons: first, according to the investigation and analysis in reference BIB011 , people generally spend about 80-90% of their daily time indoors. Furthermore, 70% of mobile phones are used indoors and 80% of data communications are performed indoors. Second, the above-mentioned data show that the demand for indoor mobile communication is very strong. Nevertheless, function areas in indoor spaces-especially large indoor public spaces such as airports, plazas, hotels, and so on-become more complicated and larger, for example, restaurants, cinemas, jewelers and many other kinds of stores may be integrated on only one floor level in a plaza, and so they may prove difficult for customers to find interesting areas. Therefore, from the above-mentioned viewpoints, indoor location-based services are of huge market potential. During the last decade, indoor positioning technology has rapidly developed, with many new emergent technologies and methods. From the RADAR system developed by Microsoft Research Asia BIB001 and the Horus system put forward by the University of Maryland BIB003 , to the LIFS system proposed by Tsinghua University BIB009 and the WHERE@UM system BIB010 created by University of Minho. As positioning results are becoming more accurate, costs are similarly reduced. Indoor positioning technologies can be mainly divided into two fields, one based on a 2D model, and the other based on a 3D model. The positioning technologies based on a 2D model mainly use Bluetooth BIB008 , ZigBee BIB004 , and Wi-Fi as positioning signals, and combine the signal intensity or space-time attributes with positioning algorithms. Infrared BIB002 , ultra-wideband (UWB) BIB006 , and ultrasonic BIB005 are used for positioning based on a 3D model. Compared with 3D model-based technology, the hardware used in 2D model-based positioning technology is much cheaper than Wi-Fi routers, which can easily bring down costs, and the network it requires has already been widely distributed. Based on its advantages, 2D model-based positioning technologies are widely used around the world. In contrast, 3D model-based positioning technologies mostly use TOA (Time of Arrival method), AOA (Angle of Arrival method) and other positioning methods based on time and space attributes. These types of technologies require an additional hardware platform, which greatly increases costs, but positioning accuracy is higher. As the positioning results are maintained at a decimeter level, the technology also has considerable applications in specific fields. The fingerprint positioning technology based on Wi-Fi is one of the most popular positioning technologies based on 2D modeling, and has become an area of indoor location research as it can be used in both outdoor and indoor positioning. For outdoor positioning as per reference BIB012 , outdoor fingerprint positioning technology mainly uses three types of fingerprint. The first type is a visual fingerprint like image; the second type is a motion fingerprint, which uses motion sensors such as accelerometers, electronic compasses, etc.; and the third type is a signal fingerprint like RSSI (Received Signal strength indication). In comparison, indoor fingerprint positioning technology mainly uses signal fingerprint and uses the received signal strength of every location (as fingerprints stored in the fingerprint database) to match the signal strength measured at users' locations to achieve positioning. This paper focuses on the fingerprint positioning technology based on Wi-Fi, as shown in Figure 1 (which is based on the Figure 1 in reference BIB007 ). Indoor positioning technologies can be mainly divided into two fields, one based on a 2D model, and the other based on a 3D model. The positioning technologies based on a 2D model mainly use Bluetooth BIB008 , ZigBee BIB004 , and Wi-Fi as positioning signals, and combine the signal intensity or spacetime attributes with positioning algorithms. Infrared BIB002 , ultra-wideband (UWB) BIB006 , and ultrasonic BIB005 are used for positioning based on a 3D model. Compared with 3D model-based technology, the hardware used in 2D model-based positioning technology is much cheaper than Wi-Fi routers, which can easily bring down costs, and the network it requires has already been widely distributed. Based on its advantages, 2D model-based positioning technologies are widely used around the world. In contrast, 3D model-based positioning technologies mostly use TOA (Time of Arrival method), AOA (Angle of Arrival method) and other positioning methods based on time and space attributes. These types of technologies require an additional hardware platform, which greatly increases costs, but positioning accuracy is higher. As the positioning results are maintained at a decimeter level, the technology also has considerable applications in specific fields. The fingerprint positioning technology based on Wi-Fi is one of the most popular positioning technologies based on 2D modeling, and has become an area of indoor location research as it can be used in both outdoor and indoor positioning. For outdoor positioning as per reference BIB012 , outdoor fingerprint positioning technology mainly uses three types of fingerprint. The first type is a visual fingerprint like image; the second type is a motion fingerprint, which uses motion sensors such as accelerometers, electronic compasses, etc.; and the third type is a signal fingerprint like RSSI (Received Signal strength indication). In comparison, indoor fingerprint positioning technology mainly uses signal fingerprint and uses the received signal strength of every location (as fingerprints stored in the fingerprint database) to match the signal strength measured at users' locations to achieve positioning. This paper focuses on the fingerprint positioning technology based on Wi-Fi, as shown in Figure 1 (which is based on the Figure 1 in reference BIB007 ). The rest of this paper is organized as follows. Section 2 makes a comprehensive introduction to the positioning technology based on Wi-Fi; Section 3 mainly focuses on fingerprint positioning technology based on Wi-Fi; Section 4 looks at auxiliary positioning technology with inertial sensors; Section 5 mainly introduces some public databases and competitions in this field; and Section 6 concludes the paper and addresses directions in future research. The rest of this paper is organized as follows. Section 2 makes a comprehensive introduction to the positioning technology based on Wi-Fi; Section 3 mainly focuses on fingerprint positioning technology based on Wi-Fi; Section 4 looks at auxiliary positioning technology with inertial sensors; Section 5 mainly introduces some public databases and competitions in this field; and Section 6 concludes the paper and addresses directions in future research.