The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

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Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

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Application of WiFi-based indoor positioning system for labor tracking at construction sites: A case study in Guangzhou MTR

An indoor tracking and navigation system based on measurements of received signal strength (RSS) in wireless local area network (WLAN) is proposed. In the system, the location determination problem is solved by first applying a proximity constraint to limit the distance between a coarse estimate of the current position and a previous estimate. Then, a Compressive Sensing-based (CS--based) positioning scheme, proposed in our previous work , , is applied to obtain a refined position estimate. The refined estimate is used with a map-adaptive Kalman filter, which assumes a linear motion between intersections on a map that describes the user's path, to obtain a more robust position estimate. Experimental results with the system that is implemented on a PDA with limited resources (HP iPAQ hx2750 PDA) show that the proposed tracking system outperforms the widely used traditional positioning and tracking systems. Meanwhile, the tracking system leads to 12.6 percent reduction in the mean position error compared to the CS-based stationary positioning system when three APs are used. A navigation module that is integrated with the tracking system provides users with instructions to guide them to predefined destinations. Thirty visually impaired subjects from the Canadian National Institute for the Blind (CNIB) were invited to further evaluate the performance of the navigation system. Testing results suggest that the proposed system can be used to guide visually impaired subjects to their desired destinations.

Indoor Tracking and Navigation Using Received Signal Strength and Compressive Sensing on a Mobile Device

Current WiFi positioning systems (WPSs) require databases – such as locations of WiFi access points and propagation parameters, or a radio map – to assist with positioning. Typically, procedures for building such databases are time-consuming and labour-intensive. In this paper, two autonomous crowdsourcing systems are proposed to build the databases on handheld devices by using our designed algorithms and an inertial navigation solution from a Trusted Portable Navigator (T-PN). The proposed systems, running on smartphones, build and update the database autonomously and adaptively to account for the dynamic environment. To evaluate the performance of automatically generated databases, two improved WiFi positioning schemes (fingerprinting and trilateration) corresponding to these two database building systems, are also discussed. The main contribution of the paper is the proposal of two crowdsourcing-based WPSs that eliminate the various limitations of current crowdsourcing-based systems which (a) require a floor plan or GPS, (b) are suitable only for specific indoor environments, and (c) implement a simple MEMS-based sensors’ solution. In addition, these two WPSs are evaluated and compared through field tests. Results in different test scenarios show that average positioning errors of both proposed systems are all less than 5.75 m.

Evaluation of Two WiFi Positioning Systems Based on Autonomous Crowdsourcing of Handheld Devices for Indoor Navigation

Internet of Things (IoT) is a novel design paradigm, intended as a network of billions to trillions of tiny sensors communicating with each other to offer innovative solutions to real time problems. These sensors form a network named as wireless sensor networks (WSNs) to monitor physical environment and disseminate collected data back to the base station through multiple hops. WSN has the capability to collect and report data for a specific application. The location information plays an important role for various wireless sensor network applications. A majority of the applications are related to location-based services. The development of sensor technology, processing techniques, and communication systems give rise to a development of the smart sensor for the adaptive and innovative application. So a single localization technique is not adequate for all application. In this paper, a recent extensive analysis of localization techniques and hierarchical taxonomy and their applications in the different context is presented. This taxonomy of the localization technique is classified based on presence of offline training in localization, namely self-determining and training dependent approaches. Finally, various open research issues related to localization schemes for IoT are compared and various directions for future research are proposed.

Location of Things (LoT): A Review and Taxonomy of Sensors Localization in IoT Infrastructure

A WLAN (Wireless Local Area Network) based Extended Kalman Filter (EKF) method for indoor positioning is introduced in this paper. WLAN based indoor positioning is more economical than other methods because it does not require any special equipment dedicated to positioning. The most popular technique used for indoor positioning is the fingerprinting method, but the EKF method is easier to deploy because, unlike fingerprinting, it does not require a time consuming off-line phase. This paper also provides experimental comparisons of our EKF method with other indoor positioning methods.

Extended Kalman Filter for wireless LAN based indoor positioning

Location Based Service (LBS) cannot be realized unless the location of the user is available. For indoor LBS, indoor positioning must be utilized and many researchers have been working on indoor positioning and tracking. For example, Extended Kalman filter (EKF) was exploited in Bluetooth based indoor positioning. Nowadays, WLAN (Wireless Local Area Network) is available virtually everywhere. Thus, WLAN based indoor positioning and tracking is more economical than Bluetooth based ones. This paper proposes a new WLAN based EKF indoor tracking method by extending existing Bluetooth based EKF positioning method. After analyzing the experimental results of it, we modified it to use K-NN method in the measurement stage of it. Then we propose to further improve the accuracy of indoor tracking by adjusting the parameter values referring to the map information. Experimental results comparing our method with other previous methods are discussed.

Improvement of Kalman filters for WLAN based indoor tracking

Location-based service is one of the most popular buzzwords in the field of U-cities. Positioning a user is an essential ingredient of a location-based system in a U-city. For outdoor positioning, GPS based practical solutions have been introduced. However, the measurement error of GPS is too big for it to be used for U-campus services, because the size of a campus is smaller than that of a city. We propose the Relative-Interpolation Method to improve the accuracy of outdoor positioning. However, indoor positioning is also necessary for a U-campus because the GPS signal is not available inside buildings. For indoor positioning, various systems including Cricket, Active Badge, and so on have been introduced. These methods require special equipment dedicated to positioning. Our method does not require such equipment because it determines the user's position based on the received signal strength indicators (RSSIs) from access points (AP) which are already installed for WLAN. The algorithm we use for indoor positioning is a kind of fingerprinting method. However, our algorithm builds a decision tree instead of a look-up table in the off-line phase. Therefore, the proposed method is faster than the existing indoor positioning methods in the real-time phase. We integrated our indoor and outdoor positioning methods and implemented a prototype indoor-outdoor positioning system on a laptop. The experimental results are discussed in this paper. In implementing the prototype, we also implemented a C# library function which can be used to read the RSSIs from the APs.

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Implementation of a Prototype Positioning System for LBS on U-campus

Extended Kalman filter (EKF), which has long been used in the field of GPS positioning, has recently been used for indoor positioning, introduced extended Kalman filter (EKF) indoor positioning method based on Bluetooth signal strength However, the error range of the EKF is so large when it was applied in WLAN tracking that we modified it to use K-NN method for the measurement of the EKF Furthermore, this paper proposes an idea of improving accuracy of EKF tracking by adjusting parameter values referring to map information Experimental results comparing our method with other previous methods are discussed

Utilizing Map Information for WLAN-Based Kalman Filter Indoor Tracking

LBS (Location Based Service) cannot be realized unless we can obtain the user's current location. Therefore, in order to realize indoor LBS, many researchers have been working on WLAN (Wireless Local Area Network) based indoor positioning and tracking. Meanwhile, Kalman filter has been widely used in the field of GPS based outdoor user tracking. The main purpose of this paper is proposing an extended Kalman filter method for indoor tracking. Our experimental results show that Kalman filter can be used to improve the accuracy of the measured tracks and the track can be further improved by making use of the map information

Seunghwan Jeong

Papers

Extended Kalman Filter for wireless LAN based indoor positioning

Improvement of Kalman filters for WLAN based indoor tracking

Implementation of a Prototype Positioning System for LBS on U-campus

Utilizing Map Information for WLAN-Based Kalman Filter Indoor Tracking

Wireless LAN-Based User Tracking Method and Experiment for Location-Based Services