스마트 기기의 생활화와 증강현실 활용 증가로 실내 위치 인식 시스템의 수요가 급증함에 따라, BLE(Bluetooth Lower Energy) 비콘 그리고 UWB(Ultra Wide Band) 등을 이용한 실내 측위 시스템이 개발되고 있다. 본 논문에서는 BLE Beacon을 기반으로 RSSI(Received Signal Strength Indicator)를 이용한 삼변측량(Trilater...

기존의 전파에 의존한 실내 위치측위 시스템의 한계점 발생으로 실내환경에서 정확도 향상을 위한 새로운 방법의 도입이 필요한 시점이다. 최근 생태계 모방 기술이 미래의 핵심기술이 되었고, 이에 따라 귀소본능을 가진 동물들이 지구자기장을 탐색하여 생체자석으로 위치친지에 사용하는 점을 적용한 정확한 위치 측위 방법을 연구하였다. 실내 위치측위를 위한 새로운 자원인 지구자기장의 적용 가능성을 확인하기 위해 건물구조, 구성재료를 구분하고 실제 자기장센서를 탑재할 수 있는 구조물과 데이터수집 모듈을 설계한 뒤, Fingerprint 기법의 위치측위시스템을 구성하여 위치측위 자원으로서의 지구자기장의 적용 가능성을 연구하였다. 위치측위 시스템 성능 평가에서 기존의 무선랜이 설치된 건물에서는 지구자기장 세기 기반의 위치측위 시스템이 무선랜 기반 위치측위 시스템과 유사하거나 약 20% 성능이 높이 나타났다. 이와같이 실내 위치측위를 위한 인프라 설치가 되어 있지 않는 환경에서는 지구자기장이라는 고유의 지구자원으로 실내 위치측위가 가능하다.

電波を利用した無線局端末の位置推定は様々なア プリケーションに利用でき,また,ユビキタスネット ワークシステムのような新たなサービスにおいても重 要であると考えられているため最近注目を集めてい る [1]~[3].近年,携帯電話による緊急時の通報が増 加する中,米国の連邦通信委員会(FCC)が緊急サー ビスの向上のため,緊急時の携帯電話利用者の居場所 を特定できる機能 Enhanced 911(E-911)について 制定を行った.例として緊急時の発信に対し,移動局 の位置を半径 125 m 以内に 67%の確率で報告するこ とを求めている.このことも位置推定が注目される理 由の一つとして考えられる [2], [4]. 電波を利用した位置推定方法として Global Positioning System(GPS)が挙げられる.GPS は衛星 からの電波到来時間の情報に基づいて位置を推定して いる.近年では GPS のデバイスも安価になり,最も 普及し様々な分野で利用されている位置推定システム といっても過言ではない.しかしながら,GPSには専

v LIST OF TABLES xii LIST OF FIGURES xiii

of dissertation entitled: An Accurate and Robust Indoor Localization System, Submitted by Chan Chun Lun, Eddie for the degree of Doctor of Philosophy in the Department of Computing at The Hong Kong Polytechnic University in April 2010. A wireless tracking system such as the Global Positioning System (GPS) is the most effective in relatively open and flat outdoor environments but is much less effective in non-line-of-sight (NLOS) environments such as hilly, mountainous, or built-up areas. In recent years, IEEE 802.11 Wireless Local Area Networks (WLANs) have been widely deployed and are now fuelling a wide range of location-aware computing applications. The localization of devices in these networks (i.e., estimating their location) makes use of the Wi-Fi signal strength. There are two location-sensing techniques for indoor environments, propagation-based and location-fingerprinting techniques. Although these two techniques can locate WLAN-enabled devices, recent research on indoor localization system is still not satisfactory. There are five major problems that lead to inaccurate and low efficiency localization systems. First, signal overlaps and interferences seriously worsen the performance of localization system. Second, existing positioning algorithms suffer from high computational complexity of location estimation as a result of the burden of having to carry out many signal sampling, weighting, and Doctor of Philosophy Chan Chun Lun, Eddie 0790 iv filtering tasks. Third, existing WLAN infrastructures are often deployed in an ad-hoc, empirical and non-optimal configurations. Such unstructured approaches lead to poor resource utilization and inaccurate localization due to signal overlap. Fourth, there is a lack of analytical models that can be used as a framework for designing and deploying the positioning systems. Most existing models ignore radio signal properties and some assume the distribution of the RSS is Gaussian and pair wise. Such assumptions may ignore or distort the real behavior of RSS and lead to RSS analysis that is not accurate enough. Finally, a localization system should provide location-aware information according to the user’s needs. However, it is very difficult to retrieve location-aware information that matches the behavior of users and at the same time suits current conditions. An accurate, robust, scalable and cost-effective localization solution should have (1) a stable WLAN signal transmission, (2) an accurate positioning algorithm, (3) a structural WLAN infrastructure that can support intensive tracking, (4) a model that can visualize the WLAN signal distribution to prevent the occurrence of signal black spots and interference and finally (5) a query interpretation and information retrieval system that can provide location-aware information suitable to user behaviors and preferences. This thesis proposes an indoor localization framework that tackles each of these problems. First, it proposes a cell-based installation of wireless access points and suggests a channel assignment scheme to have a stable WLAN signal transmission. Second, it Doctor of Philosophy Chan Chun Lun, Eddie 0790 v makes use of the Newton Trust-Region algorithm and Kalman Filter to improve the accuracy of positioning algorithms. Third, it suggests the use of Fuzzy Logic and Topographic modeling to visualize the signal distribution in a 2-D and 3-D manner. Finally, it develops an agent-based module for retrieving location-aware information that can improve the speed of retrieval while maintaining or even improving the accuracy by making use of semantic information in the data to develop smaller training sets. A series of experiments was carried out to measure the performance of the proposed framework and contrast the result with existing approaches. The major findings are that the proposed framework could help engineers to save 50% of WLAN infrastructure resources (access points) while at the same time increasing the accuracy of localization by 20%. Experimental analysis also shows that channel interference usually obeys a right-skewed distribution and positioning accuracy is greatly affected by channel interference between access points. Finally, the proposed framework provides a quick reference and efficient analytical tool for improving the design of WLAN infrastructure. Doctor of Philosophy Chan Chun Lun, Eddie 0790 vi List of Publications Journal Papers [1] Eddie C. L. Chan, George Baciu and S.C. Mak, "Properties of Channel Interference for Wi-Fi Location Fingerprinting", IEEE Journal of Communications Software and Systems, vol. 6, no. 2, 2010 (JCOMSS) [2] Eddie C. L. Chan, George Baciu and S.C. Mak, "Cognitive Location-Aware Information Retrieval by Agent-based Semantic Matching", International Journal of Software Science and Computational Intelligence (IJCiNi), vol. 2, no. 3, pp.21-31, July-September, 2010 [3] Eddie C. L. Chan, George Baciu and S.C. Mak, "Using a Cell-based WLAN Infrastructure Design for Resource-effective and Accurate Positioning", IEEE Journal of Communications Software and Systems, vol. 5, no. 4, pp.117-127, 2009 (JCOMSS) [4] Eddie C. L. Chan, George Baciu and S.C. Mak, "Newton Trust Region Method for Wi-Fi Tracking", submitted to IEEE Transactions on Mobile Computing [5] Eddie C. L. Chan, George Baciu and S.C. Mak, “Wireless Signal and Information Tracking Using Fuzzy Logic”, submitted to Spring-Verlag Studies in Computational Intelligence. [6] Rong-Hua, Li Zhiping Luo, Eddie C. L. Chan, George Baciu, Zheng-Jun Zha, Jinhui Doctor of Philosophy Chan Chun Lun, Eddie 0790 vii Tang, Tat-seng Chua, “Equivalence Between Direct LDA and LDA/QR”, submitted to Pattern Recognition Journal Conference Papers [7] Eddie C. L. Chan, George Baciu and S.C. Mak, "Effect of Channel Interference on Positioning in Wireless Local Area Network", accepted by 6th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications. IEEE WiMOB 2010. Niagara Falls, Canada. [8] Eddie C. L. Chan, George Baciu and S.C. Mak, "Orientation-based Wi-Fi Positioning on the Google Nexus One", accepted by 6th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications. IEEE WiMOB 2010. Niagara Falls, Canada. [9] Rong-hua Li, Eddie, C. L. Chan, George Baciu, “DLDA and LDA/QR Equivalence Framework for Human Face Recognition”, accepted by 9th IEEE International Conference on Cognitive Informatics, IEEE ICCI 2010. Beijing, China. [10] Shuang Liang, Eddie C. L. Chan, George Baciu, Rong-Hua Li, “Cognitive Garment Design Interface Using User Behavior Tree Model”, accepted by 9th IEEE International Conference on Cognitive Informatics, IEEE ICCI 2010. Beijing, China. [11] Eddie C. L. Chan, George Baciu and S.C. Mak, "Wi-Fi Positioning based on Fourier Descriptors", IEEE International Conference on Communications and Mobile Doctor of Philosophy Chan Chun Lun, Eddie 0790 viii Computing 2010, vol. 3, pp.545-551. IEEE CMC 2010. Shenzhen, China [12] Shuang Liang, Rong-Hua Li, George Baciu, Eddie C. L. Chan and D. Zheng, "Partial Matching of Garment Panel Shapes with Dynamic Sketching Design", 1st ACM Augmented Human International Conference, article 16, pp. 1-6. AH 2010. Megève, France [13] Eddie C. L. Chan, George Baciu and S.C. Mak, "Using the Newton Trust-Region Method to Localize in WLAN Environment", 5th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, pp.363-369. IEEE WiMOB 2009. Marrakech, Morocco. [14] Eddie C. L. Chan, George Baciu and S.C. Mak, "Fuzzy Topographic Modeling in WLAN Tracking Analysis", International Conference on Fuzzy Computation, pp. 17-24. ICFC 2009. Madeira, Portugal. ** [15] Eddie C. L. Chan, George Baciu and S.C. Mak, "Resource-effective and Accurate WLAN Infrastructure Design and Localization Using a Cell-structure Framework", 5th IEEE International Conference on Wireless Communications, Network and Mobile Computing, volume 6, pp.9-15, 2009. Beijing, China. [16] Eddie C. L. Chan, George Baciu and S.C. Mak, "Cognitive Location-Aware Information Retrieval by Agent-based Semantic Matching", 8th IEEE International Conference on Cognitive Informatics, pp. 435-440 IEEE ICCI 2009, Hong Kong. Doctor of Philosophy Chan Chun Lun, Eddie 0790 ix [17] Eddie C. L. Chan, George Baciu and S.C. Mak, "Using Wi-Fi Signal Strength to Localize in Wireless Sensor Networks", IEEE International Conference on Communications and Mobile Computing, pp. 538-542. IEEE CMC 2009. Kunming, Yunnan, China. [18] Eddie C. L. Chan, George Baciu and S.C. Mak, "Wireless Tracking Analysis in Location Fingerprint", 4th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, pp.214-220. IEEE WiMOB 2008. Avignon, France. [19] Raymond S.T. Lee, Eddie C. L. Chan and Raymond Y. W. Mak, "iJADE Content Management System (CMS) An Intelligent Multi-agent Based Content Management System with Chaotic Copyright Protection Scheme, Knowledge-Based Intelligent Information and Engineering Systems", pp. 652-658, Springer-Verlag Berlin Heidelberg. 2006. Bournemouth, United Kingdom. [20] Eddie C. L. Chan and Raymond S.T. Lee, "iJADE reporter: An intelligent multi-agent based contextaware news reporting system", Knowledge-Based Intelligent Information and Engineering Systems, pp. 693-699, Springer-Verlag Berlin Heidelberg. 2005. Melbourne, Australia. Other Papers [21] George Baciu, Eddie C. L. Chan, "Where Virtual Mobility and Real Location-aware Doctor of Philosophy Chan Chun Lun, Eddie 0790 x Information Merge Using Apple's iPhone", HKSAR Information Technology Innovation Section e-Newsletter 2010, Issue 7, pp.1-4 [22] George Baciu, Eddie C. L. Chan, "Face Recognition Using DLDA and LDA/QR Equivalence Framework", HKSAR Information Technology Innovation Section e-Newsletter 2010, Issue 6, pp.5-8 [23] George Baciu, Eddie C. L. Chan, "Channel Interference Effects in Wi-Fi Positioning", HKSAR Information Technology Innovation S

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Work in indoor positioning so far broadly relies on either signal propagation models or location fingerprinting. The former approach has inherent modelling complexity as a result of intervening walls and movement in the environment which, impacts the accuracy of such models. The latter approach on the other hand, is acclaimed to give better accuracy. However, for it to be used, an added overhead of surveying history data of a calibration of every indoor environment is required. Moreover, if any of the mobile Access Points (APs) included in the surveyed history data is down for any reason, the result of the location fingerprinting approach is impacted. This work proposes an indoor location determination approach that uses Fuzzy Weighted Aggregation of Received Signal Strengths (RSS) of Wi-Fi signals with Compensated Weighted Attenuation Factor (CWAF) in the form of fuzzy weighted signal quality and noise. The results are compared with locations away from APs with actual physical measurement in the environmental location to verify accuracy. The performance of the proposed algorithm shows that if the normalized weighted signal strength is properly compensated with weighted signal quality and noise, the approach offers a more computationally efficient positioning with adequate accuracy for indoor localization.

Within wireless sensor networks, the location of the sensor can be a critical attribute of the data sensed. A device knowing its relative position to the other devices in the wireless network or its absolute position with respect to a map increases the value of the data sensed. Sensing the precise location of wireless devices has been a topic of serious study for some time. A number of methods and techniques have been suggested, using various physical attributes (IR, acoustic, time of flight); however, we have chosen to emphasize received signal strength (RSS), which is simple to implement and uses the signal strength sampling capability used by the network layer of a wireless network. Using algorithms such as fingerprinting and multilateration, a reasonably accurate position estimate can be established depending on the density of the network devices and the environment in which they must operate. We discuss the previous work in RSS localization and describe the limitations of these algorithms from a practical and an experimental perspective. We will conclude with a discussion of our work using these algorithms in 3 wireless network test beds constructed in an industrial refinery and an office environment.

With the widespread use of location-based services and the development of localization systems, user’s locations and even sensitive information can be easily accessed by some untrusted entities, which means privacy concerns should be taken seriously. In this paper, we propose a differential privacy framework to preserve users’ location privacy and provide location-based services. We propose the metrics of location privacy, service quality and differential privacy to introduce a location privacy preserving mechanism, which can help users find the tradeoff or optimal strategy between location privacy and service quality. In addition, we design an adversary model to infer users’ true locations, which can be used by application service providers to improve service quality. Finally, we present simulation results and analyze the performance of our proposed system.

With the rise of new analytical methods to track and predict various aspects of our lives, gathering data is as important as ever. The way an object moves indoors is an important aspect in terms of crowd flow management, retail marketing, and security fields. Wi-Fi has become a ubiquitous standard with almost every indoor space having some form of Wi-Fi device. This existing infrastructure is put into service to track an object based on received signal strength values. Received signal strength values from different access points form a unique fingerprint which can be used to recognize a location on the map. In order to make the system adaptive to change and drift in fingerprints, deep learning methods are used to convert the system from a static to a dynamic system. The proposed neural network model has the error of 1.3 m for predicting the x-coordinate and 1.8 m for the y-coordinate in the given experimental setup.

With the remarkable advances in wireless networking and pervasive computing, there have been an increasing need to figure it into LBS (location-based services) applications. This paper proposes an adaptive positioning system for the mobile terminal (MT) localization using the radio propagation modeling (RPM) and Kalman filtering (KF) according to the measurements of signal-to-noise ratio (SNR) information between indoor IEEE 802.11b (WiFi) APs and reference points (Tags). Comparing with the conventional empirical method (such as the fingerprinting method), the adaptive RPM algorithm can perform on-line calibration in real dynamic environments and reduce the number of training points. Additionally, the KF algorithm is used to track the location of MTs, where the observation information is extracted from the empirical and RPM positioning methods. The numerical simulations and experimental results show that not only the calibrating and positioning method of the RPM algorithm can improve the accuracy of an indoor WiFi locating system and alleviate the phenomenon of aliasing in the signal space, but also the estimating and tracking method of the KF algorithm can overcome the problem of the aliasing and reduce the positioning error with smaller sampling time.

With the rapid development and wide deployment of wireless local area networks (WLANs), WLAN-based positioning system employing signal-strength-based technique has become an attractive solution for location estimation in indoor environment. In recent years, a number of such systems has been presented, and most of the systems use the common nearest neighbor in signal space (NNSS) algorithm. In this paper, we propose an enhancement to the NNSS algorithm. We analyze the enhancement to show its effectiveness. The performance of the enhanced NNSS algorithm is evaluated with different values of the parameters. Based on the performance evaluation and analysis, we recommend some guidelines on optimizing the parameters of our proposed enhanced algorithm.

With the proliferation of location based services (LBS), various indoor positioning techniques have been explored based on received signal strength (RSS). To improve performance, many hybrid or fusion approaches have been proposed in the literature. In this paper, a new fusion approach is proposed to achieve better positioning performance, with a focus on the optimal utilization of RSS measurements in wireless local area network (WLAN). First, a fusion architecture is developed to make use of multiple observations from the different positioning algorithms and by employing this architecture, more than 20 percent reduction in the mean distance error is achieved. Additionally, a novel online training method is employed to estimate the covariance of the observations to achieve further improvement.

With the proliferation of location based services (LBS), various indoor localization techniques have been explored based on received signal strength (RSS). To improve performance, many algorithms and techniques have been proposed in the literature. In this paper, a new RSS based statistical location algorithm is proposed to achieve better localization performance, with a focus on statistical analysis and reasoning in wireless local area network (WLAN). Experimental results demonstrate our proposed algorithm outperforms and more than 20 percent reduction in root mean square error (RMSE) has been achieved, through comparisons with some other existing algorithms. Moreover, it has also been verified that number of training locations can be reduced without too much performance degradation.

With the proliferation of location based services (LBS), various indoor localization systems have been proposed based on received signal strength (RSS). An acceptable localization performance is achieved by using fingerprinting-based methods. However, its performance is restricted by RSS deviation. This deviation is not well modelled and studied in past. With this motivation, we propose a novel RSS model to define this deviation based on orthogonal frequency dimensional multiplexing (OFDM). Unlike conventional random RSS fluctuation, this deviation cannot be eliminated by merely using sample average. Hence, we also propose a power-bias mitigation algorithm to improve the performance through eliminating part of the deviation. Simulation and experimental results demonstrate good performance gains are achieved by using the power-bias mitigation algorithm under various scenarios.

With the popularity of wireless local area networks (WLAN), Wi-Fi based indoor positioning techniques are widely used for indoor user localization. Most popular Wi-Fi positioning methods use the received signal strength indication (RSSI). Among available approaches, fingerprinting appears to be the most feasible method for positioning in the indoor environment [1]. This method estimates the position of an object and relies on training data from a set of reference points (RP) with known locations. Fingerprinting-based methods consist of two phases, namely the offline phase and the online phase. In the offline phase, the training data (i.e., RSSI) are collected at the RPs and used to build the database, which is often called the radio map. During the online phase, the online measurements are compared against the training data at every RP. The position of the RP whose training data most closely match the online data can be regarded as the estimated position of the object.

With the popularity of smart phones and wireless network, location-based services (LBSs) attract extensive attention. The demands on indoor positioning systems (IPS) for the WiFi deployed environments are especially high. In this paper, we categorize the WiFi-based IPSs into two types, namely Received signal strength (RSS)-based and Channel state information (CSI)-based, and compare the practical performances of several WiFi-based IPSs. For the RSS-based methods, we select fingerprinting, trilateration method, sequence-based localization (SBL), and multidimensional scaling (MDS)-based methods for comparison. For the CSI-based method, we choose the time-reversal (TR) algorithm and propose a new experimental scheme to measure its performance. The pros and cons of each algorithm are also discussed.

With the popularity of smart phones and the rapid development of wireless communication technologies, people tend to determine their indoor location in the complex indoor environment such as a library, exhibition hall and shopping malls using location-based services. However, in many scenarios, two-dimensional positioning system has been unable to meet the needs. In this paper, we research on the three-dimensional positioning system using location fingerprinting techniques, improve the algorithm based on K Nearest Neighborhood, and establish the three-dimensional positioning system. The results showed that our simulation model can achieve a more accurate result than two-dimensional models.

With the popularity of smart phones and the rapid development of wireless communication technologies, people tend to determine their indoor location in the complex indoor environment such as a library, exhibition hall and shopping malls using location-based services. However, in many scenarios, two-dimensional positioning system has been unable to meet the needs. In this paper, we research on the three-dimensional positioning system using location fingerprinting techniques, improve the algorithm based on K Nearest Neighborhood, and establish the three-dimensional positioning system. The results showed that our simulation model can achieve a more accurate result than two-dimensional models.

With the popularity of smart mobile devices, “context-aware” applications have attracted intense interest, for which location is one of the most essential contexts. Compared with outdoor localization, indoor localization has received much more attention from both academia and industry these days. Given the widespread use of WiFi hotspots, the received signal strength (RSS) fingerprint-based indoor localization technique is considered as a promising and practical solution because of its relatively high accuracy and low infrastructure cost. Inspired by our observation that sparsity is inherent to the WiFi signal, we present a new RSS fingerprint-based indoor localization approach, called SparseLoc. Through sparse representation of the fingerprints, SparseLoc can estimate a smart mobile device’s location with a small error most of the time. Although the correlation between neighboring fingerprints affects the localization accuracy, SparseLoc uses the similarity between principal components of fingerprints to alleviate this effect. Based on the empirical experiments, we demonstrate that SparseLoc improves the localization accuracy by over 25% compared with the existing WiFi signal-based localization methods.