The development in wireless technology, mobile smart devices and Internet of Things has gave birth to a booming era or the wireless indoor geolocation. This technology have been increasingly used within our daily life and help people to build up the tracking system which could be used by fulfillment centers and grocery stores. To achieve higher localization accuracy with wireless geolocation, we need a higher density of deployment which involves high deployment and maintenance cost. To balance the accuracy and the cost, people have begun using wireless localization employing inertial navigation system (INS) which provide speed and direction of movement. When we combine Radio Frequency (RF) localization with INS, we have a hybrid INS/RF localization system which can achieve high localization accuracy with low cost. In this thesis, we use accelerometers and magnetometers in an Android smart phone to build a hybrid INS/RF system and use two different technologies for RF localization: Radio Frequency Identification Device (RFID) and Wi-Fi. Using this system, we conducted measurements of the hybrid localization system and evaluate its performance. The specific contributions of the thesis are: (1)Empirical performance evaluation of the INS/RFID localization system. It relates the localization error to the number and position of RFID tags. (2)Model the effect of metallic objects on accuracy of magnetometer. The model shows the relation between direction error and distance to metallic component. (3)Model shadow fading in close proximity of RF transmitter. It builds a distance dependent shadow fading model. (4)Model based performance evaluation of hybrid localization. The test bench uses our models to simulate the hybrid localization data.

Large enterprises are often interested in tracking objects and people within buildings to improve resource allocation and occupant experience. Infrastructure-based indoor positioning systems (IIPS) can provide this service at low-cost by leveraging already deployed Wi-Fi infrastructure. Typically, IIPS perform localization and tracking of devices by measuring only Wi-Fi signals at wireless access points and do not rely on inertial sensor data at mobile devices (e.g., smartphones), which would require explicit user consent and sensing capabilities of the devices. Despite these advantages, building an economically viable cost-effective IIPS that can accurately and simultaneously track many devices over very large buildings is difficult due to three main challenges. First, Wi-Fi signal measurements are extremely noisy due to unpredictable multipath propagation and signal attenuation. Second, as the IIPS obtain measurements in a best effort manner without requiring any applications installed on a tracked device, the measurements are temporally sparse and non-periodic, which makes it difficult to exploit historical measurements. Third, the cost-effective IIPS have limited computational resources, in turn limiting scalability in terms of the number of simultaneously tracked devices. Prior approaches have narrowly focused on either improving the accuracy or reducing the complexity of localization algorithms. To compute the location at the current time step, they typically use only the latest explicit Wi-Fi measurements (e.g., signal strengths). The novelty of our approach lies in considering contexts of a device that can provide useful indications of the device's location. One such example of context is device motion. It indicates whether or not the device's location has changed. For a stationary device, the IIPS can either skip expensive device localization or aggregate noisy, temporally sparse location estimates to improve localization accuracy. Another example of context applicable to a moving device is a floor map that consists of pre-defined path segments that a user can take. The map can be leveraged to constrain noisy, temporally sparse location estimates on the paths. The thesis of this dissertation is that embedding context-aware capabilities in the IIPS enhances its performance in tracking many devices simultaneously and accurately. Specifically, we develop motion detection and map matching to show the benefits of leveraging two critical contexts: device motion and floor map. Providing motion detection and map matching is non-trivial in the IIPS where we must rely only on data from the Wi-Fi infrastructure. This thesis makes two contributions. First, we develop feature-based and deep learning-based motion detection models that exploit temporal patterns in Wi-Fi measurements across different access points to classify device motion in real time. Our extensive evaluations on datasets from real Wi-Fi deployments show that our motion detection models can detect device motion accurately. This, in turn, allows the IIPS to skip repeated location computation for stationary devices or improve the accuracy of localizing these devices. Second, we develop graph-based and image-based map matching models to exploit floor maps. The novelty of the graph-based approach lies in applying geometric and topological constraints to select which path segment to align the current location estimate. Our graph-based map matching can align a location estimate of a user device on the path taken by the user and close to the user's current location. The novelty of the image-based approach lies in representing for the first time, input data including location estimates and the floor map as 2D images. This novel representation enables the design, development, and application of encoder-decoder neural networks to exploit spatial relationships in input images to potentially improve location accuracy. In our evaluation, we show that the image-based approach can improve location accuracy with large simulated datasets, compared to the graph-based approach. Together, these contributions enable improvement of the IIPS in its ability to accurately and simultaneously track many devices over large buildings.

Wireless communications are ubiquitous nowadays, especially in the new era of Internet of Things (IoT). Most of IoT devices access the Internet via some kind of wireless connections. The major role of wireless signals is a type of communication medium. Besides that, taking advantage of the growing physical layer capabilities of wireless techniques, recent research has demonstrated the possibility of reusing wireless signals for both communication and sensing. The capability of wireless sensing and the ubiquitous availability of wireless signals make it possible to meet the rising demand of pervasive environment perception. Physical layer features including signal attributes and channel state information (CSI) can be used for the purpose of physical world sensing. This dissertation focuses on exploring the sensing capability of wireless signals. The research approach is to first take measurements from physical layer of wireless connections, and then develop various techniques to extract or infer information about the environment from the measurements, like the locations of signal sources, the motion of human body, etc. The research work in this dissertation makes three contributions. We start from wireless signal attributes analysis. Specifically, the cyclostationarity properties of wireless signals are studied. Taking WiFi signals as an example, we propose signal cyclostationarity models induced by WiFi Orthogonal Frequency Division Multiplexing (OFDM) structure including pilots, cyclic prefix, and preambles. The induced cyclic frequencies is then applied to the signal-selective direction estimation problem. Second, based on the analysis of wireless signal attributes, we design and implement a prototype of a single device system, named MobTrack, which can locate indoor interfering radios. The goal of designing MobTrack is to provide a lightweight, handhold system that can locate interfering radios with sub-meter accuracy with as few antennas as possible. With a small antenna array, the cost, complexity as well as size of this device are reduced. MobTrack is the first single device indoor interference localization system without the requirement of multiple pre-deployed access points (AP). Third, channel state information is studied in applications of human motion sensing. We design WiTalk, the first system which is able to do fine-grained motion sensing like leap reading on smartphones using the CSI dynamics generated by human movements. WiTalk proposes a new fine-grained human motion sensing technique with the distinct context-free feature. To achieve this goal using CSI, WiTalk generates CSI spectrograms using signal processing techniques and extracts features by calculating the contours of the CSI spectrograms. The proposed technique is verified in the application scenario of lip reading, where the fine-grained motion is the mouth movements. / Ph. D. / Wireless communications are ubiquitous nowadays, especially in the new era of Internet of Things (IoT). Most of IoT devices access the Internet via some kind of wireless connections. The major role of wireless signals is a type of communication medium. Besides that, taking advantage of the growing physical layer capabilities of wireless techniques, recent research has demonstrated the possibility of reusing wireless signals for both communication and sensing. The capability of wireless sensing and the ubiquitous availability of wireless signals make it possible to meet the rising demand of pervasive environment perception. Physical layer features including signal attributes and channel state information (CSI) can be used for the purpose of physical world sensing. This dissertation focuses on exploring the sensing capability of wireless signals. The research approach is to first take measurements from physical layer of wireless connections, and then develop various techniques to extract or infer information about the environment from the measurements, like the locations of signal sources, the motion of human body, etc. Based on the analysis to cyclostationary properties of wireless signals, we propose a new method for indoor interference source localization. We also design a fine-grained human motion detection system using channel state information, which can be applied to application scenarios like lip reading.

With the rapid growth of mobile, ubiquitous and wearable computing, location-based services become an indispensable part of mobile internet. These services rely on the geographical position of the mobile devices and provide location-dependent contents or services to users, such as location-based in- stant messaging, POI browsing, map navigation, and location-based virtual reality games. Most existing systems implement these location-based services by always storing and transmitting raw, plaintext GPS coordinates. However, location information is arguably a private asset of individual user, and the disclosure of such information could lead to severe privacy disclosure of other even more sensitive information, such as religion, sexuality, medical condition, or political affiliation. To address this issue, researchers have proposed a series of techniques to protect user location privacy against location-based service providers. How- ever, it is challenging to apply these theoretical and sophisticated techniques ii to practical location systems because of the computational or network over- head imposed on the mobile devices as well as the complexity of the secure protocols and algorithms for application developers. In this thesis, I will study two real-life privacy-preserving location systems and show how they can be adopted by developers with little security background. The rst is outdoor proximity detection that determines whether two users (or a user and an ob- ject) are within a given distance threshold. This is a fundamental service in many geo-social or map services. For example, \People nearby" in Wechat and QQ interconnect users because of their locality and/or mutual interests in some topics, such as food and movies. The second is indoor location mon- itoring and tracking. Wearable devices such as smart watch and bracelets continually broadcast Bluetooth Low Energy signals, which can be easily cap- tured by monitoring devices such as WiFi routers and Bluetooth scanners. As more and more wearable devices emerge, unauthorized monitoring and track- ing by adversary becomes great privacy threats not only in the cyberworld, but also in the physical world. To protect location privacy, I develop a real- life location monitoring system that is based on Bluetooth Low Energy (BLE) privacy feature that changes the device physical address periodically. To en- able users to better control their privacy level while still providing monitoring and tracking service to authorized parties (e.g., for child and elderly care), I extend BLE privacy by enriching its privacy semantics with a comprehensive set of metrics, such as simple opt-in/out, k-anonymity, and granularity-based anonymity. Both systems have been posted online and evaluated in terms of accuracy and user study.

A technique is proposed to improve the accuracy of indoor positioning systems based on WIFI radio-frequency signals by using dynamic access points and fingerprints (DAFs). Moreover, an indoor position system that relies solely in DAFs is proposed. The walking pattern of indoor users is classified as dynamic or static for indoor positioning purposes. I demonstrate that the performance of a conventional indoor positioning system that uses static fingerprints can be enhanced by considering dynamic fingerprints and access points. The accuracy of the system is evaluated using four positioning algorithms and two random access point selection strategies. The system facilitates the location of people where there is no wireless local area network (WLAN) infrastructure deployed or where the WLAN infrastructure has been drastically affected, for example by natural disasters. The system can be used for search and rescue operations and for expanding the coverage of an indoor positioning system.

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2015. / Recently, the deployment and availability of wireless technology have led to the development of location and positioning services. These Location Based Services (LBSs) are attracting the attention of researchers and mobile service providers. With the importance of ubiquitous computing, the main challenge seen in the LBS is in the mobile positioning or localization within reasonable and certain accuracy. The Global Positioning System (GPS), as a widely known and used navigation system, is only appropriate for use in outdoor environments, due to the lack of line-of-sight (LOS) in satellite signals that they cannot be used accurately inside buildings and premises. Apart from GPS, Wi-Fi is among others, a widely used technology as it is an already existing infrastructure in most places. This work proposes and presents an indoor positioning system. As opposed to an Ad-hoc Positioning System (APS), it uses a Wireless Mesh Network (WMN). The system makes use of an already existing Wi-Fi infrastructure. Moreover, the approach tests the positioning of a node with its neighbours in a mesh network using multi-hopping functionality. The positioning measurements used were the ICMP echo requests, RSSI and RTS/CTS requests and responses. The positioning method used was the trilateral technique, in combination with the idea of the fingerprinting method. Through research and experimentation, this study developed a system which shows potential as a positioning system with an error of about 2 m – 3 m. The hybridization of the methods proves an enhancement in the system though improvements are still required

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2017. / The implosion of the mobile phones, mobile applications and social media in recent years has triggered a great interest for more dedicated user-generated contents. Mobile users being the focal point, these modern virtual platforms depend on and live for collecting, structuring and manipulating the very fine-grained details about users' day-to-day activities. Since every human activity takes place in a geographical context, location information ranks high among the set of data to gather about user's daily life. User's specific location details can help filter content to serve and retrieve from them. Therefore, location-based services have been developed and successfully integrated into most virtual platforms in the quest for these precious data. However, location-based services do not fulfil all requirements. They depend on a range of positioning systems which show numerous limitations. None of the existing positioning systems is perfectly accurate. Today, it is therefore difficult to pinpoint a user in a venue using location-based services. Nevertheless, with the set of existing technology and techniques, it is possible to estimate and track users’ whereabouts in real-time. Providing the best possible estimation of user's position within a given venue can help achieve better user engagement. Depending on the gap of accuracy, the end result may actually match the outcome expected from perfectly accurate positioning systems. In this work, the focus is to develop a prototype positioning system which provides the best estimation of user's position in real-time in relation to a targeted venue or location. Through a series of research and comparison study, the most suited technology and techniques are objectively selected to build the intended prototype. The challenge of indoor positioning is also addressed in this work – bearing in mind the fact that this prototype is set to work accurately and efficiently in any geographical location and structure. The prototype is evaluated according to a set of predefined standard metrics, and theories are extracted to grow knowledge about this trending topic.

No description available.

No description available.

The aim of this term project was to analyze wireless indoor localization. It contains analysis of some wireless localization techniques such as Time of Arrival or Time Difference of Arrival. The paper also describes the system of SEWIO Company. Main part of the master’s thesis is description, design and implementation of the Kalman filter. The Kalman filter is used to improve two-dimensional positional data and synchronization of anchors (devices for finding a position of an object in SEWIO system). There are described a few system models for the Kalman filter.