Indoor Positioning Using Acoustic Pseudo-Noise Based Time Difference of Arrival

The Global Positioning System (GPS) provides good precision on a global scale, but is not suitable for indoor applications. Indoor positioning systems (IPS) aim to provide high precision position information in an indoor environment. IPS has huge market opportunity with a growing number of commercial and consumer applications especially as Internet of Things (IoT) develops. This paper studies an IPS approach using audible sound and pseudo-noise (PN) based time difference of arrival (TDoA). The system’s infrastructure consists of synchronized speakers. The object to be located, or receiver, extracts TDoA information and uses multilateration to calculate its position. The proposed IPS utilizes sound waves since they travel much slower compared to electromagnetic waves, allowing for easier measurements. Additionally, the audible spectrum has a large availability of low directivity speakers and microphones allowing for a large coverage area compared to highly directive ultrasonic transceivers. This paper experimentally evaluates the feasibility of the proposed IPS

Uncover the Power of Multipath : Detecting NLOS Drones Using Low-Cost WiFi Devices

In recent years, consumer UAV technology has seen considerable advances. Consumer UAVs have become an ideal vector for privacy invasions due to their affordability, size, maneuverability, and their ability to stream live high-quality video. There is considerable proliferation of drones in both civil and military domains. Hence it is critical to detect invading unmanned aerial vehicles (UAVs) or drones in a timely manner for both security and safeguarding privacy. Currently available solutions like active radar, video or acoustic sensors are very expensive (especially for individuals) and have considerable constraints (e.g., requiring visual line of sight). Recent research on drone detection with passive RF signals provides an opportunity for low-cost deployment of drone detectors on commodity wireless devices. The state of the arts in this direction mainly focus on detecting drones using line-of-sight (LOS) RF signals which are less noisy as compared to their non-LOS (NLOS) counterparts. To the best of our knowledge, there is no existing cost-effective solution for the general public to enable non-LOS(NLOS) detection for drone privacy invasion, which is the most common condition and it still remains an open challenge. This thesis research provides a low-cost UAV detection system for privacy invasion caused by customer drone. Our model supports NLOS detection with low-cost hardware under $50, and hence it is affordable for the general public to deploy in their house, apartments, and office. Our work utilizes inherent drone motions (i.e., body shifting and vibrations) as unique signatures for drone detection. Firstly, we validated the relationship between drone motions and RF signal under the NLOS condition using extensive experiments. This is motivated by the fact that under NLOS conditions slight changes to the position or motion of a drone could lead to dramatic change in multi-path components in received RF signals. The NLOS condition “amplifies the RF signatures introduced by drone motions. We designed a deep learning model to capture the complex features from NLOS RF signals. In particular, we designed and trained a long short-term memory (LSTM) neural network [15, 27], a generative model which can effectively extract features of inputs for NLOS drone detection. Moreover, without knowing the presence of drones, our system starts with classifying any detected RF signals into LOS signals and NLOS signals before the NLOS drone learner is used. Classification of LOS and NLOS signals is feasible because they exhibit different combined features such as strength, variance, and distribution due to their differences in multipath effects. We used the supervised support vector machine (S-SVM) [17] as the learning model, which is effective for binary classification. This design is validated via extensive experiments using commodity drones in resident areas with other Wi-Fi enabled mobile devices

An Approach to Finding Parking Space Using the CSI-based WiFi Technology

With ever-increasing number of vehicles and shortages of parking spaces, parking has always been a very important issue in transportation. It is necessary to use advanced intelligent technologies to help drivers find parking spaces, quickly. In this thesis, an approach to finding empty spaces in parking lots using the CSI-based WiFi technology is presented. First, the channel state information (CSI) of received WiFi signals is analyzed. The features of CSI data that are strongly correlated with the number of empty slots in parking lots are identified and extracted. A machine learning technique to perform multi-class classification that categorizes the input data into classes representing the number of empty slots is employed. A prototype system of the proposed approach is developed. Experiments are performed and it is shown that the system is feasible. Compared with traditional approaches based on magnetic sensors deployed on individual parking slots, the proposed approach is non-intrusive as it does not require to install specialized devices in a parking lot, and is cost-effective since it utilizes either existing WiFi infrastructure or only a pair of WiFi devices. As a result, the average classification accuracy of system is 80.8%, and the accuracy is improved to 93.8% with a tolerance of one empty slot

Improving Location Accuracy And Network Capacity In Mobile Networks

Todays mobile computing must support a wide variety of applications such as location-based services, navigation, HD media streaming and augmented reality. Providing such services requires large network bandwidth and precise localization mechanisms, which face significant challenges. First, new (real-time) localization mechanisms are needed to locate neighboring devices/objects with high accuracy under tight environment constraints, e.g. without infrastructure support. Second, mobile networks need to deliver orders of magnitude more bandwidth to support the exponentially increasing traffic demand, and adapt resource usage to user mobility.In this dissertation, we build effective and practical solutions to address these challenges. Our first research area is to develop new localization mechanisms that utilize the rich set of sensors on smartphones to implement accurate localization systems. We propose two designs. The first system tracks distance to nearby devices with centimeter accuracy by transmitting acoustic signals between the devices. We design robust and efficient signal processing algorithms that measure distances accurately on the fly, thus enabling real-time user motion tracking. Our second system locates a transmitting device in real-time using commodity smart- phones. Driving by the insight that rotating a wireless receiver (smartphone) around a users body can effectively emulate the sensitivity and functionality of a directional antenna, we design a rotation-based measurement algorithm that can accurately predict the direction of the target transmitter and locate the transmitter with a few measurements.Our second research area is to develop next generation mobile networks to significantly boost network capacity. We propose a drastically new outdoor picocell design that leverages millimeter wave 60GHz transmissions to provide multi-Gbps bandwidth for mobile users. Using extensive measurements on off-the-shelf 60GHz radios, we explore the feasibility of 60GHz picocells by characterizing range, attenuation due to reflections, sensitivity to movement and blockage, and interference in typical urban environments. Our results dispel some common myths on 60GHz, and show that 60GHz outdoor picocells are indeed a feasible approach for delivering orders of magnitude increase in network capacity.Finally, we seek to capture and understand user mobility patterns which are essential in mobile network design and deployment. While traditional methods of collecting human mobility traces are expensive and not scalable, we explore a new direction that extracts large-scale mobility traces through widely available geosocial datasets, e.g. Foursquare "check-in" datasets. By comparing raw GPS traces against Foursquare checkins, we analyze the value of using geosocial datasets as representative traces of human mobility. We then develop techniques to both "sanitize" and "repopulate" geosocial traces, thus producing detailed mobility traces more indicative of actual human movement and suitable for mobile network design

Forests

In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms |, |, |, |, |, |, and |. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.U01 OH010841/OH/NIOSH CDC HHSUnited States/U54 OH007544/OH/NIOSH CDC HHSUnited States

Infrared ranging in multipath environments for indoor localization of mobile targets

Esta tesis aborda el problema de la medida de diferencias de distancia mediante señales ópticas afectadas por multicamino, aplicada a la localización de agentes móviles en espacios interiores. Los avances en robótica, entornos inteligentes y vehículos autónomos han creado un campo de aplicación específico para la localización en interiores, cuyos requerimientos de precisión (en el rango de los cm) son muy superiores a los demandados por las aplicaciones de localización orientadas a personas, en cuyo contexto se han desarrollado la mayor parte de las alternativas tecnológicas. La investigación con métodos de geometría proyectiva basados en cámaras y de multilateración basados en medida de distancia con señales de radiofrecuencia de banda ancha, de ultrasonido y ópticas han demostrado un rendimiento potencial adecuado para cubrir estos requerimientos. Sin embargo, todas estas alternativas, aún en fase de investigación, presentan dificultades que limitan su aplicación práctica. En el caso de los sistemas ópticos, escasamente estudiados en este contexto, los trabajos previos se han basado en medidas de diferencia de fase de llegada de señales infrarrojas moduladas sinusoidalmente en intensidad. Una infraestructura centralizada computa medidas diferenciales, entre receptores fijos, de la señal emitida desde el móvil a posicionar, y calcula la posición del móvil mediante trilateración hiperbólica a partir de éstas. Estas investigaciones demostraron que se pueden alcanzar precisiones de pocos centímetros; sin embargo, las interferencias por multicamino debidas a la reflexión de la señal óptica en superficies del entorno pueden degradar esta precisión hasta las decenas de centímetros dependiendo de las características del espacio. Así pues, el efecto del multicamino es actualmente la principal fuente de error en esta tecnología, y por tanto, la principal barrera a superar para su implementación en situaciones reales. En esta tesis se propone y analiza un sistema de medida con señales ópticas que permite obtener estimaciones de diferencias de distancia precisas reduciendo el efecto crítico del multicamino. El sistema propuesto introduce una modulación con secuencias de ruido pseudoaleatorio sobre la modulación sinusoidal típicamente usada para medida de fase por onda continua, y aprovecha las propiedades de ensanchamiento en frecuencia de estas secuencias para reducir el efecto del multicamino. El sistema, que realiza una doble estimación de tiempo y fase de llegada, está compuesto por una etapa de sincronización que posibilita la demodulación parcialmente coherente de la señal recibida, seguida de un medidor diferencial de fase sobre las componentes desensanchadas tras la demodulación. Las condiciones de multicamino óptico típicas en espacios interiores, con una componente de camino directo claramente dominante, permiten que el proceso de demodulación recupere más potencia del camino directo que del resto de contribuciones, reduciendo el efecto del multicamino en la estimación final. Los resultados obtenidos demuestran que la aplicación del método propuesto permitiría realizar posicionamiento a partir de señales ópticas con el rendimiento adecuando para aplicaciones de robótica y guiado de vehículos en espacios interiores; además, el progresivo aumento de la potencia y el ancho de banda de los dispositivos optoelectrónicos disponibles permite esperar un incremento considerable de las prestaciones de la propuesta en los próximos años

Advanced Location-Based Technologies and Services

Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements

Machine Learning Techniques for Device-Free Indoor Person Tracking

L'abstract è presente nell'allegato / the abstract is in the attachmen

Techniques to Leverage RF Signals for Context Sensing

RF signals and devices have been used for wireless communication to improve the mobility and ubiquity of mobile devices. In this dissertation, we show that these RF signals can also be used for context sensing applications. Specifically, we present cyber-physical systems and algorithms to sense human vital signals, object vibrations and movements, and object’s location to deliver new sensing capabilities for a variety of new applications including health-care monitoring, privacy protection, and indoor localization. We deliver three fundamental contributions. First, we develop an RF-based system to “sense” human breathing volume continuously in fine-grained from afar. Second, we develop a technique to “sense” the wireless signals emitted from drones/fly-cams to detect them and alert users for privacy protection. Last, we present our preliminary study on building a system to enable the mobile device to “sense” their global locations at the indoor environment. To deliver these contributions, we exploit the properties of physical characteristics of RF signals, analyze and understand targeted subjects behaviors (i.e., human, drones), work across different limitations and hardware-software barriers, and introduce novel systems and new algorithms to overcome the challenges. We implement and evaluate the system on real users/patients, test the systems across different environments, and demonstrate how they can enable many other real-world applications