1,778 research outputs found

    Mining users' significant driving routes with low-power sensors

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    While there is significant work on sensing and recognition of significant places for users, little attention has been given to users' significant routes. Recognizing these routine journeys, opens doors to the development of novel applications, like personalized travel alerts, and enhancement of user's travel experience. However, the high energy consumption of traditional location sensing technologies, such as GPS or WiFi based localization, is a barrier to passive and ubiquitous route sensing through smartphones. In this paper, we present a passive route sensing framework that continuously monitors a vehicle user solely through a phone's gyroscope and accelerometer. This approach can differentiate and recognize various routes taken by the user by time warping angular speeds experienced by the phone while in transit and is independent of phone orientation and location within the vehicle, small detours and traffic conditions. We compare the route learning and recognition capabilities of this approach with GPS trajectory analysis and show that it achieves similar performance. Moreover, with an embedded co-processor, common to most new generation phones, it achieves energy savings of an order of magnitude over the GPS sensor.This research has been funded by the EPSRC Innovation and Knowledge Centre for Smart Infrastructure and Construction project (EP/K000314).This is the author accepted manuscript. The final version is available from ACM via http://dx.doi.org/10.1145/2668332.266834

    2nd Joint ERCIM eMobility and MobiSense Workshop

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    SLS: Smart localization service: human mobility models and machine learning enhancements for mobile phone’s localization

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    In recent years we are witnessing a noticeable increment in the usage of new generation smartphones, as well as the growth of mobile application development. Today, there is an app for almost everything we need. We are surrounded by a huge number of proactive applications, which automatically provide relevant information and services when and where we need them. This switch from the previous generation of passive applications to the new one of proactive applications has been enabled by the exploitation of context information. One of the most important and most widely used pieces of context information is location data. For this reason, new generation devices include a localization engine that exploits various embedded technologies (e.g., GPS, WiFi, GSM) to retrieve location information. Consequently, the key issue in localization is now the efficient use of the mobile localization engine, where efficient means lightweight on device resource consumption, responsive, accurate and safe in terms of privacy. In fact, since the device resources are limited, all the services running on it have to manage their trade-off between consumption and reliability to prevent a premature depletion of the phone’s battery. In turn, localization is one of the most demanding services in terms of resource consumption. In this dissertation I present an efficient localization solution that includes, in addition to the standard location tracking techniques, the support of other technologies already available on smartphones (e.g., embedded sensors), as well as the integration of both Human Mobility Modelling (HMM) and Machine Learning (ML) techniques. The main goal of the proposed solution is the provision of a continuous tracking service while achieving a sizeable reduction of the energy impact of the localization with respect to standard solutions, as well as the preservation of user privacy by avoiding the use of a back-end server. This results in a Smart Localization Service (SLS), which outperforms current solutions implemented on smartphones in terms of energy consumption (and, therefore, mobile device lifetime), availability of location information, and network traffic volume

    Train Localisation using Wireless Sensor Networks

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    Safety and reliability have always been concerns for railway transportation. Knowing the exact location of a train enables the railway system to react to an unusual situation for the safety of human lives and properties. Generally, the accuracy of localisation systems is related with their deployment and maintenance costs, which can be on the order of millions of dollars a year. Despite a lot of research efforts, existing localisation systems based on different technologies are still limited because most of them either require expensive infrastructure (ultrasound and laser), have high database maintenance, computational costs or accumulate errors (vision), offer limited coverage (GPS-dark regions, Wi-Fi, RFID) or provide low accuracy (audible sound). On the other hand, wireless sensor networks (WSNs) offer the potential for a cheap, reliable and accurate solutions for the train localisation system. This thesis proposes a WSN-based train localisation system, in which train location is estimated based on the information gathered through the communication between the anchor sensors deployed along the track and the gateway sensor installed on the train, such as anchor sensors' geographic coordinates and the Received Signal Strength Indicator (RSSI). In the proposed system, timely anchor-gateway communication implies accurate localisation. How to guarantee effective communication between anchor sensors along the track and the gateway sensor on the train is a challenging problem for WSN-based train localisation. I propose a beacon driven sensors wake-up scheme (BWS) to address this problem. BWS allows each anchor sensor to run an asynchronous duty-cycling protocol to conserve energy and establishes an upper bound on the sleep time in one duty cycle to guarantee their timely wake-up once a train approaches. Simulation results show that the BWS scheme can timely wake up the anchor sensors at a very low energy consumption cost. To design an accurate scheme for train localisation, I conducted on-site experiments in an open field, a railway station and a tunnel, and the results show that RSSI can be used as an estimator for train localisation and its applicability increases with the incorporation of another type of data such as location information of anchor sensors. By combining the advantages of RSSI-based distance estimation and Particle Filtering techniques, I designed a Particle-Filter-based train localisation scheme and propose a novel Weighted RSSI Likelihood Function (WRLF) for particle update. The proposed localisation scheme is evaluated through extensive simulations using the data obtained from the on-site measurements. Simulation results demonstrate that the proposed scheme can achieve significant accuracy, where average localisation error stays under 30 cm at the train speed of 40 m=s, 40% anchor sensors failure rate and sparse deployment. In addition, the proposed train localisation scheme is robust to changes in train speed, the deployment density and reliability of anchor sensors. Anchor sensors are prone to hardware and software deterioration such as battery outage and dislocation. Therefore, in order to reduce the negative impacts of these problems, I designed a novel Consensus-based Anchor sensor Management Scheme (CAMS), in which each anchor sensor performs a self-diagnostics and reports the detected faults in the neighbourhood. CAMS can assist the gateway sensor to exclude the input from the faulty anchor sensors. In CAMS, anchor sensors update each other about their opinions on other neighbours and develops consensus to mark faulty sensors. In addition, CAMS also reports the system information such as signal path loss ratio and allows anchor sensors to re-calibrate and verify their geographic coordinates. CAMS is evaluated through extensive simulations based on real data collected from field experiments. This evaluation also incorporated the simulated node failure model in simulations. Though there are no existing WSN-based train localisation systems available to directly compare our results with, the proposed schemes are evaluated with real datasets, theoretical models and existing work wherever it was possible. Overall, the WSN-based train localisation system enables the use of RSSI, with combination of location coordinates of anchor sensors, as location estimator. Due to low cost of sensor devices, the cost of overall system remains low. Further, with duty-cycling operation, energy of the sensor nodes and system is conserved

    Precise Point Positioning Augmentation for Various Grades of Global Navigation Satellite System Hardware

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    The next generation of low-cost, dual-frequency, multi-constellation GNSS receivers, boards, chips and antennas are now quickly entering the market, offering to disrupt portions of the precise GNSS positioning industry with much lower cost hardware and promising to provide precise positioning to a wide range of consumers. The presented work provides a timely, novel and thorough investigation into the positioning performance promise. A systematic and rigorous set of experiments has been carried-out, collecting measurements from a wide array of low-cost, dual-frequency, multi-constellation GNSS boards, chips and antennas introduced in late 2018 and early 2019. These sensors range from dual-frequency, multi-constellation chips in smartphones to stand-alone chips and boards. In order to be comprehensive and realistic, these experiments were conducted in a number of static and kinematic benign, typical, suburban and urban environments. In terms of processing raw measurements from these sensors, the Precise Point Positioning (PPP) GNSS measurement processing mode was used. PPP has become the defacto GNSS positioning and navigation technique for scientific and engineering applications that require dm- to cm-level positioning in remote areas with few obstructions and provides for very efficient worldwide, wide-array augmentation corrections. To enhance solution accuracy, novel contributions were made through atmospheric constraints and the use of dual- and triple-frequency measurements to significantly reduce PPP convergence period. Applying PPP correction augmentations to smartphones and recently released low-cost equipment, novel analyses were made with significantly improved solution accuracy. Significant customization to the York-PPP GNSS measurement processing engine was necessary, especially in the quality control and residual analysis functions, in order to successfully process these datasets. Results for new smartphone sensors show positioning performance is typically at the few dm-level with a convergence period of approximately 40 minutes, which is 1 to 2 orders of magnitude better than standard point positioning. The GNSS chips and boards combined with higher-quality antennas produce positioning performance approaching geodetic quality. Under ideal conditions, carrier-phase ambiguities are resolvable. The results presented show a novel perspective and are very promising for the use of PPP (as well as RTK) in next-generation GNSS sensors for various application in smartphones, autonomous vehicles, Internet of things (IoT), etc

    Contributions to GNSS-R earth remote sensing from nano-satellites

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    Premi extraordinari doctorat UPC curs 2015-2016, àmbit de CiènciesGlobal Navigation Satellite Systems Reflectometry (GNSS-R) is a multi-static radar using navigation signals as signals of opportunity. It provides wide-swath and improved spatio-temporal sampling over current space-borne missions. The lack of experimental datasets from space covering signals from multiple constellations (GPS, GLONASS, Galileo, Beidou) at dual-band (L1 and L2) and dual-polarization (Right Hand Left Hand Circular Polarization: RHCP and LHCP), over the ocean, land and cryosphere remains a bottleneck to further develop these techniques. 3Cat-2 is a 6 units (3 x 2 elementary blocks of 10 x 10 x 10 cm3) CubeSat mission ayming to explore fundamentals issues towards an improvement in the understanding of the bistatic scattering properties of different targets. Since geolocalization of specific reflections points is determined by the geometry only, a moderate pointing accuracy is still required to correct for the antena pattern in scatterometry measurements. 3Cat-2 launch is foreseen for the first quarter 2016 into a Sun-Synchronous orbit of 510 km height using a Long March II D rocket. This Ph.D. Thesis represents the main contributions to the development of the 3Cat-2 GNSS-R Earth observation mission (6U CubeSat) including a novel type of GNSS-R technique: the reconstructed one. The desing, development of the platform, and a number of ground-based, airborne and stratospheric balloon experiments to validate the technique and to optimize the instrument. In particular, the main contributions of this Ph.D. thesis are: 1) A novel dual-band Global Navigation Satellite Systems Reflectometer that uses the P(Y) and C/A signals scattered over the sea surface to perform highly precise altimetric measurements (PYCARO). 2) The first proof-of-concept of PYCARO was performed during two different ground-based field experiments over a dam and over the sea under different surface roughness conditions. 3) The scattering of GNSS signals over a water surface has been studied when the receiver is at low height, as for GNSS-R coastal altimetry applications. The precise determination of the local sea level and wave state from the coast can provide useful altimetry and wave information as "dry" tide and wave gauges. In order to test this concept an experiment has been conducted at the Canal d'Investigació i Experimentació Marítima (CIEM) wave channel for two synthetic "sea" states. 4) Two ESA-sponsored airborne experiments were perfomed to test the precision and the relative accuracy of the conventional GNSS-R. 5) The empirical results of a GNSS-R experiment on-board the ESA-sponsored BAXUS 17 stratospheric balloon campaign performed North of Sweden over boreal forests showed that the power of the reflected signals is nearly independent of the platform height for a high coherent integration time. 6) An improved version of the PYCARO payload was tested in Octover 2014 for the second time during the ESA-sposored BEXUS-19,. This work achieved the first ever dual-frequency, multi-constellation GNSS-R observations over boreal forests and lakes using GPS, GLONASS and Galileo signals. 7) The first-ever dual-frequency multi-constellation GNSS-R dual-polarization measurements over boreal forests and lakes were obtained from the stratosphere during the BEXUS 19 using the PYCARO reflectometer operated in closed-loop mode.Global Navigation Satellite Systems Reflectometry (GNSS-R) es una técnica de radar multi-estático que usa señales de radio-navegación como señales de oportunidad. Esta técnica proporciona "wide-swath" y un mejor sampleado espacio-temporal en comparación con las misiones espaciales actuales. La falta de datos desde el espacio proporcionando señales de múltiples constelaciones (GPS, GLONASS, Galileo, Beidou) en doble banda (L1 y L2) y en doble polarización (RHCP y LHCP) sobre océano, tierra y criosfera continua siendo un problema por solucionar. 3Cat-2 es un cubesat de 6 unidades con el objetivo de explorar elementos fundamentales para mejorar el conocimiento sobre el scattering bi-estático sobre diferentes medios dispersores. Dado que la geolocalización de puntos de reflexión específicos está determinada solo por geometría, es necesario un requisito moderado de apuntamiento para corregir el diagrama de antena en aplicaciones de dispersometría. El lanzamiento del 3Cat-2 será en Q2 2016 en una órbitra heliosíncrona usando un cohete Long March II D. Esta tesis representa las contribuciones principales al desarrollo del satélite 3Cat2 para realizar observación de la tierra con GNSS-R incluyendo una nueva técnica: "the reconstructed-code GNSS-R". El diseño, desarrollo de la plataforma y un número de experimentos en tierra, desde avión y desde globo estratosférico para validar la técnica y optimizar el instrumento han sido realizados. En particular, las contribuciones de esta Ph.D. son: 1) un novedoso Global Navigation Satellite Systems Reflectometer que usa las señales P(Y) y C/A después de ser dispersadas sobre la superficie del mar para realizar medidas altimétricas muy precisas. (PYCARO). 2) La primera prueba de concepto de PYCARO se hizo en dos experimentos sobre un pantano y sobre el mar bajo diferentes condiciones de rugosidad. 3) La disperión de las señales GNSS sobre una superfice de agua ha sido estudiada para bajas altitudes para aplicaciones GNSS-R altimétricas de costa. La determinación precisa del nivel local del mar y el estado de las olas desde la costa puede proporcionar información útil de altimetría e información de olas. Para hacer un test de este concepto un experimento en el Canal d'Investigació i Experimentació Marítima (CIEM) fue realizado para dos estados sintéticos de rugosidad. 4) Dos experimentos en avión con esponsor de la ESA se realizaron para estudiar la preción y la exactitud relativa de cGNSS-R. 5) Los resultados empíricos del experimento GNSS-R en BEXUS 17 con esponsor de la ESA realizado en el norte de Suecia sobre bosques boreales mostró que la potencia reflejada de las señales es independiente de la altitud de la plataforma para un tiempo de integración coherente muy alto. 6) Una versión mejorada del PYCARO fue testeada en octubre del 2014 por segunda vez durante el BEXUS 19 que también fue patrocidado por la ESA. Este trabajo proporcionó las primeras medidas GNSS-R sobre bosques boreales en doble frecuencia usando varias constelaciones GNSS. 7) Las primeras medidas polarimétricas (RHCP y LHCP) de GNSS-R sobre bosques boreales también fueron conseguidas durante el experimento BEXUS 19.Award-winningPostprint (published version
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