882 research outputs found

    Managing big data experiments on smartphones

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    The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. Next generation smartphone applications are expected to be much larger-scale and complex, demanding that these undergo evaluation and testing under different real-world datasets, devices and conditions. In this paper, we present an architecture for managing such large-scale data management experiments on real smartphones. We particularly present the building blocks of our architecture that encompassed smartphone sensor data collected by the crowd and organized in our big data repository. The given datasets can then be replayed on our testbed comprising of real and simulated smartphones accessible to developers through a web-based interface. We present the applicability of our architecture through a case study that involves the evaluation of individual components that are part of a complex indoor positioning system for smartphones, coined Anyplace, which we have developed over the years. The given study shows how our architecture allows us to derive novel insights into the performance of our algorithms and applications, by simplifying the management of large-scale data on smartphones

    RSSI Based Indoor Localization for Smartphone Using Fixed and Mobile Wireless Node

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    Nowadays with the dispersion of wireless networks, smartphones and diverse related services, different localization techniques have been developed. Global Positioning System (GPS) has a high rate of accuracy for outdoor localization but the signal is not available inside of buildings. Also other existing methods for indoor localization have low accuracy. In addition, they use fixed infrastructure support. In this paper, we present a novel system for indoor localization, which also works well outside. We have developed a mathematical model for estimating location (distance and direction) of a mobile device using wireless technology. Our experimental results on Smartphones (Android and iOS) show good accuracy (an error less than 2.5 meters). We have also used our developed system in asset tracking and complex activity recognition

    Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

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    The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

    Development and Experimental Analysis of Wireless High Accuracy Ultra-Wideband Localization Systems for Indoor Medical Applications

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    This dissertation addresses several interesting and relevant problems in the field of wireless technologies applied to medical applications and specifically problems related to ultra-wideband high accuracy localization for use in the operating room. This research is cross disciplinary in nature and fundamentally builds upon microwave engineering, software engineering, systems engineering, and biomedical engineering. A good portion of this work has been published in peer reviewed microwave engineering and biomedical engineering conferences and journals. Wireless technologies in medicine are discussed with focus on ultra-wideband positioning in orthopedic surgical navigation. Characterization of the operating room as a medium for ultra-wideband signal transmission helps define system design requirements. A discussion of the first generation positioning system provides a context for understanding the overall system architecture of the second generation ultra-wideband positioning system outlined in this dissertation. A system-level simulation framework provides a method for rapid prototyping of ultra-wideband positioning systems which takes into account all facets of the system (analog, digital, channel, experimental setup). This provides a robust framework for optimizing overall system design in realistic propagation environments. A practical approach is taken to outline the development of the second generation ultra-wideband positioning system which includes an integrated tag design and real-time dynamic tracking of multiple tags. The tag and receiver designs are outlined as well as receiver-side digital signal processing, system-level design support for multi-tag tracking, and potential error sources observed in dynamic experiments including phase center error, clock jitter and drift, and geometric position dilution of precision. An experimental analysis of the multi-tag positioning system provides insight into overall system performance including the main sources of error. A five base station experiment shows the potential of redundant base stations in improving overall dynamic accuracy. Finally, the system performance in low signal-to-noise ratio and non-line-of-sight environments is analyzed by focusing on receiver-side digitally-implemented ranging algorithms including leading-edge detection and peak detection. These technologies are aimed at use in next-generation medical systems with many applications including surgical navigation, wireless telemetry, medical asset tracking, and in vivo wireless sensors

    A Hybrid Indoor Location Positioning System

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    Indoor location positioning techniques have experienced impressive growth in recent years. A wide range of indoor positioning algorithms has been developed for various applications. In this work a practical indoor location positioning technique is presented which utilizes off-the-shelf smartphones and low-cost Bluetooth Low Energy (BLE) nodes without any further infrastructure. The method includes coarse and fine modes of location positioning. In the coarse mode, the received signal strength (RSS) of the BLE nodes is used for location estimation while in the fine acoustic signals are utilized for accurate positioning. The system can achieve centimeter-level positioning accuracy in its fine mode. To enhance the system’s performance in noisy environments, two digital signal processing (DSP) algorithms of (a) band-pass filtering with audio pattern recognition and (b) linear frequency modulated chirp signal with matched filter are implemented. To increase the system’s robustness in dense multipath environments, a method using data clustering with sliding window is employed. The received signal strength of BLE nodes is used as an auxiliary positioning method to identify the non-line-of-sight (NLoS) propagation paths in the acoustic positioning mode. Experimental measurement results in an indoor area of 10 m2 indicate that the positioning error falls below 6 cm

    Low cost inertial-based localization system for a service robot

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    Dissertation presented at Faculty of Sciences and Technology of the New University of Lisbon to attain the Master degree in Electrical and Computer Science EngineeringThe knowledge of a robot’s location it’s fundamental for most part of service robots. The success of tasks such as mapping and planning depend on a good robot’s position knowledge. The main goal of this dissertation is to present a solution that provides a estimation of the robot’s location. This is, a tracking system that can run either inside buildings or outside them, not taking into account just structured environments. Therefore, the localization system takes into account only measurements relative. In the presented solution is used an AHRS device and digital encoders placed on wheels to make a estimation of robot’s position. It also relies on the use of Kalman Filter to integrate sensorial information and deal with estimate errors. The developed system was testes in real environments through its integration on real robot. The results revealed that is not possible to attain a good position estimation using only low-cost inertial sensors. Thus, is required the integration of more sensorial information, through absolute or relative measurements technologies, to provide a more accurate position estimation

    A survey of fuzzy logic in wireless localization

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    Opportunistic timing signals for pervasive mobile localization

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    Mención Internacional en el título de doctorThe proliferation of handheld devices and the pressing need of location-based services call for precise and accurate ubiquitous geographic mobile positioning that can serve a vast set of devices. Despite the large investments and efforts in academic and industrial communities, a pin-point solution is however still far from reality. Mobile devices mainly rely on Global Navigation Satellite System (GNSS) to position themselves. GNSS systems are known to perform poorly in dense urban areas and indoor environments, where the visibility of GNSS satellites is reduced drastically. In order to ensure interoperability between the technologies used indoor and outdoor, a pervasive positioning system should still rely on GNSS, yet complemented with technologies that can guarantee reliable radio signals in indoor scenarios. The key fact that we exploit is that GNSS signals are made of data with timing information. We then investigate solutions where opportunistic timing signals can be extracted out of terrestrial technologies. These signals can then be used as additional inputs of the multi-lateration problem. Thus, we design and investigate a hybrid system that combines range measurements from the Global Positioning System (GPS), the world’s most utilized GNSS system, and terrestrial technologies; the most suitable one to consider in our investigation is WiFi, thanks to its large deployment in indoor areas. In this context, we first start investigating standalone WiFi Time-of-flight (ToF)-based localization. Time-of-flight echo techniques have been recently suggested for ranging mobile devices overWiFi radios. However, these techniques have yielded only moderate accuracy in indoor environments because WiFi ToF measurements suffer from extensive device-related noise which makes it challenging to differentiate between direct path from non-direct path signal components when estimating the ranges. Existing multipath mitigation techniques tend to fail at identifying the direct path when the device-related Gaussian noise is in the same order of magnitude, or larger than the multipath noise. In order to address this challenge, we propose a new method for filtering ranging measurements that is better suited for the inherent large noise as found in WiFi radios. Our technique combines statistical learning and robust statistics in a single filter. The filter is lightweight in the sense that it does not require specialized hardware, the intervention of the user, or cumbersome on-site manual calibration. This makes the method we propose as the first contribution of the present work particularly suitable for indoor localization in large-scale deployments using existing legacy WiFi infrastructures. We evaluate our technique for indoor mobile tracking scenarios in multipath environments, and, through extensive evaluations across four different testbeds covering areas up to 1000m2, the filter is able to achieve a median ranging error between 1:7 and 2:4 meters. The next step we envisioned towards preparing theoretical and practical basis for the aforementioned hybrid positioning system is a deep inspection and investigation of WiFi and GPS ToF ranges, and initial foundations of single-technology self-localization. Self-localization systems based on the Time-of-Flight of radio signals are highly susceptible to noise and their performance therefore heavily rely on the design and parametrization of robust algorithms. We study the noise sources of GPS and WiFi ToF ranging techniques and compare the performance of different selfpositioning algorithms at a mobile node using those ranges. Our results show that the localization error varies greatly depending on the ranging technology, algorithm selection, and appropriate tuning of the algorithms. We characterize the localization error using real-world measurements and different parameter settings to provide guidance for the design of robust location estimators in realistic settings. These tools and foundations are necessary to tackle the problem of hybrid positioning system providing high localization capabilities across indoor and outdoor environments. In this context, the lack of a single positioning system that is able the fulfill the specific requirements of diverse indoor and outdoor applications settings has led the development of a multitude of localization technologies. Existing mobile devices such as smartphones therefore commonly rely on a multi-RAT (Radio Access Technology) architecture to provide pervasive location information in various environmental contexts as the user is moving. Yet, existing multi-RAT architectures consider the different localization technologies as monolithic entities and choose the final navigation position from the RAT that is foreseen to provide the highest accuracy in the particular context. In contrast, we propose in this work to fuse timing range (Time-of-Flight) measurements of diverse radio technologies in order to circumvent the limitations of the individual radio access technologies and improve the overall localization accuracy in different contexts. We introduce an Extended Kalman filter, modeling the unique noise sources of each ranging technology. As a rich set of multiple ranges can be available across different RATs, the intelligent selection of the subset of ranges with accurate timing information is critical to achieve the best positioning accuracy. We introduce a novel geometrical-statistical approach to best fuse the set of timing ranging measurements. We also address practical problems of the design space, such as removal of WiFi chipset and environmental calibration to make the positioning system as autonomous as possible. Experimental results show that our solution considerably outperforms the use of monolithic technologies and methods based on classical fault detection and identification typically applied in standalone GPS technology. All the contributions and research questions described previously in localization and positioning related topics suppose full knowledge of the anchors positions. In the last part of this work, we study the problem of deriving proximity metrics without any prior knowledge of the positions of the WiFi access points based on WiFi fingerprints, that is, tuples of WiFi Access Points (AP) and respective received signal strength indicator (RSSI) values. Applications that benefit from proximity metrics are movement estimation of a single node over time, WiFi fingerprint matching for localization systems and attacks on privacy. Using a large-scale, real-world WiFi fingerprint data set consisting of 200,000 fingerprints resulting from a large deployment of wearable WiFi sensors, we show that metrics from related work perform poorly on real-world data. We analyze the cause for this poor performance, and show that imperfect observations of APs with commodity WiFi clients in the neighborhood are the root cause. We then propose improved metrics to provide such proximity estimates, without requiring knowledge of location for the observed AP. We address the challenge of imperfect observations of APs in the design of these improved metrics. Our metrics allow to derive a relative distance estimate based on two observed WiFi fingerprints. We demonstrate that their performance is superior to the related work metrics.This work has been supported by IMDEA Networks InstitutePrograma Oficial de Doctorado en Ingeniería TelemáticaPresidente: Francisco Barceló Arroyo.- Secretario: Paolo Casari.- Vocal: Marco Fior
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