Location based services (LBS) are gaining widespread user acceptance and increased daily usage. GPS based mobile navigation systems (Garmin), location-related social network updates and "check-ins" (Facebook), location-based games (Nokia), friend queries (Foursquare) and ads (Google) are some of the popular LBSs available to mobile users today. Despite these successes, current user services fall short of a vision where mobile users could ask for continuous location-based services with always-up-to-date information around them, such as the list of friends or favorite restaurants within 15 minutes of driving. Providing such a location based service in real time faces a number of technical challenges. In this dissertation research, we propose a suite of novel techniques and system architectures to address some known technical challenges of continuous location queries and updates. Our solution approaches enable the creation of new, practical and scalable location based services with better energy efficiency on mobile clients and higher throughput at the location servers. Our first contribution is the development of RoadTrack, a road network aware and query-aware location update framework and a suite of algorithms. A unique characteristic of RoadTrack is the innovative design of encounter points and system-defined precincts to manage the desired spatial resolution of location updates for different mobile clients while reducing the complexity and energy consumption of location update strategies. The second novelty of this dissertation research is the technical development of Dandelion data structures and algorithms that can deliver superior performance for the periodic re-evaluation of continuous road-network distance based location queries, when compared with the alternative of repeatedly performing a network expansion along a mobile user's trajectory. The third contribution of this dissertation research is the FastExpand algorithm that can speed up the computation of single-issue shortest-distance road network queries. Finally, we have developed the open source GT MobiSim mobility simulator, a discrete event simulation platform to generate realistic driving trajectories for real road maps. It has been downloaded and utilized by many to evaluate the efficiency and effectiveness of the location query and location update algorithms, including the research efforts in this dissertation.

Advances in wireless communication have enabled mobility of personal computing services equipped with sensing and computing capabilities. This has motivated the development of location-based services (LBS) that are implemented on top of existing communication infrastructures to cater to changing user contexts. To enable and support the delivery of LBS, accurate, reliable, and realtime user location information is needed. This thesis introduces a cognitive dynamic system for tracking the position of mobile users using received signal strength (RSS) in Wireless Local Area Networks (WLAN). The main challenge in WLAN positioning is the unpredictable nature of the RSS-position relationship. Existing system rely on a set of training samples collected at a set of anchor points with known positions in the environment to characterize this relationship. The first contribution of this thesis is the use of nonparametric kernel density estimation for minimum mean square error positioning using the RSS training data. This formulation enables the rigorous study of state-space filtering in the context of WLAN positioning. The outcome is the Nonparametric Information (NI) filter, a novel recursive position estimator that incorporates both RSS measurements and a dynamic model of pedestrian motion during estimation. In contrast to traditional Kalman filtering approaches, the NI filter does not require the explicit knowledge of RSS-position relationship and is therefore well-suited for the WLAN positioning problem. The use of the dynamic motion model by the NI filter leads to the design of a cognitive dynamic tracking system. This design harnesses the benefits of feedback and position predictions from the filter to guide the selection of anchor points and radio sensors used during estimation. Experimental results using real measurement from an office environment demonstrate the effectiveness of proactive determination of sensing and estimation parameters in mitigating difficulties that arise due to the unpredictable nature of the indoor radio environment. In particular, the results indicate that the proposed cognitive design achieves an improvement of 3.19m (56\%) in positioning error relative to memoryless positioning alone.

Advances in wireless communication have enabled mobility of personal computing services equipped with sensing and computing capabilities. This has motivated the development of location-based services (LBS) that are implemented on top of existing communication infrastructures to cater to changing user contexts. To enable and support the delivery of LBS, accurate, reliable, and realtime user location information is needed. This thesis introduces a cognitive dynamic system for tracking the position of mobile users using received signal strength (RSS) in Wireless Local Area Networks (WLAN). The main challenge in WLAN positioning is the unpredictable nature of the RSS-position relationship. Existing system rely on a set of training samples collected at a set of anchor points with known positions in the environment to characterize this relationship. The first contribution of this thesis is the use of nonparametric kernel density estimation for minimum mean square error positioning using the RSS training data. This formulation enables the rigorous study of state-space filtering in the context of WLAN positioning. The outcome is the Nonparametric Information (NI) filter, a novel recursive position estimator that incorporates both RSS measurements and a dynamic model of pedestrian motion during estimation. In contrast to traditional Kalman filtering approaches, the NI filter does not require the explicit knowledge of RSS-position relationship and is therefore well-suited for the WLAN positioning problem. The use of the dynamic motion model by the NI filter leads to the design of a cognitive dynamic tracking system. This design harnesses the benefits of feedback and position predictions from the filter to guide the selection of anchor points and radio sensors used during estimation. Experimental results using real measurement from an office environment demonstrate the effectiveness of proactive determination of sensing and estimation parameters in mitigating difficulties that arise due to the unpredictable nature of the indoor radio environment. In particular, the results indicate that the proposed cognitive design achieves an improvement of 3.19m (56\%) in positioning error relative to memoryless positioning alone.

Mobile applications are becoming more and more important in the daily life and offer interesting and completely new aspects to the field of cartography. The technological possibilities provided by computer science have had a large impact on cartography and therefore cartographers must be familiar with the development of software to present geographic information and make it accessible. With the recent rise of mobile devices like smartphones, it is inevitable that this has to include mobile platforms as well. The main focus of this Bachelor thesis is on the conceptual design and implementation of tutorials to teach the development of mobile applications or 'apps' for the Android platform. The tutorials are going to be part of exercises in a practical course that accompanies the lecture 'Mobile Cartography' held at the Institute of Cartography at the Dresden University of Technology. Five exercises have been created covering key aspects of Android programming with Java. The theoretical part of this thesis starts with providing definitions of important terms of mobile computing and mobile cartography that will play an important role throughout the thesis. After that possibilities of interaction with mobile and web applications are introduced and discussed briefly. Location-based services and their usage in the context of social networks are discussed in the following section before the sensors of mobile devices are analyzed. A brief discussion of the potential for augmented reality applications is also given. The following second section deals with mobile software platforms and focuses on the Android operating system for mobile devices. Within this section, the features of the Android framework are described and some specific characteristics of software development are explained. The third section is all about the conceptual design and implementation of the tutorials. Considering the requirements for the practical course and different aspects of learning, the pedagogical approach is described. Additionally the ECLASS model which has evolved in the context of E-Learning is explained as it provided the structure of the exercises. After that, the content of the five exercises which include basics, layout, internal interaction of components, database access and the integration of maps is reflected. Finally, a brief outlook on further possible exercises is given.

<p>The research presented in this thesis investigates the design challenges associated with the development and use of mobile applications and tools for supporting collaboration in educational activities. These technologies provide new opportunities to promote and enhance collaboration by engaging learners in a variety of activities across different places and contexts. A basic challenge is to identify how to design and deploy mobile tools and services that could be used to support collaboration in different kinds of settings. There is a need to investigate how to design collaborative learning processes and to support flexible educational activities that take advantage of mobility. The main research question that I focus on is the identification of factors that influence the design of mobile collaborative learning.</p><p>The theoretical foundations that guide my work rely on the concepts behind computer supported collaborative learning and design-based research. These ideas are presented at the beginning of this thesis and provide the basis for developing an initial framework for understanding mobile collaboration. The empirical results from three different projects conducted as part of my efforts at the Center for Learning and Knowledge Technologies at Växjö University are presented and analyzed. These results are based on a collection of papers that have been published in two refereed international conference proceedings, a journal paper, and a book chapter. The educational activities and technological support have been developed in accordance with a grounded theoretical framework. The thesis ends by discussing those factors, which have been identified as having a significant influence when it comes to the design and support of mobile collaborative learning.</p><p>The findings presented in this thesis indicate that mobility changes the contexts of learning and modes of collaboration, requiring different design approaches than those used in traditional system development to support teaching and learning. The major conclusion of these efforts is that the learners’ creations, actions, sharing of experiences and reflections are key factors to consider when designing mobile collaborative activities in learning. The results additionally point to the benefit of directly involving the learners in the design process by connecting them to the iterative cycles of interaction design and research.</p>

This dissertation presents LAISYC, a modular location-aware architecture for intelligent real-time mobile applications that is fully-implementable by third party mobile app developers and supports high-precision and high-accuracy positioning systems such as GPS. LAISYC significantly improves device battery life, provides location data authenticity, ensures security of location data, and significantly reduces the amount of data transferred between the phone and server. The design, implementation, and evaluation of LAISYC using real mobile phones include the following modules: the GPS Auto-Sleep module saves battery energy when using GPS, maintaining acceptable movement tracking (approximately 89% accuracy) with an approximate average doubling of battery life. The Location Data Signing module adds energy-efficient data authenticity to this architecture that is missing in other architectures, with an average approximate battery life decrease of only 7%. The Session Management and Adaptive Location Data Buffering modules also contribute to battery life savings by providing energy-efficient real-time data communication between a mobile phone and server, increasing the average battery life for application data transfer by approximately 28% and reducing the average energy cost for location data transfer by approximately 38%. The Critical Point Algorithm module further reduces battery energy expenditures and the amount of data transferred between the mobile phone and server by eliminating non-essential GPS data (an average 77% reduction), with an average doubling of battery life as the interval of time between location data transmissions is doubled. The Location Data Encryption module ensures the security of the location data being transferred, with only a slight impact on battery life (i.e., a decrease of 4.9%). The LAISYC architecture was validated in two innovative mobile apps that would not be possible without LAISYC due to energy and data transfer constraints. The first mobile app, TRAC-IT, is a multi-modal travel behavior data collection tool that can provide simultaneous real-time location-based services. In TRAC-IT, the GPS Auto-Sleep, Session Management, Adaptive Location Data Buffering, Critical Point algorithm, and the Session Management modules all contribute energy savings that enable the phone's battery to last an entire day during real-time high-resolution GPS tracking. High-resolution real-time GPS tracking is critical to TRAC-IT for reconstructing detailed travel path information, including distance traveled, as well as providing predictive, personalized traffic alerts based on historical and real-time data. The Location Data Signing module allows transportation analysts to trust information that is recorded by the application, while the Location Data Encryption module protects the privacy of users' location information. The Session Management, Adaptive Location Data Buffering, and Critical Point algorithm modules allow TRAC-IT to avoid data overage costs on phones with limited data plans while still supporting real-time location data communication. The Adaptive Location Data Buffering module prevents tracking data from being lost when the user is outside network coverage or is on a voice call for networks that do not support simultaneous voice and data communications. The second mobile app, the Travel Assistance Device (TAD), assists transit riders with intellectual disabilities by prompting them when to exit the bus as well as tracking the rider in real-time and alerting caregivers if they are lost. In the most recent group of TAD field tests in Tampa, Florida, TAD provided the alert in the ideal location to transit riders in 100% (n = 33) of tests. In TAD, the GPS Auto-Sleep, Session Management, Adaptive Location Data Buffering, Critical Point algorithm, and the Session Management modules all contribute energy savings that enable the phone's battery to last an entire day during real-time high-resolution GPS tracking. High-resolution GPS tracking is critical to TAD for providing accurate instructions to the transit rider when to exit the bus as well as tracking an accurate location of the traveler so that caregivers can be alerted if the rider becomes lost. The Location Data Encryption module protects the privacy of the transit rider while they are being tracked. The Session Management, Adaptive Location Data Buffering, and Critical Point algorithm modules allow TAD to avoid data overage costs on phones with limited data plans while still supporting real-time location data communication for the TAD tracking alert features. Adaptive Location Data Buffering module prevents transit rider location data from being lost when the user is outside network coverage or is on a voice call for networks that do not support simultaneous voice and data communications.

Ortsbasierte Dienste erfreuen sich in den letzten Jahren starker Beliebtheit. Für deren Umsetzung sind sog. Lokalisierungsdienste notwendig, welche eine Ortung von Mobilgeräten erlauben. Das GPS stellt den wohl populärsten Lokalisierungsdienst dar, ist jedoch innerhalb von Gebäuden nur sehr beschränkt einsetzbar. In der Vergangenheit wurden deshalb Methoden vorgeschlagen, die zur Positionsbestimmung auf die Messung der Empfangsfeldstärke von WLAN-Aussendungen zurückgreifen. Im Rahmen der Diplomarbeit soll untersucht werden, ob sich ebenfalls eine Postionsbestimmung anhand von Laufzeiten der WLAN-Signale umsetzen lässt. Bedingung hierbei ist, dass der Lokalisierungsdienst - eine reine Softwarelösung darstellt und keine Modifikationen an Hard- oder Firmware voraussetzt und - die Lokalisierung ohne für diese Zwecke ausgelegte Spezialhardware umsetzbar ist. Diese Anforderungen sollen sicherstellen, dass der zu entwickelnde Lokalisierungsdienst mit bereits installierter, handelsübliche Hardware umsetzbar ist. Es sind in Frage kommende Verfahren zur Bestimmung der Signallaufzeit zu erörtern. Für die Laufzeitmessung in Frage kommenden Zeitquellen sollen zugänglich gemacht und auf ihre Tauglichkeit untersucht werden. Durch Messreihen ist zu untersuchen, ob mit den vorgeschlagenen Messverfahren eine Lokalisierung möglich ist und in welchem Rahmen sich die zu erwartende Genauigkeit bewegt. Die in dieser Arbeit beschriebenen Konzepte sollen im Rahmen einer Proof of Concept Anwendung implementiert werden. Die Software soll unter dem Gesichtspunkt der Wiederverwendbarkeit entwickelt werden, um eine spätere Nutzung im Rahmen anderer Projekte zu ermöglichen.

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Location-sharing services have become increasingly popular with the proliferation of smartphones and online social networks. People share their locations with each other to record their daily lives or satisfy their social needs. At the same time, inappropriate disclosure of location information poses threats to people's privacy. One of the reasons why people fail to protect their location privacy is the difficulty of using the current mechanisms to manually configure location-privacy settings. Since people's location-privacy preferences are context-aware, manual configuration is cumbersome. People's incapability and unwillingness to do so lead to unexpected location disclosures that violate their location privacy. In this thesis, we investigate the feasibility of using recommender systems to help people protect their location privacy. We examine the performance of location-privacy recommender systems and compare it with the state-of-the-art. We also conduct online user studies to understand people's acceptance of such recommender systems and their concerns. We revise our design of the systems according to the results of the user studies. We find that user-based collaborative filtering can accurately recommend location-privacy preferences and outperform the state-of-the-art when training data are insufficient. From users' perspective, their acceptance of location-privacy recommender systems is affected by the openness and the context of recommendations and their privacy concerns about the systems. It is feasible to use data obfuscation or decentralisation to alleviate people's concerns and meanwhile keep the systems robust against malicious data attacks.

Made available in DSpace on 2016-12-23T14:33:42Z (GMT). No. of bitstreams: 1 Dissertacao de Rickardo Bebiazze N Vieira.pdf: 1392353 bytes, checksum: 366b2c9211d12eb4c61b77094f0d2ecd (MD5) Previous issue date: 2010-08-18 / The increasing demand for Location-Based Services (LBSs) has increased the need for solutions that consider the privacy of queries in public LBSs. The currently proposed approaches rely mainly on the construction of an anonymizing spatial region (ASR) based on a rectangle containing a user consultant and at least other K&#8722;1 users, thereby setting a K level of anonymity for the query performed. In parallel, P2P infrastructures have been designed to bear the burden of mobility that has characterized much of LBSs users, given the remarkable technological advance that provided the availability of modern resources (such as processing of the GPS signal) on portable devices, such as smartphones. However, the answers obtained from LBSs, considering the context of research conducted, is limited to the indication of points of interest near the consultant user s location, not being able to indicate a route to the destination indicated. This paper aims at presenting the proposal AnoniMobi, whose main objective is to provide routes to points of interest in response to anonymous queries made to public LBSs, ensuring the privacy level requested by the consultant user. The approach separates itself from traditional proposals for formation of spatial region anonymization (ASR) when considering the use of points (random means) that allow you to group coordinates in a non-deterministic way. Additionally, an infrastructure based on CAN P2P architecture is designed to support self-organization needed for structuring and grouping users of the system / A crescente demanda por Serviços Baseados em Localização (SBLs) tem despertado a necessidade de soluções que considerem a privacidade das consultas efetuadas a SBLs públicos. As abordagens atualmente propostas apoiam-se, principalmente, na construção de uma região espacial de anonimização (ASR) baseada em um retângulo que contenha o usuário consultante e, pelo menos, outros K &#8722;1 usuários, estabelecendo, assim, um nível K de anonimidade para a consulta realizada. Paralelamente, infraestruturas P2P têm sido elaboradas para suportar a carga de mobilidade que vem caracterizando boa parte dos usuários de SBLs, haja vista o notável avançado tecnológico que proporcionou a disponibilização de modernos recursos (como capacidade de processamento do sinal GPS) em aparelhos portáteis, tais como os smartphones. Contudo, as respostas obtidas dos SBLs, considerado o contexto das pesquisas realizadas, limitam-se à indicação dos pontos de interesse mais próximos da localização do usuário consultante, não sendo capazes de indicar uma rota para o destino apontado. O presente trabalho visa apresentar a proposta AnoniMobi, cujo objetivo principal é fornecer rotas para pontos de interesse como resposta a consultas anônimas efetuadas a SBLs públicos, garantindo o nível de privacidade requerido pelo usuário consultante. A abordagem desenvolvida desvincula-se de propostas tradicionais para formação de região espacial de anonimização (ASR) ao considerar o uso de pontos (médias aleatórias) que permitem agrupar coordenadas de modo não-determinístico. Adicionalmente, uma infraestrutura P2P baseada na arquitetura CAN é elaborada como suporte à auto-organização necessária para estruturar e agrupar os usuários do sistema