人智運算(Human Computation)是近年來最熱門的研究領域之一，而適地性服務(Location-based service)也因此衍生出許多研究議題。由於傳統上在搜集資訊時往往會浪費過多的人力資源，所以我們希望使用者藉由玩遊戲的方式背後完成我們想要的事情。在此篇論文中，我們利用GWAP(Games with a Purpose)的概念設計一套行動地理標籤系統，系統採用玩家分享自身附近景點資訊再透過其他玩家來到相同地點做驗證的方式，讓玩家在一邊玩遊戲的過程中搜集相關地理資訊，另一方面達到景點資訊是可信賴的目的。 然而，對系統而言要如何選題給玩家驗證才能提升整體效能，為了解決這些問題，我們在選題策略上提出三種不同的任務分配演算法，再透過一連串的實驗模擬來證明我們所設計的系統確實能夠在選題上達到好的效能，最後我們考慮到現實生活中可能的情形後將此系統實作在智慧型手機上(Android Phone)。 / Human Computation is popular recently and have become one of the hottest research topics, therefore Location-based service also drives a lot of issues. Due to collect information always cause too many manpower- wasted in tradition , so we hoped that the users to complete the things which we want by play game’s way .In this thesis, we based on the concept of ‘Games with a Purpose’(GWAP) to develop a mobile geospatial tagging system, system adopt player share self nearby scenic spots information and then through another players come the same place do the verification, allowing the player to play the game in the side of the process of collecting the relevant geographic information, on the other hand to achieve the purpose of scenic spots information is reliable. However, how to choose topics for player verification can effectively evaluate the system performance, to solve these problems we propose three different selection strategies on the task assignment algorithms, then through a series of simulation experiments designed to prove that our system can indeed achieve good performance on the topics, finally we take into account real life situations and implement in the smart phone (Android Phone).

This research endeavor investigated the possibility to combine the visual advantages of both graphical schedule visualization methods, the Linked Gantt Charts (LGC) and Flowline graphs (FLG), derived from the activity-based and location-based scheduling systems, to help resolve some of their shortcomings by capitalizing on their combined strengths. In order to accomplish the goal of the research, a graphical representation system that combines these two scheduling visualization methods, LGC and FLG, is developed. Afterwards, the research attempted to empirically validate the ability of the proposed tool to visually communicate and combine logical and spatial relationships between scheduled activities. This is compared to comprehending the same information by looking at a stand-alone LGC or FLG. The accuracy and time, of deciphering various details of a sample project schedule, are used as parameters to evaluate the proposed scheduling visualization tool, and compare it to the existing LGC and FLG systems. The Hybrid Graphical Representation (HGR) is the tool developed by this research to combine Linked Gantt Chart bars from the activity-based scheduling approach, and flow-lines from the location-based scheduling approach. The HGR concept is founded on the basic idea that both LGC and FLG share a common X-axis, Time. The only difference is in a LGC the Activities are listed on the Y-axis, while the FLG shows Locations on the Y-axis. This research proposed adding a third dimension to the FLG, listing the project Activities on a Z-axis. Viewing the HGR 3D graph from the top, the user will observe the Gantt bars with Time on the X-axis and the Activities listed on the Z-axis. Observing the schedule from the front view, the user will see the flow-lines developed from the location-based scheduling approach with Locations on the Y-axis and Time on the X-axis. After conducting a series of online surveys measuring the time and accuracy of using a prototype HGR schedule, it was found that the users were able to reap the benefits of both scheduling approaches (LGC and FLG), and visually link and communicate information concerning the activities' logical relationships and spatial relationships. However, it took the participants a relatively longer time to achieve that higher accuracy utilizing the HGR tool.

Internet and mobile communications have had a profound effect on today's society. New services are constantly being deployed, in which personal data are being processed in return for personally tailored services. While mobile networks lay the groundwork for new innovative services, at the same time they pose numerous privacy challenges. There is the risk that honest citizens participating in mobile communications will have their privacy invaded for "the greater good". We argue that it is highly important that individuals are empowered to retain control over their personal spheres. Thus, the goal of this thesis is to design and evaluate anonymous overlay networks} adapted for mobile networks that allow users to control which information leaves their personal spheres in a mobile communication. Technically, by using an anonymous overlay network, users can communicate with their communication partners without disclosing their network identities. In this thesis, we propose three different anonymous overlay networks tailored for mobile networks. First, two approaches are proposed for anonymous browsing on the mobile Internet, namely mCrowds and a Tor-based approach. By applying theoretical analysis and\,/\,or practical experiments, we show that these approaches offer an appropriate trade-off between the offered degree of anonymity and performance loss. Second, an anonymous overlay network for use in mobile ad hoc networks - Chameleon - is suggested. Besides the actual design of these anonymous overlay networks, this thesis provides novel contributions in other essential areas of privacy protection and anonymous communication. First, also non-technical aspects of privacy protection are thoroughly discussed, including legal, social, and user interface aspects. Second, we survey existing metrics for quantifying anonymity and also propose new ideas regarding anonymity metrics. Third, we review and classify existing mechanisms for anonymous communication in mobile ad hoc networks. Lastly, we also propose a cryptographic technique for building up the user base of an anonymous overlay network in a secure and privacy-friendly manner.

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.

Providing computer-aided support for human analysis of videos has been a battle of extremes. Powerful solutions exist, but they tend to be domain-specific and complex. The user-friendly, simple systems provide little analysis support beyond basic media player functionality. We propose a human-centered, domain-independent solution between these two points. Our proposed model and system, VAST, is based on our experience in two diverse video analysis domains: science and athletics. Multiple-perspective location metadata is used to group related video clips together. Users interact with these clip groups through a novel interaction paradigm ? views. Each view provides a different context by which users can judge and evaluate the events that are captured by the video. Easy conversion between views allows the user to quickly switch between contexts. The model is designed to support a variety of user goals and expertise with minimal producer overhead. To evaluate our model, we developed a system prototype and conducted several rounds of user testing requiring the analysis of volleyball practice videos. The user tasks included: foreground analysis, ambiguous identification, background analysis, and planning. Both domain novices and experts participated in the study. User feedback, participant performance, and system logs were used to evaluate the system. VAST successfully supported a variety of problem solving strategies employed by participants during the course of the study. Participants had no difficulty handling multiple views (and resulting multiple video clips) simultaneously opened in the workspace. The capability to view multiple related clips at one time was highly regarded. In all tasks, except the open-ended portion of the background analysis, participants performed well. However, performance was not significantly influenced by domain expertise. Participants had a favorable opinion of the system?s intuitiveness, ease of use, enjoyability, and aesthetics. The majority of participants stated a desire to use VAST outside of the study, given the opportunity.

<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>

生活於行動網絡與社群網站發展快速的今日，人們逐漸依賴利用隨手可得之行動裝置紀錄生活，打卡儼然成為生命紀錄的方式之一。本研究致力於提供使用者個人化之打卡系統，讓使用者在選用群眾所推薦的地理標籤名稱之餘，同時保有個人化情感傾向特色之地理標籤名稱。透過基於地緣情感關係連結之群眾推薦地理標籤名稱，我們提供更貼近使用者個人情感偏好之地理標籤，讓使用者在省下自行建立個人化地理標籤名稱上的繁瑣程序的同時，還能同時保有個人化特色，更能夠作為未來回憶時之線索。在實驗設計上，我們邀請共二十八位受試者使用本研究所開發之打卡系統，利用三週的時間真實模擬生活中的打卡情況。我們分析受試者進行打卡時所選用的地理標籤名稱來源作為評估之依據，透過實驗結果顯示基於地緣情感關係連結之群眾推薦地理標籤名稱模型，能夠推薦較適合的地理標籤名稱給使用者使用。因此本研究有效利用特定群眾之力量協助使用者建立個人化之地理資訊。 / In the present, people used to have one or more mobile devices. With the mobile devices thriving, the social network is also getting more popular. People rely on the mobile devices or social network to record their lives—“Check in” is becoming a kind of “lifelog”. Our research provides user a customized check in system, which can free users from the troublesome procedures, naming the geotag by their own, meanwhile; they can still have their own style. Furthermore, this customized name of geotag will be the hint to their memory in the future. When it comes to the experiment, we have 28 users experience our check in system, and we spent three weeks to simulate the situation of check in in the real life. We analyze the users’ name of geotag and use it as the result to estimate. According to the result of our experiment, the Location-Based Emotion Connected model can recommend a better name of geotag to the users. Consequently, our research the effective help people to construct personalizes geographic information.