In this thesis we look into usability in the context of smartphone applications. We selected three research areas to investigate, namely Wireless Personal Area Networks, Context-awareness, and Remote Information Access. These areas are investigated through a series of experiments, which focuses on important aspects of usability within software applications. Additionally, we mainly use smartphone devices in the experiments. In experiment 1, Multi-Platform Bluetooth Remote Control, we investigated Wireless Personal Area Networks. Specifically, we implemented a system consisting of two clients, which were created for Java ME and Windows Mobile, and integrated with a server application installed on a Bluetooth-enabled laptop. For experiments 2 and 3, Context-aware Meeting Room and PainDroid: an Android Application for Pain Management, we looked closely at the research area of Contextawareness. The Context-aware Meeting Room was created to automatically send meeting participants useful meeting notes during presentations. In experiment 3, we investigated the use of on-device sensors for the Android platform, providing an additional input mechanism for a pain management application, where the accelerometer and magnetometer were used. Finally, the last research area we investigated was Remote Information Access, where we conducted experiment 4, Customised Android Home Screen. We created a system that integrated both a cloud-based server application and a mobile client running on the Android platform. We used the cloud-computing platform to provide context management features, such as the ability to store the user configuration that was automatically pushed to the mobile devices.

Context: This protocol is as a supplementary document to our review paper that investigates security-related challenges and solutions that have occurred during the past decade (from January 2003 to December 2013). Objectives: The objective of this systematic review is to identify security-related challenges, security goals and defenses in ubiquitous computing by answering to three main research questions. First, demographic data and trends will be given by analyzing where, when and by whom the research has been carried out. Second, we will identify security goals that occur in ubiquitous computing, along with attacks, vulnerabilities and threats that have motivated the research. Finally, we will examine the differences in addressing security in ubiquitous computing with those in traditional distributed systems. Method: In order to provide an overview of security-related challenges, goals and solutions proposed in the literature, we will use a systematic literature review (SLR). This protocol describes the steps which are to be taken in order to identify papers relevant to the objective of our review. The first phase of the method includes planning, in which we define the scope of our review by identifying the main research questions, search procedure, as well as inclusion and exclusion criteria. Data extracted from the relevant papers are to be used in the second phase of the method, data synthesis, to answer our research questions. The review will end by reporting on the results. Results and conclusions: The expected results of the review should provide an overview of attacks, vulnerabilities and threats that occur in ubiquitous computing and that have motivated the research in the last decade. Moreover, the review will indicate which security goals are gaining on their significance in the era of ubiquitous computing and provide a categorization of the security-related countermeasures, mechanisms and techniques found in the literature. (authors' abstract)

The rapid development of mobile technologies has facilitated users to generate and store files on mobile devices. However, it has become a challenging issue for users to search efficiently and effectively for files of interest in a mobile environment that involves a large number of mobile nodes. In this thesis, file management and retrieval alternatives have been investigated to propose a feasible framework that can be employed on resource-limited devices without altering their operating systems. The file annotation and retrieval framework (FARM) proposed in the thesis automatically annotates the files with their basic file attributes by extracting them from the underlying operating system of the device. The framework is implemented in the JME platform as a case study. This framework provides a variety of features for managing the metadata and file search features on the device itself and on other devices in a networked environment. FARM not only automates the file-search process but also provides accurate results as demonstrated by the experimental analysis. In order to facilitate a file search and take advantage of the Semantic Web Technologies, the SemFARM framework is proposed which utilizes the knowledge of a generic ontology. The generic ontology defines the most common keywords that can be used as the metadata of stored files. This provides semantic-based file search capabilities on low-end devices where the search keywords are enriched with additional knowledge extracted from the defined ontology. The existing frameworks annotate image files only, while SemFARM can be used to annotate all types of files. Semantic heterogeneity is a challenging issue and necessitates extensive research to accomplish the aim of a semantic web. For this reason, significant research efforts have been made in recent years by proposing an enormous number of ontology alignment systems to deal with ontology heterogeneities. In the process of aligning different ontologies, it is essential to encompass their semantic, structural or any system-specific measures in mapping decisions to produce more accurate alignments. The proposed solution, in this thesis, for ontology alignment presents a structural matcher, which computes the similarity between the super-classes, sub-classes and properties of two entities from different ontologies that require aligning. The proposed alignment system (OARS) uses Rough Sets to aggregate the results obtained from various matchers in order to deal with uncertainties during the mapping process of entities. The OARS uses a combinational approach by using a string-based and linguistic-based matcher, in addition to structural-matcher for computing the overall similarity between two entities. The performance of the OARS is evaluated in comparison with existing state of the art alignment systems in terms of precision and recall. The performance tests are performed by using benchmark ontologies and the results show significant improvements, specifically in terms of recall on all groups of test ontologies. There is no such existing framework, which can use alignments for file search on mobile devices. The ontology alignment paradigm is integrated in the SemFARM to further enhance the file search features of the framework as it utilises the knowledge of more than one ontology in order to perform a search query. The experimental evaluations show that it performs better in terms of precision and recall where more than one ontology is available when searching for a required file.

An increasing number of technologies and applications have begun to focus on mobile computing and the wireless Web as a way to conduct commerce-oriented transactions. M-commerce Websites that are usability friendly must emphasize information quality, system quality, and service quality, as these are proxy measures to mobile commerce user satisfaction (MCUS). Measureable variables can help researchers to understand how satisfaction induces users to return to a Website. Although several studies on m-commerce user satisfaction focus on content quality, appearance, service quality, and ease of use, the results of these studies were inconclusive because they do not address contextual usability barriers for the mobile Web. The task context, which consist of two activities, search and transaction, is a prominent contextual factor that affects mobile usability positively or negatively in achieving MCUS. The researcher determined what end user expectations and actual usage beliefs result in disconfirmation or MCUS. An empirical field study of an online retailer and search platform was conducted using a sampling of 98 participants. The participants used a smartphone to search and complete a transaction in the participant's natural setting. The study results indicated that expectations and usability measures including efficiency, errors, and flexibility significantly influence actual usage leading to MCUS. The original Expectation Confirmation Theory (ECT) model assumed that user's expectations could lead to actual usage. However, in the modified ECT model, it is the actual usage experience itself that determines MCUS. Disconfirmation is an inconsistent measure tied to actual usage, purchase intent, or satisfaction as mobile users often make purchases from m-commerce Websites because of convenience, flexibility, efficiency, and accessibility. If the user experiences an error during the search or transaction activity, such a drawback can cost a mobile Website future traffic, the loss of a transaction, and lead to lower levels of satisfaction.

Context: This protocol is as a supplementary document to our review paper that investigates security-related challenges and solutions that have occurred during the past decade (from January 2003 to December 2013). Objectives: The objective of this systematic review is to identify security-related challenges, security goals and defenses in ubiquitous computing by answering to three main research questions. First, demographic data and trends will be given by analyzing where, when and by whom the research has been carried out. Second, we will identify security goals that occur in ubiquitous computing, along with attacks, vulnerabilities and threats that have motivated the research. Finally, we will examine the differences in addressing security in ubiquitous computing with those in traditional distributed systems. Method: In order to provide an overview of security-related challenges, goals and solutions proposed in the literature, we will use a systematic literature review (SLR). This protocol describes the steps which are to be taken in order to identify papers relevant to the objective of our review. The first phase of the method includes planning, in which we define the scope of our review by identifying the main research questions, search procedure, as well as inclusion and exclusion criteria. Data extracted from the relevant papers are to be used in the second phase of the method, data synthesis, to answer our research questions. The review will end by reporting on the results. Results and conclusions: The expected results of the review should provide an overview of attacks, vulnerabilities and threats that occur in ubiquitous computing and that have motivated the research in the last decade. Moreover, the review will indicate which security goals are gaining on their significance in the era of ubiquitous computing and provide a categorization of the security-related countermeasures, mechanisms and techniques found in the literature. (authors' abstract) / Series: Working Papers on Information Systems, Information Business and Operations

Li, Chak Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 150-159). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

Tao Suyi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 51-53). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.2 / Chapter 1.1.1 --- Mobile Phone Applications --- p.3 / Chapter 1.1.2 --- Location Methods --- p.4 / Chapter 1.1.3 --- Location Algorithms --- p.9 / Chapter 1.2 --- AoA Localization --- p.10 / Chapter 1.3 --- The NLOS Problem --- p.11 / Chapter 2 --- AoA Localization --- p.13 / Chapter 2.1 --- Conventional Approach to AoA Localization --- p.14 / Chapter 2.2 --- Least Squares Approach to AoA Localization --- p.16 / Chapter 2.2.1 --- Ordinary Least Squares Approach (OLS) by Pages-Zamora --- p.16 / Chapter 2.2.2 --- The Weighted Least Squares Approach (WLS) --- p.18 / Chapter 2.3 --- Approximate Maximum Likelihood Method (AML) for AoA Localization --- p.19 / Chapter 2.4 --- Simulations --- p.21 / Chapter 3 --- Analysis and Mitigation of NLoS Effects --- p.26 / Chapter 3.1 --- The Non-Line-of-Sight (NLoS) Effects --- p.26 / Chapter 3.2 --- NLoS Mitigation in AoA Localization --- p.27 / Chapter 3.2.1 --- A Selective Model to Suppress NLOS Errors --- p.27 / Chapter 3.2.2 --- Dimension Determination and LOS Identification --- p.29 / Chapter 3.3 --- Simulations --- p.34 / Chapter 3.3.1 --- Experiment 1 --- p.34 / Chapter 3.3.2 --- Experiment 2 --- p.38 / Chapter 4 --- Conclusions and Suggestions for Future Work --- p.42 / Chapter 4.1 --- Conclusions --- p.42 / Chapter 4.2 --- Suggestions for future work --- p.44 / Chapter A --- Derivation of the Cramer Rao Lower Bound (CRLB) for AoA Localization --- p.45 / Chapter A.1 --- CRLB for all LoS --- p.45 / Chapter A.2 --- CRLB for both LoS and NLoS --- p.46 / Chapter B --- Derivation of the Error Covariance for OLS and WLS Estima- tors --- p.48 / Chapter B.1 --- Error Covariance for OLS Estimator --- p.49 / Chapter B.2 --- Error Covariance for WLS Estimator --- p.50 / Bibliography --- p.51

無線通信和移動技術已經從根本上改變了人和人之間相互通信的方式，隨著像智能手機這樣功能強大的移動設備不斷普及，現在我們有更多的機會去監測用戶的運動狀態、社交情況和地理位置等信息。近期，越來越多的基於智能手機的傳感研究相繼出現，這些研究利用智能手機中的多種傳感、定位以及近距離無線設備來識別手機用戶當前的活動狀態和周圍環境。一些可識別用戶活動狀態和監控身體健康狀況的移動應用程式已經被開發并投入使用。儘管如此，當前大部份關於智能手機的研究忽視了這樣一個問題，智能手機是用戶與外界通信的一個指令中心。移動用戶可以使用智能手機用很多種方式聯繫他們的朋友，例如打電話、發送短消息、電子郵件、或者通過即時通信程序或者社交網絡，這些多渠道的通信方式和人與人之間面對面的交流一樣重要，因此智能手機是識別用戶和其他聯繫人的社會關係的關鍵。在本論文中，我們提出用智能手機中 獨有的多渠道用戶通信數據來對用戶的的社會關係進行分類。作為我們研究的開始，我們生成人工的通信數據並且用社交矩陣來為人與人之間的通信建立模型，這也幫助我們測試了很多可以應用在此類問題的數據挖掘算法。接下來，我們通過招募真實用戶來採集他們的各種社交通信數據，這些數據包括手機通話記錄、電子郵件、社交網絡（Facebook和Renren）和面對面的交流。通過在社交矩陣上應用不同的分類算法，我們發現SVM的分類性能要超過KNN和決策樹算法，SVM對於社會關係的分類準確率可以達到82.4%。我們也對來自不同渠道的通信數據進行了比較，最終發現來自社交網絡和面對面交流的數據在社交關係分類中起更大的作用。另外，我們通過使用降低維度算法可以把社交矩陣從65維度映射到9維度，關係分類的準確率卻沒有明顯降低，在降低維度的過程中我們也可以提取出用戶主要的通信特徵，從而更好地解釋社會關係分類的原理。最後，我們也應用了CUR矩陣分解算法從社交矩陣65列中選出13列建立新的社交矩陣，關係分類的準確率從82.4%降低到77.7%，但是我們卻可以通過 CUR來選擇合適的傳感器抽樣採集頻率，這樣可以在利用手機採集數據過程中節省更多手機電量。 / Wireless Communications and Mobile Computing have fundamentally changed the way people interact and communicate with each other. The proliferation of powerful and programmable mobile devices, smartphones in particular, has offered an unprecedented opportunity to continuously monitor the physical, social and geographical activities of their users. Recently, much research has been done on smartphone-based sensing which leverages the rich set of sensing, positioning and short-range radio capabilities of the smartphones to identify the context of user activities and ambient environment conditions. Mobile applications for personal behavior tracking and physical wellness monitoring have also been developed. Despite that, most of the existing work in mobile sensing has neglected the role of smartphone as the command-center of the user’s communications with the outside world. As mobile users contact their friends via phone, SMS, emails, instant messaging, and other online social-networking applications, these multi-modal communication activities are as equally important as physical activities in proling one’s life. They also hold the key to understand the user’s social relationship with other people of interest. In this thesis, we propose to use the unique multi-model interaction data from smartphone to classify social relationships. To jump start our study, we generate articial interaction data and build social interaction matrix to modeMl the interaction between people. This also helps us in testing a wide range of data mining analysis techniques for this type of problem. We then carry out a social interaction data collection campaign with a group of real users to obtain real-life multi-modal communication data, e.g., phone call, Email, online social network(Facebook and Renren), and physical location/proximity. After applying different classification algorithms on social interaction matrix, we find that SVM outperforms KNN and decision tree algorithms, with a classification accuracy of 82.4% (the accuracies of KNN and decision tree are 79.9% and 77.6%, respectively). We also compare the data from different interaction channels and finally find that on-line social network and location/proximity data contribute more to the overall classification accuracy. Additionally, with dimensionality reduction algorithms, the social interaction matrix can be embedded from 65 to 9 dimensional space while preserving the high classification accuracy and we also get principle interaction features as by-product. At last, we use CUR decomposi¬tion to select 13 out of the 65 features in the social interaction matrix. The classification accuracy drops from 82.4% to 77.7% after CUR decomposition. But it can help to determine the right sensor sampling frequency so as to enhance energy efficiency for social data collection. / Detailed summary in vernacular field only. / Sun, Deyi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 90-96). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Research Background --- p.7 / Chapter 2.1 --- Related work of social relationship analysis --- p.7 / Chapter 2.1.1 --- Community detection in social network --- p.8 / Chapter 2.1.2 --- Social influence analysis --- p.10 / Chapter 2.1.3 --- Modeling social interaction data --- p.10 / Chapter 2.1.4 --- Social relationship prediction --- p.12 / Chapter 2.2 --- Classification methodologies --- p.14 / Chapter 2.2.1 --- Algorithms for social relationship classification --- p.14 / Chapter 2.2.2 --- Algorithms for dimensionality reduction --- p.16 / Chapter 3 --- Problem Formulation of Relationship Classicification --- p.19 / Chapter 3.1 --- Multi-modal data in smartphones --- p.20 / Chapter 3.2 --- Formulation of relationship classification problem --- p.21 / Chapter 3.3 --- Refinement of feature definition and energy efficiency --- p.27 / Chapter 3.4 --- Chapter summary --- p.28 / Chapter 4 --- Social Interaction Data Acquisition --- p.30 / Chapter 4.1 --- Social interaction data collection campaign overview --- p.31 / Chapter 4.2 --- Format of raw interaction data --- p.33 / Chapter 4.3 --- Building social interaction matrix with real-life interaction data --- p.37 / Chapter 4.4 --- Chapter summary --- p.43 / Chapter 5 --- Statistical Analysis of Social Interaction Data --- p.45 / Chapter 5.1 --- Coverage of social interaction data --- p.46 / Chapter 5.2 --- Social relationships statistics --- p.48 / Chapter 5.3 --- Social relationship interaction patterns --- p.52 / Chapter 5.4 --- Chapter summary --- p.59 / Chapter 6 --- Automatic Social Relationship Classification Based on Smartphone Interaction Data --- p.61 / Chapter 6.1 --- Comparison of different classification algorithms --- p.62 / Chapter 6.2 --- Advantages of multi-modal interaction data --- p.65 / Chapter 6.3 --- Comparison of interaction data in different communication channels --- p.67 / Chapter 6.4 --- Dimensionality reduction on social interaction data --- p.72 / Chapter 6.5 --- Discussions in deploying social relationship classification application --- p.80 / Chapter 6.5.1 --- Considerations of user privacy --- p.81 / Chapter 6.5.2 --- Saving smartphone resources --- p.82 / Chapter 6.6 --- Chapter summary --- p.83 / Chapter 7 --- Conclusion and Future Work --- p.86 / Bibliography --- p.90

by Chiu-Fai Fong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 76-81). / Abstract --- p.i / Acknowledgments --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Distributed Data Caching --- p.2 / Chapter 1.2 --- Mobile Terminal Tracking --- p.5 / Chapter 1.3 --- Thesis Overview --- p.10 / Chapter 2 --- Personal Communication Network --- p.11 / Chapter 2.1 --- Network Architecture --- p.11 / Chapter 2.2 --- Resource Limitations --- p.13 / Chapter 2.3 --- Mobility --- p.14 / Chapter 3 --- Distributed Data Caching --- p.17 / Chapter 3.1 --- System Model --- p.18 / Chapter 3.1.1 --- The Wireless Network Environment --- p.18 / Chapter 3.1.2 --- Caching Protocol --- p.19 / Chapter 3.2 --- Caching at Mobile Computers --- p.22 / Chapter 3.3 --- Broadcasting at the Server --- p.24 / Chapter 3.3.1 --- Passive Strategy --- p.27 / Chapter 3.3.2 --- Active Strategy --- p.27 / Chapter 3.4 --- Performance Analysis --- p.29 / Chapter 3.4.1 --- Bandwidth Requirements --- p.29 / Chapter 3.4.2 --- Lower Bound on the Optimal Bandwidth Consumption --- p.30 / Chapter 3.4.3 --- The Read Response Time --- p.32 / Chapter 3.5 --- Experiments --- p.35 / Chapter 3.6 --- Mobility Concerns --- p.42 / Chapter 4 --- Mobile Terminal Tracking --- p.44 / Chapter 4.1 --- Movement Model --- p.45 / Chapter 4.2 --- Optimal Paging --- p.48 / Chapter 4.3 --- Transient Analysis --- p.52 / Chapter 4.3.1 --- The Time-Based Protocol --- p.55 / Chapter 4.3.2 --- Distance-Based Protocol --- p.59 / Chapter 4.4 --- The Reverse-Guessing Protocol --- p.64 / Chapter 4.5 --- Experiments --- p.66 / Chapter 5 --- Conclusions & Future Work --- p.71 / Chapter 5.1 --- Distributed Data Caching --- p.72 / Chapter 5.2 --- Mobile Terminal Tracking --- p.73 / Bibliography --- p.76 / A Proof of NP-hardness of the Broadcast Set Assignment Problem --- p.82

The prevalence of wireless networks is on the increase. Society is becoming increasingly reliant on ubiquitous computing, where mobile devices play a key role. The use of wireless networking is a natural solution to providing connectivity for such devices. However, the availability of infrastructure in wireless networks is often limited. Such networks become dependent on wireless ad hoc networking, where nodes communicate and form paths of communication themselves. Wireless ad hoc networks present novel challenges in contrast to fixed infrastructure networks. The unpredictability of node movement and route availability become issues of significant importance where reliability is desired.To improve reliability in wireless ad hoc networks, predicting future connectivity between mobile devices has been proposed. Predicting connectivity can be employed in a variety of routing protocols to improve route stability and reduce unexpected drop-offs of communication. Previous research in this field has been limited, with few proposals for generating future predictions for mobile nodes. Further work in this field is required to gain a better insight into the effectiveness of various solutions.This thesis proposes such a solution to increase reliability in wireless ad hoc routing. This research presents two novel concepts to achieve this: the Communication Map (CM), and the Future Neighbours Table (FNT). The CM is a signal loss mapping solution. Signal loss maps delineate wireless signal propagation capabilities over physical space. With such a map, connectivity predictions are based on signal capabilities in the environment in which mobile nodes are deployed. This significantly improves accuracy of predictions in this and in previous research. Without such a map available, connectivity predictions have no knowledge of realistic spatial transmission ranges. The FNT is a solution to provide routing algorithms with a predicted list of future periods of connectivity between all nodes in an established wireless ad hoc network. The availability of this information allows route selection in routing protocols to be greatly improved, benefiting connectivity. The FNT is generated from future node positional information combined with the CM to provide predicted signal loss estimations at future intervals. Given acceptable signal loss values, the FNT is constructed as a list of periods of time in which the signal loss between pairs of nodes will rise above or fall below this acceptable value (predicted connectivity). Future node position information is ideally found in automated networks. Robotic nodes commonly operate where future node task movement is developed and planned into the future, ideal for use in predicted connectivity. Non-automated prediction is also possible, as there exist some situations where travel paths can be predictable, such as mobile users on a train or driving on a highway. Where future node movement is available, predictions of connectivity between nodes are possible.