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

Indiana University-Purdue University Indianapolis (IUPUI) / Mobile IP though allows mobility features to a node it suffers from signaling Latencies which are mainly incurred due to the fact that the MN itself is involved in the handover process. To overcome this problem proxy mobile IPv6(PMIPv6) was defined where the mobility signaling is taken care of by a proxy server while keeping track of the MN's movement. PMIPv6 has considerably reduced the handover latency but the demand for real time applications over the network has increased tremendously due to recent explosion of the cloud era. My thesis focuses on increasing the L3 handoff signaling efficiency by reducing the latency. This is achieved by our idea to do both the AAA authentication as well as the LMA registration in PMIPv6 at the same time. The simulation results show that our proposed approach perform better than the current PMIPv6 L3 handover signaling reducing the latency as well as packet loss.