近年來無線行動通訊網路蓬勃發展，各種不同型態的無線網路能夠提供不一樣的涵蓋範圍與服務項目。而目前國內外許多機構與學者更致力於各種無線網路的整合研究，期望能在異質性網路(heterogeneous network)下達到無所不在的網路通訊服務。對移動終端而言，在各種不同型態的無線網路之間做切換並且使得現有連線不中斷的動作叫做換手(handoff)。在換手之前，移動終端必須要先能夠感知到“未來即將要做換手”並打開移動終端上所有的網路介面來偵測可用的網路環境，此動作叫做網路探索(network discovery)。在網路探索期間，移動終端必須藉由其所有的網路介面來搜尋可用的無線網路基地台、連線、認證、取得IP等相關工作。假如沒有足夠的時間進行上述網路探索流程，則將會導致移動終端上的現有連線中斷。但持續的進行網路探索動作，則會導致過多的能源消耗。為了解決上述問題，本論文針對室內環境提出一個NDMD (Network Discovery with user Motion Detection)網路探索演算法，此演算法可以幫助移動終端僅利用接收訊號強度(Received Signal Strength)並且不用藉助定位系統來預測其使用者之移動行為。藉由得知其使用者行為資訊，移動終端可以適時的進行網路探索動作，來降低換手失敗機率與在移動終端上的電池能源消耗。模擬結果顯示出NDMD網路探索演算法可以有效的預測使用者行為、降低能源消耗以及改善換手效能。

Integration of different wireless network environments can provide a universal ubiquitous coverage and different types of service for a mobile terminal (MT). To achieve ubiquitous computing, an MT needs to switch its connection between different or similar types of wireless networks. This switching process is called handoff. Before handoff, an MT needs to be aware of the impending handoff and discover what other wireless networks are available in time. During the network discovery stage, an MT needs to activate its interfaces for base station searching, association, AAA (Authentication, Authorization, Accounting), address acquisition, etc., which consumes large power. Thus, if not enough time is spared for the MT to do above procedures, the connection will lose. But frequent interfaces activation can lead to unnecessary battery drain. In this thesis, a predictive algorithm for Network Discovery based on user Motion Detection, NDMD, is proposed to solve above problems in an indoor environment. NDMD can simply use the received signal strength (RSS) to predict a user’s moving behavior without any assistance of a positioning system. Based on the prediction of user’s moving behavior, an MT can do the network discovery in time to reduce handoff dropped rate when a handoff is needed. Furthermore, an MT can postpone the activation of its interfaces until necessary to save the battery power. Simulation results show that NDMD can effectively detect user behavior, reduce power consumption in network discovery and improve handoff quality.

Title Page I
Abstract (Chinese) II
Abstract (English) III
Acknowledgement (Chinese) IV
Contents V
Figure Index VI
Table Index VII
1. Introduction 1
1.1. Background 1
1.2. Motivation 2
1.3. Approach 2
1.4. Contribution 3
1.5. Thesis Organization 3
2. Related Work 4
2.1. Proactive and Reactive Method 4
2.2. Location-aware Method 5
3. Predictive Algorithm for Network Discovery 6
3.1. UMD Mechanism 6
3.1.1. Preliminary 6
3.1.2. Methodology 7
3.2. NDMD Algorithm 9
3.3. Algorithm Analysis 11
3.3.1. Factors Affecting Algorithm Performance 11
3.3.2. Selection of UMD Parameters 13
4. Performance Evaluation 15
4.1. Evaluation of UMD Mechanism 15
4.1.1. Comprehensive Analysis 15
4.1.2. Feasibility of UMD Mechanism 22
4.1.3. Experiment 24
4.2. Evaluation of NDMD Algorithm 26
5. Conclusion and Future Work 30
Reference 31

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