臺灣博碩士論文加值系統

由於異質無線網路之間的資料存取需求日益增加，行動節點之資料存取會隨著跨越不同無線網路而切換到新的無線網路，而且原本的傳輸也繼續運作不會中斷。這種跨越不同無線網路間，如：2.5G/3G行動通訊網路和IEEE 802.11無線區域網路(WLAN)的切換，稱之為vertical handoff。Vertical handoff與傳統Horizontal handoff的不同點在於vertical handoff會跨越不同Layer 1和Layer 2的protocols，因此，這與傳統的Horizontal handoff相較之下，信號強度已經不是判斷是否切換的唯一條件，還必須考量service types、network bandwidth、QoS Mapping等因素。所以，我們於這篇論文提出一個以信號強度(Received Signal Strength – hysteresis，RSS)為基礎加上考量可用頻寬的線上競爭成本方法(Competitive On-Line，COL)的高效率vertical handoff方法，並整合了Mobile IPv6提供更完整Layer 1到Layer 3的網路切換機制。主要貢獻包括：明顯降低無效的vertical handoff次數，有效抑制vertical handoff的dropping次數，提高網路利用率(Utilization)和減少使用者的網路存取費用。在數據結果上，我們所提的方法在vertical handoff的次數與dropping次數都明顯的少於其他方法，並且能維持throughput的產量。

The demand and amount of data transfer in heterogeneous wireless network increases as the techniques of the third/fourth generation mobile communications (3G/4G) are progressed significantly. Vertical handoff provides continuous and seamless transfer between various wireless mobile networks, which have different data transmission techniques and standards such as GPRS, UMTS, cdma2000 and IEEE 802.11 family WLANs. In contrast to the horizontal handoff, the vertical handoff should consider not only the strength of signal power but also QoS classes, network bandwidth and service types, etc. Most of the previous works proposed the Received Signal Strength (RSS) based methods to set thresholds for making the decision of performing vertical handoff or not. The RSS-based approach yields serious ping-pong effect when the mobile node moves around the overlay area of two heterogeneous wireless networks, which causes unnecessary handoff and increases the handoff overhead. Although the RSS-based approach with the hysteresis technique decreases the number of unnecessary handoffs, it remains large overhead of handoff, which causes some drawbacks, including low network throughput, long handoff delay and high dropping probability. Therefore, an efficient cost-based vertical handoff algorithm is proposed herein to reduce unnecessary handoffs while increasing network throughput, decreasing handoff delay and avoiding connection dropping. Two primary motivations of this paper include the prediction of node mobility from RSS and the adaptive cost-based competitive on-line (COL) method to determine the optimal network for handoff from several available wireless networks. Furthermore, message flows of vertical handoff between 3G/UMTS and WLAN with mobile IPv6 protocol are investigated in detail. Numerical results demonstrate that the proposed cost-based approach outperforms other approaches in the number of vertical handoffs and connection dropping while yielding competitive throughput.

Contents
中文摘要 I
Abstract II
誌 謝 III
Contents IV
Table of tables V
List of figures VI
Chapter 1 Introduction 7
Chapter 2 Networks Model 11
Chapter 3 Adaptive Cost-based Vertical Handoff with Predictive RSS Approach 15
Phase 1. the phase of predictive RSS with hysteresis 15
Phase 2. the phase of determination of optimal wireless network 19
Chapter 4 MIPv6-based Message Flows and Delay Analysis 22
A. Message Flows in MIPv6-based Vertical Handoff Approach 22
B. Delay Analysis of Message Flows in MIPv6-based Vertical Handoff Approach 27
Chapter 5 Numerical Results 31
Chapter 6 Conclusions and Future Works 40
Chapter 7 References 41


Table of tables
Table 1. Cellular communication systems VS. WLANs 11


List of figures
Fig. 1. Heterogeneous network architecture for integration with 3G/UMTS and WLAN 12
Fig. 2. An example of a mobile node moving in a heterogeneous 3G/UMTS and WLAN networks 12
Fig. 3. Two-threshold hysteresis 16
Fig. 4. An example of a mobile node moves from WLAN to the 3G/UMTS Network 23
Fig. 5. Mobile IPv6 Fast Vertical Handoff from 3G to WLAN 24
Fig. 6. Mobile IPv6 Fast Vertical Handoff from WLAN to 3G 25
Fig. 7. Mobile IPv6 Fast Horizontal Handoff from WLAN1 to WLAN2 26
Fig. 8. Vertical handoff delay 28
Fig. 9. Horizontal handoff delay 28
Fig. 10. The number of vertical handoffs of different approaches under various arrival rates 34
Fig. 11. Dropping probabilities of different approaches under various arrival rates 34
Fig. 12. Network throughput of different approaches under various arrival rates 35
Fig. 13. The number of vertical handoffs of different approaches under various mobility 36
Fig. 14. Dropping probabilities of different approaches under various mobility 36
Fig. 15. Network throughput of different approaches under various mobility 37
Fig. 16. The number of vertical handoffs of different approaches under various NDS 38
Fig. 17. Dropping probabilities of different approaches under various NDS 38
Fig. 18. Network throughput of different approaches under various NDS 39

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