林永松臺灣大學：資訊管理學研究所郭文政Kuo, Wen-ChengWen-ChengKuo2007-11-262018-06-292007-11-262018-06-292006http://ntur.lib.ntu.edu.tw//handle/246246/54166近年來，無線感測網路在於諸多應用上都具有其優越性。然而，在硬體和環境的限制下，感測器對於能源消耗具有高度限制性。採用資料集縮(data aggregation)能夠有效率地降低資料傳送量，以達到節省能耗的目的。 本篇論文研究在感測器具有資料集縮能力之無線感測網路中，使用集縮樹的適當路由分配以完成最大化系統生命週期。我們將問題化為一個數學模式，目的函式為最大化系統生命週期，並採用拉格蘭日鬆馳法獲得近似最佳解。In recent years, wireless sensor networks have the advantages in a variety of applications. However, due to the limitations of hardware and the environment, the sensors are highly energy-constrained. By adopting data aggregation, we can effectively reduce the amount of data and thereby save energy consumption. In this thesis, we adopt data aggregation trees to efficiently arrange routing assignments in order to maximize the system lifetime of data-centric WSNs. We model the problem a mathematical formulation, where the objective function is to maximize the system lifetime, and use Lagrangean Relaxation method to derive an optimal solution.謝 詞 I 論文摘要 II THESIS ABSTRACT III Table of Contents IV List of Tables VI List of Figures VII Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 3 1.3 Literature Survey 4 1.3.1 Data Aggregation Tree 4 1.3.2 Clustering 6 1.3.3 Genetic Algorithm 7 Chapter 2 Problem Formulation 9 2.1 Problem Description 9 2.2 Problem Notation (IP) 12 2.3 Problem Formulation (IP) 14 Chapter 3 Solution Approach 17 3.1 Introduction to Lagrangean Relaxation Method 17 3.2 Lagrangean Relaxation (LR) 19 3.4.1 Subproblem 1 (related to decision variable 、 ) 21 3.4.2 Subproblem 2 (related to decision variable ) 22 3.4.3 Subproblem 3 (related to decision variable 、 ) 23 3.4.4 Subproblem 4 (related to decision variable ) 24 3.3 The Dual Problem and the Subgradient Method (IP) 25 Chapter 4 Getting Primal Feasible Solutions 27 4.1 Lagrangean Relaxation Results 27 4.2 Getting Primal Feasible Solutions 27 4.3 Simple Heuristic Algorithms 30 Chapter 5 Computational Experiments 31 5.1 Experiment Environment 31 5.2 Random Network 33 5.2.1 Network Topology 33 5.2.2 Solution Quality 34 5.3 Grid Network 37 5.3.1 Network Topology 37 5.3.2 Solution Quality 38 5.4 Result Discussion 41 Chapter 6 Conclusion and Future Work 43 6.1 Conclusion 43 6.2 Future Work 44 References 47603547 bytesapplication/pdfen-US生命週期資料集縮高效率節能資料中心路由最佳化拉格蘭日鬆弛法整數線性規劃無線感測網路。LifetimeData aggregationEnergy-EfficientData-centric RoutingOptimizationLagrangean Relaxation MethodInteger Linear ProgrammingWireless Sensor Network.otherhttp://ntur.lib.ntu.edu.tw/bitstream/246246/54166/1/ntu-95-R93725039-1.pdf