近年來，無線感測器網路已成為相當活躍的研究領域。理由之一是，由於不需要安置高價的電源和訊號線，在任何區域中都可輕易地放置無線感測器。然而，佈置無線感測器網路系統仍需要考量諸如佈建成本、系統需求和效能等因素。事實上，佈建不同的系統需要不同的佈建策略來滿足對應的需求。在本論文中，我們考慮三個迥異的系統，並針對每個系統各自的最佳化目標設計高效率低成本的佈建演算法。
首先，我們研究如何安裝無線感測器網路來輔助追蹤系統。在此系統中，被追蹤目標的行為可被定義成數學模組。從前在感測器佈建的研究只考慮感測器與環境模組；我們證實了，透過額外考慮目標模組可大幅地降低佈建成本。以機器人導航為例，我們可用無線感測器網路提供額外的位置資訊來校準機器人的位置。我們針對此系統所設計的演算法的目標，是找出最少量的感測器，來保證機器人不會產生高於需求值的位置誤差。我們證明此最佳化問題的問題複雜度為NP-hard並提出效率較高的演算法。我們並透過廣泛的實驗來評估所提出的演算法的效能。
接著，我們考慮當感測器只能佈建在觀察區域的周圍時要如何佈建系統。此問題相當於，要如何在區域周圍佈建最少的感測器，來盡可能地涵蓋區域中的活動資訊。我們針對以下案例來研究此類問題：佈建基地台在河岸以收集河流中流動的感測測器所取得的河流資訊。我們首先定義此周邊佈建問題，並針對理想佈建環境和真實佈建環境的條件，分析此問題在涵蓋率方面的效能瓶頸。接著，我們提出一套包含初期設定和精煉步驟的佈建流程，來高效率地解決此問題。已我們透過廣泛的模擬和一套土石流觀測系統來評估所提出的演算法的效能。
最後，我們研究將無線感測器佈置在固定地點的佈建問題。一般情況下，感測器只能攜帶固定的能源量，並且必須完成把所有偵測到的資訊透過無線網路傳輸給機器台的工作。針對不同的系統壽命需求，問題在於如何找出每個地點所需要配置的最低能源量。我們透過調整每筆資訊的傳輸路徑來設計演算法以完成所需要的工作內容並達到系統的壽命需求。我們首先定義此限定地點的佈建問題。針對此問題，我們透過線性規劃的方式來尋找整數解，以求得本問題的最佳解和佈建成本的最低值。接著，我們提出一套高效率的演算法來解決此問題。所提出的演算法包括一套考量電量的路徑規劃演算法，可針對電池用量的情況來選擇如何傳輸資訊；另外，包含一套精煉演算法，透過調整整體流量來減少所需攜帶的能源量，用來更進一步地改進第一套演算法的佈建結果。我們用廣泛的模擬來評估所提出的演算法的佈建成本和殘餘電量。結果顯示，我們所提出的演算法所需要的佈建能源量非常接近最低值。

Wireless sensor network (WSN) has been an active research area in recent years. One reason is because the wireless sensors can be deployed in the target area easily without requiring costly wiring. However, this does not imply that the sensors can be deployed at will without considering issues such as cost, application requirements, and system qualities. In fact, different applications require different deployment strategies to fulfill their respective needs. In this thesis, we consider three different kinds of applications and develop efficient deployment algorithms to meet their respective optimization goals.
We first study WSN tracking applications, in which the behavior of the targets is known and can be modeled. While previous work on sensor deployment has considered only sensor and environment models, we show that considering also target models can greatly reduce the deployment cost. We use robot navigation as a case study, in which a WSN is deployed to provide external location references to correct the robot's configuration errors. The goal is to find the minimum number of sensors to provide the required bound on configuration errors. We show that this minimization problem is NP-hard and a number of heuristics are then proposed. The presented algorithms are evaluated through extensive simulations.
We next consider problems in which the sensors can only be deployed at the periphery of the target area to be monitored. The question is what is the minimum number of sensors to be deployed at the periphery to cover as much as possible the activities inside the target area. As a case study, we consider the problem of deploying base stations at the periphery to collect information from mobile sensors moving inside the target area. We first formulate the periphery deployment problem and then analyze its performance bounds in terms of coverage percentage under both ideal and practical deployments. Next, we propose a deployment procedure that consists of initial construction and refinement for solving the periphery deployment problem with polynomial time. Proposed algorithms are evaluated through extensive simulations and a watercourse monitoring system for debris flow.
Finally, we study the problem that deploys wireless sensors at fixed locations. The sensors have to transmit their sensed data back to the sink through multihop communications, but they can only carry battery packs of a fixed capacity. The question is, given a target system lifetime, what is the minimum number of battery packs needed at each location so that the target lifetime can be satisfied. The factor that can be controlled to achieve the above goal is the routing path of each sample data to the sink. We formulate the problem as a constrained multiple deployment problem. We first derive the optimal solution using integer linear programming and a lower bound of the deployment cost. A heuristic is then developed that solves the problem in polynomial time. The heuristic consists of (1) a battery-aware routing algorithm that gives an initial solution by selecting routing paths based on the battery usage, and (2) a refinement procedure to improve the initial solution by adjusting traffic to reduce the number of battery packs required. We performed extensive simulations to evaluate the proposed algorithms in terms of the deployment cost and residual energy. The results show that our algorithm generates deployments close to the lower bound.

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