無線感測網路可用來追蹤單一會移動的目標物。每個無線感測器(sensor)只有有限的電量以及有限的偵測範圍。因此，為了節省電量，感測器會在適當的時候進入睡眠模式(sleep mode)。進入睡眠模式的感測器無法與其他感測器通訊。當目標物移動到處於睡眠模式的感測器之偵測範圍時，感測器也無法偵測到目標物，而產生追蹤錯誤(tracking errors)。因此，我們需要根據已知的目標物的相關資訊來決定感測器之睡眠時間。考慮能量消耗及追蹤錯誤間的折衷，我們提出根據目標物移動歷史記錄來決定感測器的睡眠時間的方案，使用電腦模擬來評估比較我們所提出的方案的合理性。

A wireless sensor network can be used to track an object. Every sensor has limited energy and detecting range. In order to conserve energy, sensors may be put into sleeping mode. A sensor in the sleeping mode can not communicate with other sensors or detect objects. When the object moves to the sensing range of a sleeping sensor, a tracking error occurs. To minimize the tracking error subject to an constraint on energy consumption, we should determine the sleeping schedules of sensors based on the mobility pattern of the object. We propose determining the sleeping schedules based on the observation history of the moving object. We use computer simulation to justify the usage of the proposed approach.

誌謝....................................................................... I
中文摘要.................................................................. II
Abstract ................................................................. III
目錄...................................................................... IV
圖目錄..................................................................... V
第一章 緒論 ............................................................... 1
1.1 簡介.............................................................................................................................. 1
1.2 研究動機........................................................................................................................ 2
第二章 研究背景及相關研究................................................. 3
2.1 Sparse Topology and Energy Management (STEM)............................................... 3
2.2 Distributed Target Classification ............................................................................... 4
2.3 Virtual Patrol .............................................................................................................. 6
2.4 Partially observable Markov decision process.......................................................... 7
第三章 根據目標物移動紀錄決定睡眠策略.................................... 12
3.1 系統架構 ..................................................................................................................... 12
3.2 利用 POMDP 追蹤目標物............................................................................................. 15
3.3 根據目標物移動記錄設定睡眠時間 ......................................................................... 19
第四章 模擬數據及結果.................................................... 28
4.1 模擬環境變數............................................................................................................ 28
第五章 結論.............................................................. 32
六、參考文獻.............................................................. 33

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