在無線感測網路應用中 (如軍隊監控和野生動物習性監控)，追蹤技術扮演一個相當重要的角色。目前大多的追蹤方法都是基於一種移動模組在預測物體未來的位置，並喚醒預測位置附近的感測器定期監控物體。但是，這樣的追蹤系統並無法有效的長時間追蹤物體。因為在現實生活的應用中，一個物體通常都不會長時間使用同一種移動方式在移動。因此要正確地表示物體的移動軌跡，往往都需要多種的移動模組，而非使用單一種移動模組就能夠完整表示物體的移動軌跡。目前只使用單一種移動模組的追蹤方法，將會很容易發生偵測不到物體的狀況，因而需要消耗更多的能源找回遺失的物體。在這一篇論文裡，我們提供了一個追蹤平台，稱之為 Multi-Model based Object Tracking Architecture (MMOTA)，用省能源的方式長時間追蹤物體。MMOTA 的基本想法就是在追蹤過程中，針對不同的狀況，動態從多個預先設計好的追蹤方法中選出最省能源的追蹤方式，並利用選擇的追蹤方式追蹤物體。接下來，我們開發一個 Monitoring-Cost Evaluator，衡量未被使用的追蹤方法的追蹤成本。並設計三個追蹤方法的選擇策略，包含有Greedy Strategy、 Min-Max Strategy 和 Weighted Moving Average Strategy，選出最有效率的追蹤方法給 MMOTA 追蹤物體。最後，我們模擬一組多樣性的實驗，比較 MMOTA 和目前的追蹤系統的追蹤效能，以及衡量三個所提出的選擇追蹤模組策略。實驗結果顯示，和目前的追蹤系統比較起來，MMOTA 可以消耗相當少能源追蹤物體，甚至可以節省超過 50.7% 的能源。

The tracking techniques play a significant role in wireless sensor network applications, such as troops monitoring and wildlife habitat monitoring. The methodology of recently proposed tracking schemes predicts the object location in the future based on a moving model, and then activate nearby sensors to monitor the target periodically. However, existing tracking systems cannot effectively track targets for a long time. This is because in most real-world applications, a target frequently or occasionally moves with different patterns, and accurately predicting the movement of a target needs multiple moving models, instead of a single model. Thus, the existing schemes frequently incur target loss, and a great amount of energy is consumed to find back the target. In this thesis, we propose a tracking framework, called Multi-Model based Objet Tracking Architecture (MMOTA), to energy-efficiently track a moving target. MMOTA can dynamically select the best tracking modules to monitor the target among the multiple predesigned tracking modules in different situations. Next, we derive a Monitoring-Cost Evaluator to evaluate the monitoring cost for the inactive tracking modules, and then design three tracking module selection strategies, including Greedy Strategy, Min-Max Strategy, and Weighted Moving Average Strategy, to select the most effectively tracking module for monitoring the target. Finally, we conduct a set of comprehensive experiments to compare MMOTA against the existing tracking systems and evaluate the three proposed tracking module selection strategies. The result shows that MMOTA consumes the much less energy for monitoring the target than the existing tracking systems, and saves more than 50.7% amount of energy for monitoring the target.

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