為了監控道路上的交通狀況，感測器普遍應用於道路網路之中。當觀測的網路範圍非常大時，為了節省成本，感測器因而分散地佈署。過去關於定位有相當多的研究是基於範圍的量測以及交換連結的訊息，然而這些研究在感測器分散的環境之中卻變得沒效率，已不再適用。

為了解決這個問題，本篇論文提出了根據道路上的隨機性質而發展出的定位演算法。我們使用僅有二位元偵測能力的感測器，並且分析所偵測的車輛資訊來分類感測器。經由交通訊息的分析，我們一開始先將感測器分成兩大類(位於非十字路口及位於十字路口)。我們首先把非十字路口的感測器依照各自所屬的道路區段一一重組回來。接著我們再將十字路口的感測器與非十字路口的感測器群合併。透過已知的道路地圖，我們可以了解每個感測器在道路上的實際位置。

In order to monitor traffic in roads, sensors are common applied to road networks. When we want to observe a large range network, sensors are deployed sparsely for the sake of saving cost. Many localization approaches which based on the range measurements or connectivity information become ineffective, and are not suitable in such environment.

To solve the question, this thesis presents a localization algorithm according to the random property in the road network. We use the sensors only with binary detection and analyze the vehicle-detection information to classify sensors. After traffic analysis, we classify sensors into two groups (non-intersection nodes and intersection nodes). We combine the non-intersection nodes in each road segment first, and then we connect the intersection nodes with the non-intersection node clusters. With a known road map, we can realize each sensor’s position in the real roadway.

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