在傳統網路如Internet或是無線網路(Wireless Network)，電腦可以使用IP address或是 MAC address當作識別ID，因為這兩種address都具有唯一的特性。然而，在感知網路的架構之下，感知器製造趨向"體積小"、"價格便宜"，使得感知器網路不會有類似IP以及 MAC address的唯一ID。此外，透過感知器所收集的資料通常都必須搭配位置資訊才會具有意義，例如：收集某個區域內的溫度、濕度等資料，如果不知道資料來自於那個位置，則這些資料就沒有用了，因此為感知器分配ID以及估計位置這兩個問題是高度相關的，因為感知器的位置資訊就具有unique的特性。
為了解決這個問題，有些研究學者提出在”少量”的感知器上裝設GPS，其餘未裝設GPS的感知器可透過ToA, RSS等距離量測機制，搭配位置估計演算法達到估計感知器位置的目的，由於不準確的距離估算，使得位置計算變得複雜且精確度不佳。因此我們提出以格子為基礎的感知器網路位置估計演算法，此演算法不致力於估算每個感測器的精確位置，只估計靠近格子點的位置座標，有了這些格子點的座標之後，其他不在格子點附近的感知器只需估算和格子上感測器間的距離，再利用查表的方式判斷出感測器大略的位置，並依其位置，定義ID。如此一來，不但可大幅減少感知器定位時的計算量，在應用上，只要透過感測器的ID，便可推知感測器的大略位置。

In sensor networks, sensors usually do not equipped with global IDs similar to IP addresses or MAC addresses in IP-based networks to be its identifier due to the application-driven usage and the cost of massive production. However, knowledge of physical locations of sensor nodes is essential for many geographic aware applications, such as target tracking, coverage management, and directional flooding, etc. Accordingly, many researchers have proposed several localization algorithms. They often assume some sensors are equipped with GPS devices, and the other sensor nodes can estimate their location through certain of distance computations.
Considering the tradeoff between computation complexity and precision of localization algorithms, in this study, we propose a grid-based approach to fulfill the requirements. In fact, instead of making effort to calculate all sensors’ locations directly, we would rather establish a grid system of partial nodes which are approximately deployed in uniform. Therefore, other sensor nodes can reckon which grid they belong to by some judgment with the distance table maintain by each grid node. Finally, we assign each sensor node a unique ID according to their location information.
The average estimated error of grid sensor node is decreased when there are more nodes involves or with higher grid dimension. The average estimated error is under 0.45 units if the sensors are deployed in a 100□100 unit space. Moreover, the localization and ID assignment of non-grid nodes is efficient once a new sensor node is added in the network. About 80% of non-grid nodes have correct predicted result.

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