目前大多數的環境感測網路蒐集資料的方式分為兩個種類，第一是靜態收集，也就是將感測點放置到固定位置，並藉由無線或有線網路將感測資料回傳到資料庫儲存。另一種為了可以在網路較不發達的地區也能夠蒐集資料，因而使用車載系統，行駛到該區域，將環境資料蒐集回來。但是這兩種蒐集資料的方式都屬於在同一平面上感測資料，無法量測到三維空間的數據。以觀測空氣汙染物為例，近年來越來越多國家積極地減少空氣汙染所帶來的外部成本，為了達成此目的，必須防止汙染物的產生，因此需要追蹤汙染物的來源，從源頭減少有毒物質排放，才能真正改善空氣品質。但是空氣汙染物的擴散模型需要三維空間的汙染物資料，而上述兩種方式便存在其限制。為了達成此目的，本論文使用無人機做為飛行載具，蒐集三維空間的環境資料。
本論文結合四旋翼無人機與環境感測平台，並利用無人機能在三維空間飛行的優勢，蒐集不同高度的環境資料，包括溫度、濕度以及PM2.5濃度，藉由無線傳輸模組傳回資料庫，並以視覺化網頁呈現資料，最後再針對不同高度之間的數據進行相關性分析。
為實現上述方法，本論文使用微控制器控制溫濕度感測器、PM2.5濃度感測器以及無線傳輸模組作為環境感測平台，搭載到實驗室自製的四旋翼無人機上，蒐集每小時、不同高度的環境數據。將資料儲存到資料庫中，並以網頁的方式將資料圖表化。最後計算不同高度(0公尺、9公尺、18公尺、27公尺)的溫度、濕度及PM2.5濃度之間的相關性，發現溫度及濕度都與高度有高相關性，而PM2.5濃度在9公尺以下與高度僅具有中度相關，在9公尺以上到27公尺之間是與高度具有高相關性。

At present, there are two categories to collect ambient information. One is the static collection, which the sensors are placed in a fixed position and send sensing data to database by wired or wireless network. In order to collect environmental data in the areas of less developed network, the other way use the vehicle system to travel to the region and collect the information. However, these two methods always gather the sensing data of same plane and cannot measure information in three-dimension. Take observing air pollutants as an example, more and more countries have been actively reducing the external costs of air pollution in recent years. To achieve this purpose, it is necessary to prevent the generation of pollutants. Therefore, it must track the source of pollutants and toxic substances in order to improve the air quality certainly. However, the diffusion model of air pollution requires three-dimensional pollutant information and the above two ways will be restricted. In order to achieve this goal, this thesis purposes the UAV (Unmanned Air Vehicle) as a flying vehicle to collect the ambient data of three-dimensional space.
This thesis integrates microcontroller, temperature and humidity sensor, PM2.5 sensor and wireless transmission module as the environmental sensing platform. Install it into laboratory-made quadcopter and collect environmental data of different height hourly. Store the information in the database and visualize the data in the web page. Finally, calculate the correlation between temperatures, humidity, PM2.5 concentration and heights. It is found that temperature and humidity show highly correlated with the height. While PM2.5 concentration below 9 meters moderately correlates with the height and other cases have highly correlation.

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