模糊C-均值聚類方法應用於無線基地台分群在谷歌地圖雲端服務領域;Fuzzy C-means Clustering Method is Applied to the Wireless Base Station Clustering in the Google Maps Cloud Service Area

NCU Institutional Repository > 資訊電機學院 > 通訊工程學系碩士在職專班 > 博碩士論文 > Item 987654321/53164

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题名:	模糊C-均值聚類方法應用於無線基地台分群在谷歌地圖雲端服務領域;Fuzzy C-means Clustering Method is Applied to the Wireless Base Station Clustering in the Google Maps Cloud Service Area
作者:	范綱倫;Kang-Lun Fan
贡献者:	通訊工程研究所碩士在職專班
关键词:	GPS;模糊分群;WiFi定位;谷歌地圖;GPS;Google Maps;WiFi positioning;fuzzy clustering
日期:	2012-06-01
上传时间:	2012-06-15 20:22:41 (UTC+8)
摘要:	谷歌地圖定位通常透過全球定位系統(GPS)得知當下的座標位置，但準確誤差率大概是3~5公尺，而GPS有訊號穿透不足的問題，當雲層比較厚時，或是定位的地點有較大的遮蔽物，定位效果就會較差且定位的時間也會比較長，若是進到室內或是隧道，GPS就無用武之地了，不少文獻中已經有探討使用WiFi來做定位的可行性，當使用者開啟行動裝置的WiFi時，行動裝置主動偵測周遭無線網路基地台，只要搜尋到三個以上的基地台訊號，並利用訊號的強弱(RSSI)及三點定位等方法，就可以推算出實際位置，其定位的準確度比GPS來的更佳，無線基地台在都會區中部署的相當密集，使得我們生活的周遭到處充斥著無線基地台的訊號，定位的速度也會比GPS來的快上許多。利用WiFi完成定位，其原理如同上述所言，由於這些無線基地台通常都會放置在固定位置，如果掌握這些AP的位置，並建立成資料庫，若是在區域內先進行無線基地台的分群，快速篩選出可用的無線基地台訊號，相信對於縮短雲端運算的時間會有實質的幫助，因此，本研究結合Google Maps API的實作，並提出應用模糊分群的方法，實際模擬區域內無線基地台的訊號，在區域內完成無線基地台的分群，來實現降低雲端運算時間之目的。Google maps latitude is usually learned through the Global Positioning System (GPS) coordinates of the current, but the exact error rate of about 3 to 5 meters, while the GPS signals penetrate the problem of insufficient thick clouds, or positioning place a larger shelter, the positioning effect will be poor positioning time will be longer, if into your living room or tunnel, the GPS is useless, a lot of literature has been exploring the use of WiFi to do positioning feasibility, when the user opens a WiFi mobile devices, mobile devices, proactive detection around the wireless network base station, as long as the search to three or more base station signals, and signal strength (RSSI) and three point positioning method , we can calculate the actual location of the positioning accuracy better than the GPS to the wireless base stations deployed in the metropolitan area is quite dense, making our lives around us is full of the signal of the wireless base station positioning speed GPS to fast on many. Using WiFi to complete positioning, the principle as mentioned above, these wireless base units are usually placed in a fixed position, grasp the location of the AP, and the establishment of a database, first the clustering of the wireless base station in the region, rapid screening available wireless base station signal, I believe there will be a real help for shortening the time of the cloud computing Therefore, this thesis combined with the implementation of the Google Maps API, and the application of fuzzy clustering method, the actual simulation area wireless base station signal in the region to complete the clustering of wireless base stations, to reduce the time of cloud computing.
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