Fuzzy C-means Clustering Method is Applied to the Wireless Base Station Clustering in the Google Maps Cloud Service Area
| Autor: | Kang-Lun Fan, 范綱倫 |
|---|---|
| Rok vydání: | 2012 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 100 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. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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