| Summary: | 碩士 === 淡江大學 === 電機工程學系 === 88 === We establish a site survey tool system to predict the Received Signal Strength Index (RSSI) in indoor environment. The system includes six items. 1. The fading function : It corrects the functional characteristics of the RSSI for different kinds of Wireless LAN card in free space. 2. Setting the attributes of obstructers in indoor environment : The idea of “single attribute of local area” is proposed in this paper. If there are same obstructers in one area, we set the area as one attribute. 3. Genetic Algorithm : We use Reproduction, Crossover and Mutation to obtain the propagation loss through the different obstructers (Li). 4. Neural Network : When we transmit signal, we find that the Received Signal Strength will often increase due to multipath effect. We use Neural Network Concept to correct the prediction error arisen from the multipath effect in indoor environment. 5. The auxiliary judgment for the sampling points : When Genetic Algorithm is used to obtain the propagation loss through different obstructers, we will not know where the best sampling points are. We thus design a program to show up the sampling points automatically. The method is helpful to users in establishing the best sampling points. 6. The calibration of prediction results : We use calibration to correct the prediction error arisen from Li .
Besides measuring RSSI, we also use 11M Wireless LAN cards to perform experiments on transmitting packets. We place receivers at different locations to measure Packet Count Per Second (PCPS). We also open Microwave Oven to analyze the PCPS. Besides PCPS, we also analyze the special phenomenon of strange data and try to explain the phenomenon.
The advantages of the site survey tool are as follows. By classifying different obstructers by color, the users can obtain a clear view on the location distribution of obstructers in the indoor environment. The auxiliary judgment for the sampling points is helpful to users in establishing the best sampling points. The application of Genetic Algorithm and method of Neural Network result in more precise prediction values and an increased calculation speed. The calibration of Wireless LAN cards and prediction errors also render more accurate prediction values.
Finally we use the prediction results to predict the optimal locations of access points (AP) in indoor environment and illustrate the method of site survey tool application.
|
|---|
