Location determination of mobile device for indoor WLAN application using neural network

Due to increases in the use of wireless local networks (WLANs) and mobile computing devices, and the popularity of location-based services, determining the location of a device at any time is important. Although numerous GPS-based applications have been developed and successfully utilized in various fields, they have serious limitations. Specify applicable to outdoor applications. Therefore, to develop and approach that determines the location of what that is suitable for indoor environments is necessary. This study presents a novel location determination mechanism that uses an indoor WLAN and back-propagation neural network (BPN). A museum is taken as an example indoor environment. Location determination is achieved using the combined strengths of 802.11b wireless access signals. With a significant numerous access points (APs) installed in the museum, hand-held devices can sense the strengths of the signals from all access points to which the devices can connect. Using a back-propagation network, device locations can be estimated with sufficient accuracy. A novel adaptive algorithm is implemented.