Positioning in WLAN environment by use of artificial neural networks and space partitioning

Short range wireless technologies such as wireless local area network (WLAN), Bluetooth, radio frequency identification, ultrasound and Infrared Data Association can be used to supply position information in indoor environments where their infrastructure is deployed. Due to the ubiquitous presence of WLAN networks, positioning techniques in these environments are the scope of intense research. In this paper, the position determination by the use of artificial neural networks (ANNs) is explored. The single ANN multi-layer feedforward structure and a novel positioning technique based on cascade-connected ANNs and space partitioning are presented. The proposed techniques are thoroughly investigated on a real WLAN network. Also, an in-depth comparison with other well-known techniques is shown. Positioning with a single ANN has shown good results. Moreover, when utilising space partitioning with the cascade-connected ANNs, the median error is further reduced for as much as 28%.