Location determination of mobile devices for an indoor WLAN application using a neural network

• Published in: Knowledge and information systems Vol. 20; no. 1; pp. 81 - 93
• Main Authors: Tsai, Chih-Yung, Chou, Shuo-Yan, Lin, Shih-Wei, Wang, Wei-Hao
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.07.2009; Springer; Springer Nature B.V
• Subjects: Accuracy; Applied sciences; Back propagation; Computer Science; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Data Mining and Knowledge Discovery; Database Management; Energy consumption; Exact sciences and technology; Feature selection; Global positioning systems; GPS; Information Storage and Retrieval; Information Systems and Communication Service; Information Systems Applications (incl.Internet); Information systems. Data bases; IT in Business; Local area networks; Location based services; Memory organisation. Data processing; Museums; Neural networks; Propagation; Regular Paper; Software; Studies; Wireless networks; Wireless LAN; Location determination; Mobile devices; Back-propagation network; GPS system; Adaptive algorithm; Outdoor installation; Radiolocalisation; Satellite; Neural network; Context aware; Distributed system; Obstruction; Interest; Backpropagation algorithm; Indoor installation; Object oriented; Museum; Localization; Mobile computing
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-008-0154-2

Due to the popularity of location-based services, determining the location of a device at all times has become a subject of great interests. Although many GPS-based applications have been developed and successfully deployed in various fields, their applicabilities are hindered by the obstruction of the objects in the environment. Essentially, as satellite signals cannot penetrate the walls of buildings, the coverage of GPS systems is limited to outdoor environments. To fully exploit the benefit of location-based services, approaches that determine the location of a device in indoor environments need to be established. This study presents a novel location determination mechanism that uses an indoor WLAN and back-propagation neural network (BPN). A museum is taken as the context of the example indoor environment. Location determination is achieved using the combined strengths of 802.11b wireless access signals. With a significant number of access points (APs) installed in the museum, hand-held devices can sense the strengths of the signals from all APs 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 for enhancing the accuracy of the estimation.