Journal of Computer Science 6 (10): 1206-1212, 2010 ISSN 1549-3636 © 2010 Science Publications Corresponding Author: Hamid Mehmood, Department of Remote Sensing and Geographic Information Systems, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand 1206 Indoor Positioning System Using Artificial Neural Network Hamid Mehmood, Nitin K. Tripathi and Taravudh Tipdecho Department of Remote Sensing and Geographic Information Systems, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand Abstract: Problem statement: Location knowledge in indoor environment using Indoor Positioning Systems (IPS) has become very useful and popular in recent years. A number of Location Based Services (LBS) have been developed, which are based on IPS, these LBS include asset tracking, inventory management and security based applications. Many next-generation LBS applications such as social networking, local search, advertising and geo-tagging are expected to be used in urban and indoor environments where GNSS either underperforms in terms of fix times or accuracy, or fails altogether. To develop an IPS based on Wi-Fi Received Signal Strength (RSS) using Artificial Neural Networks (ANN), which should use already available Wi-Fi infrastructure in a heterogeneous environment. Approach: This study discussed the use of ANN for IPS using RSS in an indoor wireless facility which has varying human activity, material of walls and type of Wireless Access Points (WAP), hence simulating a heterogeneous environment. The proposed system used backpropogation method with 4 input neurons, 2 output neurons and 4 hidden layers. The model was trained with three different types of training data. The accuracy assessment for each training data was performed by computing the distance error and average distance error. Results: The results of the experiments showed that using ANN with the proposed method of collecting training data, maximum accuracy of 0.7 m can be achieved, with 30% of the distance error less than 1 m and 60% of the distance error within the range of 1-2 m. Whereas maximum accuracy of 1.01 can be achieved with the commonly used method of collecting training data. The proposed model also showed 67% more accuracy as compared to a probabilistic model. Conclusion: The results indicated that ANN based IPS can provide accuracy and precision which is quite adequate for the development of indoor LBS while using the already available Wi-Fi infrastructure, also the proposed method for collecting the training data can help in addressing the noise and interference, which are one of the major factors affecting the accuracy of IPS. Key words: Artificial neural networks, indoor positioning systems, Wi-Fi, backpropogation method INTRODUCTION Wi-Fi Received Signal Strength (RSS) has drawn great attention in recent years for the development of Indoor Positioning Systems (IPS), because of its distinct advantages like already existing infrastructure and wide coverage area. With numerous new Wi-Fi nodes being added daily, the global Wi-Fi coverage is continuously growing. Recent tests have shown that indoor positioning with Wi-Fi RSS based Positioning Systems (WPS) can achieve positioning accuracy of 1 to 4 meter indoors and 10-40 m in the outdoor environment (Prasithsangaree et al., 2002; Doherty et al., 2001; Hightower and Borriello, 2001). A typical WPS consists of a Mobile User (MU), wireless Access Point (AP) and a positioning algorithm. There are three basic methods to compute the location of a MU in indoor envrionment; (a) Angle of Arrival (AoA), (b) Time of Arrival (ToA)/Time Difference of Arrival (TDoA) (Yamasaki et al., 2005) and (c) Received Signal Strength (RSS) (Kolodziej and Hjelm, 2006). AoA is based on the time of flight of electromagnetical waves, which requires very accurate timming measurements and thus implementation of AoA requires additional hardware. ToA and TDoA are based on the measurement of the time flight of the signal, the implementation of this technique requires