Issue No. 01 - Jan. (2013 vol. 12)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TMC.2011.243
A. K. M. Mahtab Hossain , Internet Educ. & Res. Lab. (intERLab), Asian Inst. of Technol. (AIT), Khlong Luang, Thailand
Yunye Jin , Inst. for Infocomm Res., A*STAR (Agency for Sci., Technol. & Res.), Singapore, Singapore
Wee-Seng Soh , Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore
Hien Nguyen Van , Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA
Fingerprint-based methods are widely adopted for indoor localization purpose because of their cost-effectiveness compared to other infrastructure-based positioning systems. However, the popular location fingerprint, Received Signal Strength (RSS), is observed to differ significantly across different devices' hardware even under the same wireless conditions. We derive analytically a robust location fingerprint definition, the Signal Strength Difference (SSD), and verify its performance experimentally using a number of different mobile devices with heterogeneous hardware. Our experiments have also considered both Wi-Fi and Bluetooth devices, as well as both Access-Point(AP)-based localization and Mobile-Node (MN)-assisted localization. We present the results of two well-known localization algorithms (K Nearest Neighbor and Bayesian Inference) when our proposed fingerprint is used, and demonstrate its robustness when the testing device differs from the training device. We also compare these SSD-based localization algorithms' performance against that of two other approaches in the literature that are designed to mitigate the effects of mobile node hardware variations, and show that SSD-based algorithms have better accuracy.
Robustness, Bluetooth, IEEE 802.11 Standards, Mobile handsets, Wireless communication, heterogeneous devices, Location fingerprint, signal strength difference (SSD), Wi-Fi, Bluetooth, indoor localization, positioning system
Yunye Jin, Wee-Seng Soh, Hien Nguyen Van and A. K. Mahtab Hossain, "SSD: A Robust RF Location Fingerprint Addressing Mobile Devices' Heterogeneity," in IEEE Transactions on Mobile Computing, vol. 12, no. , pp. 65-77, 2013.