AIT  Vol.3 No.4 , October 2013
iPhone Independent Real Time Localization System Research and Its Healthcare Application
ABSTRACT
This project studied several popular localization algorithms on iPhone and, according to the demands, specifically designed it to improve healthcare IT system in hospitals. The challenge of this project was to realize the different localization systems on iPhone and to make balance between its response time and localization accuracy. We implemented three popular localization algorithms, namely nearest neighbor (NN), K-nearest neighbor (KNN), and probability phase, and we compared their performance on iPhone. Furthermore, we also implemented a real-time localization system using the ZigBee technology on iPhone. Thus, the whole system could realize not only self-localization but also others-localization. To fulfill the healthcare needs, we developed an application, which can be used to improve the hospital IT, system. The whole project included three phases. The first phase was to localize iPhone’s position using the received WiFi signal by iPhone, compare and optimize their performances. During the second phase, we implemented a ZigBee RFID localization system and combined it with the WiFi system. Finally, we combined new features of the system with a healthcare IT system. We believe that this application on iPhone can be a useful and advanced application in hospitals.

Cite this paper
X. Lu, W. Liu and Y. Guan, "iPhone Independent Real Time Localization System Research and Its Healthcare Application," Advances in Internet of Things, Vol. 3 No. 4, 2013, pp. 53-65. doi: 10.4236/ait.2013.34008.
References
[1]   L. M. Ni, Y. H. Liu, Y. C. Lau and A. P. Patil, “LAND- MARC: Indoor Location Sensing Using Active RFID,” Proceedings of the 1st IEEE International Conference on Pervasive Computing and Communications, March 2003.

[2]   J. Wyffels, J.-P. Goemaere, P. Verhoeve, P. Crombez, B. Nauwelaers, L. DeStrycker, K. A. H. O. Sint-Lieven, K. U. Leuven and N. V. Televic, “A Novel Indoor Localization System for Healthcare Environments,” International Symposium on Computer-Based Medical Systems (CBMS), 2012.

[3]   M. Pourhomayoun, Z. P. Jin and M. Fowler, “Spatial Sparsity Based Indoor Localization in Wireless Sensor Network for Assistive Healthcare,” IEEE International Conference on Engineering in Medicine and Biology Society (EMBC), 2012.

[4]   M. Bharanidharan, X. J. Li, Y. Y. Jin, J. S. Pathmasuntharam and G. X. Xiao, “Design and Implementation of a Real Time Locating System Utilizing WiFi Signals from iPhones,” IEEE International Conference on Networks (ICON), 2012.

[5]   M. Ali, “iPhone SDK Programming: Developing Mobile Applications for Apple iPhone and iPod Touch,” Wiley Press, 2009.

[6]   Q. X. Chen; D.-L. Lee and W.-C. Lee, “Rule-Based WiFi Localization Methods,” IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, 2008.

[7]   T. Bagosi and Z. Baruch, “Indoor Localization by WiFi,” IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), 2011.

[8]   Effelsberg, “COMPASS: A Probabilistic Indoor Positioning System Based on 802.11 and Digital Compasses,” WiNTECH ‘06 Proceedings of the 1st International Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization, 2006, pp. 34-40.

[9]   W.-H. Chen, H. H. Chang; T. H. Lin, P. C. Chen, L. K. Chen, S. J. Hwang, D. H. J. Yen, H. S. Yuan and W. C. Chu, “Dynamic Indoor Localization Based on Active RFID for Healthcare Applications: A Shape Constraint Approach,” International Conference on Biomedical Engineering and Informatics (BMEI), 2009.

[10]   L. Y. Hao, G. Y. Liang and L. Wei, “Indoor Positioning system ( Middleware),” Nanyang Technological University, Nanyang, 2012.

 
 
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