Improving Accuracy of FingerPrint DB with AP Connection States
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1 Improving Accuracy of FingerPrint DB with AP Connection States Ilkyu Ha, Zhehao Zhang and Chonggun Kim 1 Department of Computer Engineering, Yeungnam Umiversity Kyungsan Kyungbuk , Republic of Korea ilkyuha@ynu.ac.kr, cgkim@yu.ac.kr Abstract Studies for indoor positioning system based on the FingerPrint method are increasing its importance. Improving the accuracy of FingerPrint DB using Wi-Fi AP(Access Point) can give great effects on positioning. In this paper, we analyze active Wi-Fi signal phenomena from reference AP which is accessed by multi-users. Some factors which can affect the accuracy of position are studied by comparing signal patterns in the DB and real-time active signals. We propose a modified FingerPrint DB method on both AP connection and AP disconnection which can improve performance accuracy of the positioning. Keywords: Indoor positioning system, FingerPrint, Wi-Fi AP, real-time active signal, signals pattern comparison 1. Introduction Global positioning using satellites is the common outdoor positioning system. But the Global Positioning System (GPS) can't be used in indoor environments [1]. So another method for indoor positioning system is needed. WLAN shows good performance in terms of feasibility, infrastructure, implementation of positioning technology and IPR (Intellectual Property Right) [2, 3]. Wi-Fi is the most common infrastructure in WLAN. Because of Multi-Path of a signal source and interference among signals, there is an irregular signal attenuation phenomenon in indoor wireless environments. The signal sent from an AP (Access Point) is propagated through the multiple path with different distance, so that irregular RSSI (Received Signal Strength Indication) values may be measured at the same place [4, 5]. 2. Relatd Reserch The FingerPrint positioning method based on RSSI is used as a common method in indoor positioning system; it stores the previous signal pattern of each RP (Reference Point) in the DB (Data Base) to compare that pattern with new, real-time signals [6, 13] Signal Strength in FingerPrint Positioning The received signal strength pattern at each RP(Reference Point) from APs is stored in the FingerPrint DB. Figure 1 shows the concept of FingerPrint DB [2, 10]. Each AP is a Wi-Fi access point in WLAN and an RP is a reference position for calculation the position in a work area. 1 Corresponding author 143
2 Figure 1. Concept of FingerPrint DB In Figure 2, AP1, AP2, AP3 and AP4 are Wi-Fi access points for signal sources. The positioning area is divided into a grid and each square works as a reference point (RP). The mobile device collects the signal pattern measured in real-time from APs and sends it to the server, and the pattern is compared with the FingerPrint DB by using similarity function to assume the location of the mobile device [7]. The real-time pattern measured in Figure 2 is similar to the RP s pattern of B4 in Figure 3. So we decide the mobile is located at B4. Figure 2. Pattern Matching with FingerPrint DB Figure 3. An Environment of FingerPrint real-time Positioning The KNN (K-Nearest Neighbor) is a widely used similarity function which is one of the memory-based reasoning methods for pattern matching. The most similar K patterns will be extracted by the KNN algorithm, comparing the signal pattern measured in real-time with the FingerPrint DB [8]. In equation (1), indicates the similarity. means the signal strength received from the th AP at th RP. indicates the signal strength received from the th AP in real-time. is the distance type. It is called Manhattan distance when, and Euclidean distance when IEEE g Multi-rate transmission AP uses various modulations provided by a/b/g/n after checking among the clients RF (Radio Frequency) condition. For example, when a frame is transmitted between AP and user X by 54Mbps Modulation, and the sensitivity of restored frame is less than -65dBm, the receiver may fail to have correct signal, then sender does a few more tries. If the tries fail repeatedly, the sender chooses a lower rate, for example, 48Mbps modulation. (1) 144
3 3. Analysis Indoor Wi-Fi Signals The most important thing is to ensure the accuracy of FingerPrint DB according to the principles of 2.1. To know the characteristic of the momentary received signal strength in FingerPrint training phase and in real-time measuring for positioning is important. The RSSI values measured by the mobile device are also important. Because of the change of environments there may be some errors in momentary measuring. Thus, there may be a moment that the received signal strength from AP changes dramatically [11, 12]. 3.1 An experimental Environment A mobile device which has Wi-Fi Inspector v1.2.0 program provided by Xirrus is used for signal inspection [9]. Wi-Fi Inspector v1.2.0 collects and shows the detailed information including RSSI of AP s searched around. RP1 RP20 0m 10m 20m 1m 5m AP2(CIS_Lab) AP1(myLGNet) Figure Reference Points for Analysis and Experimental Environments In order to observe the received signal pattern, we consider three cases: with AP1 and AP2 disconnected, with AP1 connected and with only AP2 connected. We measured 100 times in 1 second intervals. Mobile device can receive signals from 8 additional APs scattered around the experimental area. 3.2 Signal Strength based on Disconnection State Figure 5 indicates the difference of received signal strength from each access point (AP) over time at each reference point (RP). (a) At RP1 (b) At RP19 Figure 5. Received Signal Strength from Aps based on Disconnected Aps At a time point, some signal strengths are changed suddenly by a communication disturbance. 3.3 Signal Strength based on Connection State The received signal strength from APs except AP1 shows stable phenomena in figure 6. But the signals strength from AP1 shows wave phenomena when the mobile device is connected with AP1 at RP1 as shown in Figure 6(a). The connection with an AP means a 145
4 mobile device associate with the AP using a Wi-Fi channel based on IEEE x to connect with networks. It has some differences according to the AP connection state. The strength of signals received from AP1 has small changes compared to the other signal strengths from neighboring APs though the environment is changed. We found a similar pattern with AP2 at RP5 with that of AP1 as shown in Figure 6(b). (a) At RP1(Connected With AP1) (b) At RP5(Connected With AP2) Figure 6. The Received Signals Strength from APs based on AP1 Connection The following elements may give some impact to the RSSI signals according to the phenomena found in Figure 6. (1) There are some changes in the receiving signals at some RPs according to the AP connection state. (2) The received signal strength on connection state to an RP is more stable than that on a disconnection state though the signal propagation environment is changed. The received signals strength based on AP connection is higher than that of AP disconnection as shown in Figure 7. Figure 7. Received Signal Strength on RPs based on AP Connection State Figure 8. The Average of Similarity Error Rate based on AP Connection State Figure 8 shows the average of similarity error rate based on AP connection state of the FingerPrint DB and the mobile device according to Figure 7. That is, it has error of 3.28% when it does not consider the AP connection states. So in this paper, we propose an AP connection states based method to construct the FingerPrint DB to reduce the similarity errors. 146
5 4. FingerPrint DB based on Connection States 4.1 Positioning Procedure based on Proposed FingerPrint DB To increase the accuracy of FingerPrint DB on indoor positioning, the signal strengths of connection based and disconnection based must be considered on preparing reference FingerPrint DB. All the added element in DB must be compared on real-time positioning. Find target APs Check signals On disconnection Connect with the APs Decision of positioning Prepare similarity with the proposed FingerPrint DB and real time signals Check signals on connection Figure 9. The procedure of positioning decision 4.2 Impact Factors to Improve the FingerPrint DB A FingerPrint DB training procedure, the following factors must be considered to increase the accuracy of DB. AP connection and disconnection states Channel overlapping among APs The number of mobile node in the WLAN BSS Interruption of other ISM bandwidth communications In this study, we found that strength of received signals based on access point (AP) connection is higher than that of AP disconnection through experiment. The connection and disconnection signal strength to indoor positioning must be applied on FingerPrint DB for improving positioning performance. 5. Conclusions The signals strength based on access point(ap) connection is higher than that of AP disconnection. We propose a connection-based and disconnection-based FingerPrint DB for improving accuracy. Other important factors of FingerPrint DB are also studied. Using both states of connection and disconnection, accuracy of positioning decision are improved compared to the disconnection only FingerPrint DB. As further research, we have to study various AP configuration models, effects of other ISM communications, calculation complexity and effects of channel overlapping among APs. Acknowledgement This research was supported by 2011 financial support of Yeungnam University. References [1] M. Kim, Implementation of Indoor Location Aware System using Wireless Signal Learning Algorithm, Korea Computer Congress, Vol. 34, No. 1(C), pp. 361~365, (2007). [2] W. Choi, Status and direction of development of the indoor positioning technology, Electricity of the world, Vol. 58, No. 11, pp. 36~42, (2009). 147
6 [3] J.-J. Kang, Implementation of the Dual Band Chip Antenna for WLAN, The Journal of IWIT, Vol. 9, No. 1, pp. 103~106, (2009). [4] C.-K. Park, Implementation of HSDPA-to-WiFi Access Point HSDPA CPE, The Journal of IWIT, Vol. 10 No. 5, pp. 167~172, (2010). [5] A. B. Jorgen, T. S. Rappaport and S. Yoshida, Propagation Measurements and Models for Wireless Communications Channels, IEEE Commun. Mag., vol. 33, no. 1, pp. 4249, (1995) January. [6] C.-S. Nam, K.-S. Jang and D.-R. Shin, Energy-Efficient Data Aggregation and Dissemination based on Events in Wireless Sensor Networks, The Journal of IWIT, Vol. 11, No. 1, pp. 35~40, (2011). [7] C. Choi, Review about RTLS Technology and Development Status, Special ReportⅡ, Vol. 57, No. 5, pp. 25~34, (2009). [8] A. Hatami and K. Pahlavan, Comparative Statistical Analysis of Indoor Positioning Using Empirical Data and Indoor Radio Channel Models, IEEE Communications Society subject matter experts for publication in the IEEE CCNC 2006 proceedings, pp. 1018~1022, (2006). [9] [10] Mikkel Baun Kjærgaard, Indoor Location Fingerprinting With Heterogeneous Clients, Pervasive and Mobile Computing, ELSEVIER, pp.31~43, (2011). [11] T. Vaupel, J. Seitz, F. Kiefer, S. Haimerl and J. Thielecke, Wi-Fi Positioning: System Considerations and Device Calibration, International Conference On Indoor Positioning And Indoor Navigation, (2010). [12] S. Shuo, S. Hao and S. Yang, Design of An Experimental Indoor Position System Based on RSSI, Information Science and Engineering (ICISE), pp. 1989~1992, (2010). [13] C. G. Kim and M. Wu, Leader election on tree-based centrality in ad hoc networks, telecommunication system, DOI /s Ilkyu Ha Authors Received the PhD degree in computer engineering from Yeungnam University, Korea. He is currently a Lecturer in computer engineering department at Yeungnam University. He was a software developer at HSS of Korea, a senior researcher at center for innovation of engineering education at Yeungnam University and a visiting professor at Yeungnam University. His research interests include Information Networks, Internet Applications and software engineering. Zhehao Zhang Received the master s degree in computer engineering from Yeungnam University. He is currently a researcher in computer network laboratory of computer engineering department at Yeungnam University. He is interested in Internet Applications and Internet Technology. Chonggun Kim Received the PhD degree in computer science from the University of Electro-Communications, Japan. He is a professor in Computer Engineering department at Yeungnam University. He was a research fellow at UCSC and Virginia Tech, USA. He is currently interested in Information Networks, Internet Applications and social networking services. He is a member of the IEEE and the IEEE Computer Society. 148
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