ERU SYMPOSIUM, 2011: FACULTY OF ENGINEERING, UNIVERSITY OF MORATUWA, SRI LANKA
Wi-Fi based Positioning System L G A I Liyanagedara, B R Madarasinghe, M A T Tillekeratne, K N I Ubhayaratne and D Dias
Abstract Real Time Location Estimation systems (RTLS) developed for outdoor environments have shown significant errors due to changes in atmospheric conditions, multipath propagation and inherent signal strength variations. In this paper, we present an improved Wi-Fi based RTLS solution for the outdoors that reduces the above impacts. In addition this paper presents the development of a portable device for implementing the above RTLS solution. The research makes use of Euclidean, standard deviation based and probabilistic algorithms. Data smoothening is done to reduce the multipath propagation issues. The different algorithm combinations are compared via their error cumulative distribution functions. which mainly follow the fingerprinting approach and use both deterministic, probabilistic algorithms for both raw and smoothened data. The performance of these algorithms for the outdoors is assessed and an optimal algorithm is developed which is an integration of several algorithms. In addition this paper includes the development of a portable device (a Wi-Fi tag) for measuring the RSSI signals and sending them to a system which applies the positioning algorithms to get location of the device.
1. Introduction1 Wi-Fi has gained popularity throughout the world as an easy and inexpensive mode of internet access. The added advantage of user mobility is the primary cause of this popularity. On the other hand, considerable amount of effort has gone into the development of Real Time Location Estimation Systems (RTLS) in recent times. These systems which were previously based on Global Positioning System (GPS) is now being shifted to wireless local area network (WLAN) based systems due to the inaccuracy of GPS in urban areas. Even though Wi-Fi was not designed for location estimation, it is possible to use Wi-Fi Received Signal Strength Characteristics Indicator (RSSI) and Time Difference of Arrival (TDOA) of IP packets transmitted in a Wi-Fi network to determine position to a reasonable accuracy.
2. Methodology The research uses Wi-Fi access points and Wi-Fi tags and uses received signal strength as the wireless parameter for position estimation. A radio map is built with Wi-Fi RSSI data at different locations with equal spacing and fingerprinting algorithms are developed.
The WLAN based RTLS were primarily built for asset tracking applications in indoor environments where Wi-Fi networks already exist. The use of Wi-Fi for the indoors is done by [1]. Locations estimation could be done though wireless environment modeling [2] as well as through fingerprinting [3]. A triangulation algorithm is used by [2] when following the environment modeling approach. In terms of fingerprinting algorithms there are two types of algorithms used in position estimation, namely, deterministic and probabilistic. Deterministic algorithms include the minimum Euclidean distance based and standard deviation based methods which are described in [4]. Probabilistic algorithms use Baye’s rule in determining the location [2]. Multipath propagation causes adverse variations in signal strength measurements. An algorithm to reduce this effect is discussed in [5].
2.1 Data collection and pre-processing We develop two radio maps, a smaller map with 14m x 6m area and 1m grid spacing and a larger one with 30m x 10m with 2m grid spacing. At each grid point we take 50 samples of Wi-Fi RSSI data per Access Point with 1 second sampling time. The log files are saved as text documents by default by the `WirelessMon` software. Measurements were also taken at test points within the grids to apply algorithms and determine the accuracy. Prior to applying the data to the algorithms we calculate the averages and the standard deviations of the data at each grid point. 2.2 Positioning Algorithm Development As our research we develop different algorithms that make use of the different characteristics of RSSI for position estimation．This consists of Minimum Euclidean Distance Algorithm [4], Standard Deviation based algorithm [4] and probabilistic method [2]. Additionally we intend to identify the impact of the grid spacing size to the accuracy of the results.
As seen in the related work cited above, a considerable amount of research has been done on the use of Wi-Fi for indoor position estimation. In this paper, we present a Wi-Fi based RTLS solution for the outdoors
To increase accuracy, a filtering algorithm is used that filters out previously possible locations based on access point signal availability at each grid point.
1
L G A I Liyanagedara, B R Madarasinghe, M A T Tillekeratne and K N I Ubhayaratne are undergraduates of the Department of Electronic and Telecommunication Eng., Univ. Moratuwa and D. Dias is a professor of Department of Electronic and Telecommunication Eng., Univ. Moratuwa. (e-mail:
[email protected];
[email protected];
[email protected];
[email protected]; dileeka@ ent.mrt.ac.lk)
Since multipath propagation introduces variations to the RSSI values，Smoothening algorithms are developed to reduce this effect．The accuracy of the smoothening
1
ERU SYMPOSIUM, 2011: FACULTY OF ENGINEERING, UNIVERSITY OF MORATUWA, SRI LANKA algorithm is compared with the different window sizes and the optimal window size is found. To address the issue of temporal variations in RSSI data a calibrating algorithm is to be introduced.
2.3 Product Development We develop a portable device which scans the existing access points and measures the Wi-Fi RSSI from each access point at each grid point. It has the feature of transmitting the measured signal strength values to a central server via either TCP or the UDP protocols. We also develop a web based application for users to interact with the system to get required location information.
Figure 2: Effect of accept region on SD based algorithm accuracy
3. Results and Discussion The research was done in two phases, training phase and the testing phase. During the training phase the signal strengths at the grid points are measured and the radio maps are developed. The training phase was constructed using datasets taken on several days to improve robustness of the aggregated radio map. In the testing phase we collect the RSSI data at test points and apply the above mentioned algorithms and the radio maps to find the coordinates of the test points.
Further for the smoothening algorithm we drew CDFs with different window sizes and the obtained results are included below. Error CDF of Euclidean Method for smooth data 100 90 80
percentage of error
70
The error Cumulative Distribution Functions (CDF) are drawn for comparing the performance of different algorithms. The obtained results are as follows.
60 50 40 Window size = Window size = Window size = Window size = Window size = Window size =
30
Figure 1 shows the improvement of the results when multiple training data sets are used to construct the aggregated radio map as opposed to using few data sets. Figure 2 depicts the results of the SD based algorithm for several accept region sizes.
20 10 0
0
5
10
15 error(m)
20
3 5 7 8 9 10
25
30
Figure 3a: Effect of window size on smoothening for window sizes of 3 to 10
The results of smoothened data are shown in Figure 3a, 3b and 3c for different window sizes.
Error CDF of Euclidean Method for smooth data 100
Error CDF of Euclidean method
90
100 80 90 70
percentage of error
% of Probability of error
80 70 60 50
60 50 40 30
Window size = Window size = Window size = Window size = Window size =
40 20 30 10 20 Previous test with average grid Previous test with latter day grid previous test with previous grid
10 0
0
5
10
15 Error(m)
20
25
0
0
5
10
15 error(m)
20
25
11 12 13 14 15 30
30
Figure 3b: Effect of window size on smoothening for window sizes of 11 to 15
Figure 1: Error comparison for test data using Euclidean algorithm which shows the effect of the stronger training phase.
2
ERU SYMPOSIUM, 2011: FACULTY OF ENGINEERING, UNIVERSITY OF MORATUWA, SRI LANKA 3.1 Conclusion The results show that the temporal variation of the Wi-Fi data can be reduced by using multiple training data sets taken in different environmental conditions.
Error CDF of Euclidean Method for smooth data 100 90
percentage of error
80
Additionally, the two ratios calculated for smoothened data gave two different optimal window sizes. However, by comparing those results with figure 3a, it is evident that the optimal window size given by figure 5 gives more accurate results. Therefore, the optimal window size was taken as 5.
70 60 50
Window size = Window size = Window size = Window size = Window size = Window size = Window size =
40 30 20
0
5
10
15 error(m)
20
25
20 25 30 35 40 45 50
The results obtained by using smoothened data are not as good as for indoor location estimation. This may be due to the difference of physical nature of indoor and outdoor environments. Therefore, the effect of multipath propagation maybe different in the two environments.
30
Figure 3c: Effect of window size on smoothening for window sizes of 20 to 50 Ratios developed to identify optimal window size for data smoothening are shown in Figures 4 and 5. Ratio used in figure 4, calculates the spatial to temporal variation for each window size. maximum ratio is obtained at a window size of 30 as shown in the figure. Figure 5 calculates the ratio between number of zero error calculations and maximum error for each window size.
References [1] Y. Wang, X. Jia, H.K. Lee, An indoors wireless positioning system based on wireless local area network infrastructure. SatNav 2003,The 6th International Symposium on Satellite Navigation Technology Including Mobile Positioning & Location Services Melbourne, Australia 22–25 July 2003） [2] Nilushika Shironi Kodippili, Prof. Dileeka Dias, Integration of Fingerprinting and Trilateration Algorithms for Improved Indoor Localization Performance. Dept. of Electronic and Telecommunication Engineering, University of Moratuwa, Sri Lanka [3] B.D.S.Lakmali, Dileeka. Dias, Database Correlation for GSM Location in Outdoor & Indoor Environments. Dialog-University of Moratuwa Mobile Communications Research Laboratory, Department of Electronic & Telecommunication Engineering, University of Moratuwa, Moratuwa, 10400, Sri Lanka
Figure 4: Ratio between Spatial and Temporal Variation for different window sizes
[4] B.D.S.Lakmali, W.H.M.P.Wijesinghe, K.U.M.De Silva, K.G.Liyanagama, S.A.D.Dias, Design, Implementation & Testing of Positioning Techniques in Mobile Networks. Department of Electronic & Telecommunication Engineering, University of Moratuwa, Sri Lanka [5] Shih-Hau Fang, Member, IEEE, Tsung-Nan Lin, Senior Member, IEEE, and Kun-Chou Lee, Senior Member, IEEE, A Novel Algorithm for Multipath Fingerprinting in Indoor WLAN Environment. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 7, NO. 9, SEPTEMBER 2008
Figure 5: Accuracy Ratio for different window sizes
3
