Π8: Indoor Positioning System using WLAN Received Signal Strength Measurements Preface

Transcription

1 Π8: Indoor Positioning System using WLAN Received Signal Strength Measurements Preface In this deliverable we provide the details of building an indoor positioning system using WLAN Received Signal Strength (RSS) fingerprints. The positioning system has been deployed at the premises of KIOS Research Center and follows a terminal-based-network-assisted architecture. In our case, users that carry a terminal (Tablet PC) are able to self-locate and positioning is performed entirely on the device using the observed RSS fingerprint. Currently, one positioning method, i.e. K-Nearest Neighbors (KNN) and one filtering method, i.e. Kalman filter, has been integrated into our positioning platform. In the future we plan to implement and integrate more positioning and filtering modules, while at the same time try to improve the performance with respect to the positioning error by tweaking and fine-tuning both modules. The rest of this report is structured as follows. Section 1 provides the details of our experimentation area in the premises of KIOS Research Center, University of Cyprus. In Section 2 we describe the hardware components that we used, including the WLAN Access Points (AP) and Tablet PCs, while the software, including free or open source programs and custom software written in MATLAB, is detailed in Section 3. The experimental setup, e.g. installation, verification and testing of the APs is presented in Section 4, while the data collection process to build the so called RSS radio map that contains the training RSS fingerprints is described in Section 5. Finally, performing positioning inside KIOS with our MATLAB software and Graphical User Interface (GUI) is presented in Section 6.

2 1. Experimentation Area Our indoor positioning system is deployed at the premises of the KIOS Research Center for Intelligent Systems & Networks, University of Cyprus. The KIOS Research Center occupies the second floor of a two storey building and the floorplan is depicted in Figure 1. This is a typical office environment and the total area is x m 2. The floor has an open plan interior design and consists of the foyer (221), several workstations at the cubicles ( ), open (208, 220) and private offices ( ), one lab (207), a kitchen room (219), a conference room (209) and several corridors (2C1-7). Figure 1: Floorplan of the experimentation area at KIOS Research Center.

3 2. Hardware Components The hardware components required to build the indoor positioning system include the WLAN infrastructure, i.e. WLAN Access Points (AP), and the mobile devices that are used to perform positioning by using a terminalbased-network-assisted architecture. We have used 7 WLAN APs that follow the IEEE b/g/n standard and 2 tablet PCs equipped with WLAN adapters; see Figure 2. Figure 2: The hardware components of the indoor positioning system. WLAN APs The specifications of the APs, including the brand/model, AP name (ESSID), MAC Address and transmission channel are tabulated in the following table. The two different models are shown in Figure 3 and Figure 4, respectively. Model ESSID MAC Address Channel Intellinet 300N KIOS_1 00:1F:1F:89:85:A0 3 Intellinet 300N KIOS_2 00:1F:1F:89:80:28 7 Intellinet 300N KIOS_3 00:1F:1F:89:85:50 11 Cisco WRT160NL KIOS_4 68:7F:74:11:39:96 1 Cisco WRT160NL autowinfi 68:7F:74:11:38:AC 6 Cisco WRT160NL KIOS_6 68:7F:74:11:3A:B3 7 Cisco WRT160NL KIOS_7 68:7F:74:11:39:F0 11

4 Figure 3: Intellinet 300N b/g/n AP. Figure 4: Cisco WRT160NL b/g/n AP. Tablet PCs The specifications of the Tablet PCs, including the brand/model, display technology, operating system and WLAN adapter model are tabulated in the following table. Model Display Operating System WLAN adapter Fujitsu-Siemens 10.4 Capasitive Microsoft Windows XP Intersil Prism Stylistic ST4121 Reflective TFT (stylus) Asus eeepc T101MT 10.1 Resistive Multitouch TFT (stylus+finger) Microsoft Windows 7 Atheros AR9265 Figure 5: Fujitsu-Siemens Stylistic ST4121. Figure 6: Asus eeepc T101MT.

5 3. List of Software Components In the deployment of the indoor positioning system we have used some open source or freely available software in order to test the functionality of the WLAN APs and analyze the coverage in the area of interest. Moreover, we have developed custom software using MATLAB for collecting the training data (i.e. RSS fingerprints), analyzing these data (e.g. RSS levels of a specific AP at the reference locations inside the area, number of detected APs per reference location). Finally, we have also developed a piece of software and a Graphical User Interface (GUI) in MATLAB to perform positioning and plot the user location on the floorplan map of KIOS Research Center. Specifically, we have used the following software components. Open source and free software Ekahau HeatMapper Ekahau HeatMapper is a free software tool for quick and easy coverage mapping of Wi-Fi (802.11) networks. It's the only free, easy-to-use tool that shows, on a map, the wireless network coverage in a home or small office; see a screenshot in Figure 7. HeatMapper also locates all access points and provides a real-time view to all access points and their configurations [1]. Figure 7: Ekahau HeatMapper software tool.

6 Metageek InSSIDer InSSIDer is a free, open-source Wi-Fi scanning software [2]. This is an alternative to network scanners like NetStumbler [3], however inssider works better with Windows Vista, Windows 7, and 64-bit PCs. It uses the native Wi- Fi API and the current Wireless network card, while it sorts the results by MAC Address, SSID, Channel, RSS and "Time Last Seen". Some very helpful features include inspecting private WLAN APs and surrounding networks to troubleshoot competing APs, tracking the RSS in dbm over time, filtering the APs in an easy-to-use format and highlighting APs for areas with high Wi-Fi concentration. A screenshot of the inssider software is depicted in Figure 8. Figure 8: Metageek inssider software tool. Custom MATLAB software WiFi Scanner This software is written in MATLAB to aid the collection of the training data inside the area of interest. The user initially provides an image of the floorplan map, the actual dimensions of the floorplan and the resolution of the image. For the data collection process, the user clicks on the map to define the reference locations where the RSS fingerprints will be collected. This is

7 illustrated in Figure 9 for the KIOS floorplan, where the tiles on the floor are also included. The number of fingerprints to be recorded per reference location is a user-defined parameter. The user, moves to the starting location and by pressing s on the keyboard the software collects the fingerprints. Then, all the reference locations are visited in sequence and when the last location has been sampled the user presses q on the keyboard to quit the software and all RSS fingerprints are stored in a file along with the respective location coordinates. We are using a local coordinate system and location (0,0) is the lower left corner of the floorplan map. Figure 9: Setting reference locations with the WiFi Scanner software tool. Training Data Analyzer This MATLAB software is used to analyze the collected training data. The user is able to see the number of detected APs per reference location and the average RSS value of a specific AP at the reference locations where it is detected. This software will be demonstrated in detail in Section 5. WiFi Tracker This software and GUI, shown in Figure 10 for the Asus eeepc tablet, is written in MATLAB and is the actual positioning application. It exploits the training data collected at the reference locations and estimates the user

8 location using the currently observed fingerprint. For the time being only the K-Nearest Neighbour (KNN) positioning method has been integrated in the application and the plan is to develop and add more methods in the future. The user can initiate positioning by pressing the Track me!!! button and has the option to visualize the current location estimate (point), the history of all estimates (points connected to form the estimated travelled path) or the room that he/she resides on the floorplan map. There is also the possibility to activate the Kalman filter as a post processing step to smooth out some high errors in the location estimates provided by KNN method. More options, such as a Particle filter, will be included in future versions of this software. Filtering can be used in combination with the visualization options discussed previously and when the history of estimated locations is selected, then the estimated paths both with and without filtering are plotted to highlight the importance of filtering for improving the positioning accuracy. Pressing the Quit button exits the software. Figure 10: Screenshot of the WiFi Tracker GUI.

9 4. Experimental Setup The APs are placed in different locations throughout the floor to provide coverage in the whole area of interest and most of them are installed in the ceiling, so as to reduce the disruption of the signal propagation as people are moving around; see for instance Figure 11 and Figure 12. Figure 11: Installation of the AP with SSID autowinfi in room 208. Figure 12: Installation of the AP with SSID KIOS_1 in room 209. The exact locations of the WLAN APs are shown on the floorplan in Figure 13. Figure 13: Locations of the WLAN APs in the experimental setup.

10 Next, we used the Ekahau HeatMapper software to check the coverage throughout the floor and the coverage of each AP. In Figure 14 the WLAN coverage in the whole area is plotted and we observe that the WLAN signals are quite strong almost everywhere (darker green colour indicates stronger signal) and there is at least one AP that provides strong signal to every location in the floor. We can also see that the software has more or less accurately placed the APs on the map, quite close to their actual locations. Figure 14: WLAN coverage in KIOS Research Center. We used this software to verify the coverage of a specific AP and get a rough idea of the RSS values related to that AP. The coverage of the AP with SSID autowinfi (placed in room 208) is depicted in Figure 15. We can see that the RSS values from that AP are getting weaker as we move away from it, i.e. from the left to the right part of the floor. Similarly, the coverage of the AP with SSID KIOS_4 (placed in room 202) is depicted in Figure 16 and we can see that in this case the RSS values are getting weaker as we move away from it, i.e. from the right to the left part of the floor.

11 Figure 15: Coverage of AP with SSID autowinfi. Figure 16: Coverage of AP with SSID KIOS_4. We used the Metageek inssider software to get more details on our installation. We placed the eeepc Tablet in the middle of room 208 and monitored the RSS values of all available APs for 5 minutes. In the lower left corner the total number of detected APs is shown, including the private APs in our installation and several APs in the neighbourhood; see Figure 17. We have selected the APs in our setup and the table lists the MAC addresses, SSIDs, RSS values (noted as RSS Indicator - RSSI), transmission channels, vendor, privacy settings etc. The time graph at the lower part shows the RSS values (amplitude in db) for our APs; note the fluctuation of the RSS values even though the Tablet PC remains static which is caused by the noise in the measurements mainly due to multipath propagation and shadow fading. Figure 17: Time graph of RSS values when the Tablet PC is placed in room 208.

12 We have highlighted the AP with SSID autowinfi which has the higher RSS value in this case (mean RSS value around -38dB) and this is expected because this AP is located very close to the Tablet PC. Another example is illustrated in Figure 18, while monitoring the RSS values of our APs when the Tablet PC is placed in the middle of room 202. In this case the AP with SSID KIOS_4 installed in that room is closer to the Tablet PC and provides higher RSS values (mean value around -40dB) and this is verified by the time graph. Figure 18: Time graph of RSS values when the Tablet PC is placed in room 202. Taking a closer look at both time graphs it is now easy to understand how positioning with RSS fingerprints works in practice. Assuming the following ordering of our 7 APs (KIOS_1, KIOS_2, KIOS_3, KIOS_4, autowinfi, KIOS_6, KIOS_7) we can see from Figure 17 that the RSS signature of the central location in room 208, i.e. the mean RSS fingerprint is (-78, -70, -72, -68, -38, - 75, -71), while for the center of room 202 it is (-67, -58, -49, -41, -74, -63, -74). With this information we can distinguish the two locations (and of course the rooms) and perform positioning later using a new fingerprint recorded at the unknown user location.

13 5. Measurement Campaign We performed an extensive measurement campaign with both Tablet PCs to collect the training data for each device. First, we identified 109 reference locations that form a grid to cover the whole floor and these locations are m apart. We have used the tiles (size 60x60cm) on the floor to easily identify the reference locations which are shown in red in Figure 19. We then employed the WiFi Scanner software to collect 20 fingerprints per location for a total of 2180 fingerprints as the training dataset for each device. Figure 19: Reference locations in KIOS Research Center. In the following we used the Training Data Analyzer software to analyze the training data collected with the eeepc Tablet. First, we examine the number of detected APs per reference location (Figure 20). We can see that in most locations all 7 KIOS APs are detected, while in few locations 6 KIOS APs and in only two locations five APs are detected; see the colour bar next to the figure. Note that in some reference locations, especially in the rooms next to the balcony, many APs outside KIOS are also detected. For simplicity we do not consider those APs in our analysis, however they are kept in the reference fingerprints and used during positioning because they improve the performance of the positioning method.

14 The mean RSS value of a specific AP at the reference locations is also of interest; see for instance mean RSS value of the APs with SSID KIOS_2, KIOS_1 and KIOS_4 in Figure 21, Figure 22 and Figure 23, respectively. Figure 20: Visualization of the number of APs detected per reference location. Figure 21: Visualization of average RSS values for KIOS_2 AP. Figure 22: Visualization of average RSS values for KIOS_1 AP. Figure 23: Visualization of average RSS values for KIOS_4 AP.

15 6. Indoor Positioning We can then use the collected training data to perform positioning with the WiFi Tracker software by employing the currently observed fingerprint. One option for the user is to visualize his/her currently estimated location on the floorplan map. In Figure 24, the user is located inside the conference room (room 209) and the positioning method successfully estimates the location which is shown with a point mark (green star over a red square). As the user moves around in the floor the location is updated according to the new RSS fingerprint. A second option for the user is to see the location history as he/she moves around. In Figure 25 the user follows a circular path around the cubicles in the centre of the floor and the successive location estimates are shown in blue dots connected with a line to represent the travelled path. The red square indicates the beginning of the path (i.e. first location estimate), while the green star indicates the current location estimate. We can see that the location estimates are not always very accurate and the estimated path does not reflect the actual path. However, using more advanced positioning methods, such as probabilistic methods or our proposed method that uses Radial Basis Function Networks, the positioning accuracy is expected to be improved. However, the performance in terms of the positioning error can only be slightly improved in such an environment because the RSS values do not vary significantly in the centre of the floor, where the cubicles are located. In other words, any fingerprint-based positioning method will not be able to distinguish among the different reference locations in this part of KIOS Research Centre. In more large scale setups that include multiple floors, corridors and concrete walls fingerprint-based methods perform better in general. However, in our setup we can still locate the user and provide room level accuracy as shown in Figure 26. In this case, the user is correctly located in room 204, which is highlighted with light green colour. Finally, the user may select any of the visualization options discussed previously combined with filtering. Filtering is a statistical processing method that is applied on the rough location estimates provided by the positioning

16 method. Filtering improves the overall tracking performance by mitigating the highly erroneous location estimates that do not correspond to the user mobility pattern that is assumed. In our case the user is assumed to walk at a steady speed inside KIOS Research Centre. Currently, we have integrated Kalman filter in the WiFi Tracker software and Figure 27 depicts the GUI after the user has walked on the same circular path around the cubicles as before. The rough location estimates obtained with the KNN method are shown in magenta and we can observe that the estimated path contains many location estimates that are far from the actual locations. However, the filtered estimates shown in blue are closer to the actual travelled path because the user is assumed to be walking and some high errors in the rough location estimates are smoothed out. Figure 24: Visualizing the current estimated user location. Figure 25: Visualizing the history of estimated user locations. Figure 26: Visualizing the room where the user resides. Figure 27: Visualizing the history of user locations when filtering is selected (magenta: no filtering, blue: Kalman filter).

17 References [1] [2] [3]