mented on a Google android phone and tested on 3rd floor,. Main-building ... used in real fields if the open radio map accumulates sufficient amount ofWi-Fi ...
Open Radio Map Based Indoor Navigation System
Dongsoo Han, Minkyu Lee, Laeyoung Chang, Hyunil Yang
Department of Computer Science Korea Advanced Institute of Science and Technology 119 Munji-ro, Yuseong-gu, Daejeon, Korea {dshan, niklaus, gadis, alteregos4}@kaist.ac.kr
Abstract-In this presentation, we introduce an open radio
set of Wi-Fi signals captured by a device, and each Wi-Fi signal is represented by a pair of MAC address and Wi-Fi signal strength or Received Signal Strength Indicator (RSSI). There are several approaches for constructing an open in door radio map. Fully participatory approach by general users and partly participatory approach by trained experts and general users are two representative approaches in col lecting Wi-Fi fingerprints. Each approach has its pros and cons in terms of reliability and cost. Once the indoor radio map is constructed and ready, the location recognition is rather straightforward because the location information is associated with every Wi-Fi fingerprints in the radio map. All we need to do is to find the closest Wi-Fi fmgerprint to the Wi-Fi fmgerprint captured by a device from the radio map. If the indoor location recognition for a device is possi ble, we can provide indoor navigation service by incorporat ing an indoor map, and applying a path fmding algorithm for a source and a destination on the map. We implemented an indoor navigation system and tested its feasibility on 3rd floor, main-building, KAIST Munji Campus, Korea. The open radio map was constructed by partly participatory approach. That is, the radio map data for rooms and laboratories were collected by general users, and for corridor by trained experts. According to our test, our radio map based indoor navigation is feasible to be used in real fields if the radio map accumulates sufficient amount of Wi-Fi fingerprints.
map based indoor navigation system. The system is imple mented on a Google android phone and tested on 3rd floor, Main-building, KAIST Munji Campus, Korea. The open radio map for the system is constructed by partly participatory ap proach. That is, the radio map data for rooms and laboratories are collected by ordinary users of smart phones, and for corri dors by trained experts. We confirmed that the open radio map based indoor navigation service has a high potential to be used in real fields if the open radio map accumulates sufficient amount ofWi-Fi fingerprints.
Keywords-indoor navigation; open radio map; fingerprint; place recognition
I.
WLAN
INTRODUCTION
The indoor navigation service for pedestrians is one of in teresting services we can provide through mobile devices [1]. However there are several hurdles we have to overcome to provide a good quality indoor navigation service to users. First of all, the precise indoor location recognition at low cost is the biggest problem that has not been solved yet. The support of diverse forms of indoor map construction and incorporation is another problem we need to address for the complete support of the indoor navigation service. Since the GPS signals are usually blocked in an indoor environment, there were some trials to recognize indoor locations such as Active Badge [2], Active Bat [3], Cricket [4], SpotOn [5], Ubisense [6], and Smart Floor [7]. Howev er these approaches inevitably incur too much cost to cover all the public urban indoor spaces. Fortunately, the recent proliferation of IEEE 802.11 Wi-Fi infrastructure enabled mobile devices to capture several Wi-Fi signals generated from ambient Access Points (AP) in urban indoor spaces. Thus if we could recognize indoor locations of a mobile device based on Wi-Fi signals, it would be possible to rec ognize indoor location of a device. But if there is no addi tional information such as the deployed location of APs, we cannot figure out the indoor location of a device with only Wi-Fi signals captured by the device. When we consider that several millions of APs are deployed in each country by numerous companies and individuals, obtaining the precise deployed location information of APs is almost impossible. In this presentation, we introduce an indoor navigation system based on an open radio map. The open radio map is a collection of Wi-Fi fmgerprints and associated place in formation, where the Wi-Fi fmgerprints are gathered by crowd-sourced approach. Here, Wi-Fi fingerprints denote a
978-1-4244-5328-3/10/$26.00 ©201O IEEE
II. A.
RADIO MAP BASED INDOOR LOCATION POSITIONING
Basic Idea
Usually a smart phone such as Google android phone or Apple i-Phone is equipped with devices that can capture Wi Fi signals at a place or inside of a building. Each Wi-Fi sig nal is usually characterized by its signal strength, direction and MAC address. However the positions of APs cannot be identified directly from the captured signals. Thus we need some preliminary place information associated with AP signals to estimate the location from the captured AP signals. In other words, if we have some preliminary location infor mation related to Wi-Fi fingerprints, a set of captured AP signals by a device at a position provides some clues in es timating the indoor location of the device. For example, if a specific Wi-Fi fmgerprint is captured at a specific place 'Ll some time ago, and a newly captured Wi-Fi fmgerprint by a device such as a mobile phone has a similar pattern to the captured specific Wi-Fi fmgerprint, we can estimate that the I
844
device is located at or near the place 'LI'. That is, we can estimate the indoor location of a device with a specific Wi Fi fingerprint if an appropriate Wi-Fi radio map for the in door site is available.
gation, destination specification is rather simple and straightforward in an indoor navigation as well. Store name, telephone number, room number, etc. or their combinations are used for the specification of a destination. The destina tion is usually specified manually by users. Once the desti nation is determined by a user then a navigation system identifies the location in a map and marks the destination on the map. Thus the navigation system should have some ways or mechanisms for mapping specified destinations to locations on a map. Unlike destination specification, source specification is not the role of users. The source or the current location of a mobile device must be recognized by the system in real time. The radio map and location recognition techniques in the above are used for this. The device captures an Wi-Fi fin gerprints, and its location is estimated based on the captured Wi-Fi fmgerprint and marked on the radio map. The device may capture Wi-Fi fmgerprints several times until it gets more reliable Wi-Fi fingerprint. However the indoor loca tion recognition of a device based on Wi-Fi fingerprints is not accurate enough to mark the location in high precision with a small-size circle on a map. Thus we use a big and gray transparent circle to specify the area where the device is estimated to be located. The size of the circle may change according to the expected accuracies of the estimations.
B. Open Radio Map Construction One of essential preliminary steps for an open radio map based indoor navigation service is the construction of an indoor radio map. The indoor radio map is the collection of Wi-Fi fmgerprints on pre-designated places or points of buildings or indoor spaces. Since we assume that the place information of pre-designated places or points are known at collecting time, the data is collected in a form of pair. The data for a radio map construction can be collected by trained experts or gen eral users through participatory approach. Well-trained ex perts are eligible for collecting good quality data in small areas, whereas general users should be involved in collect ing low or medium quality data in large areas at low cost. We have used a partly participatory approach. That is, the radio map data for rooms and laboratories are collected by general users, and for corridors by trained experts C.
Location Recognition
Once the indoor radio map is constructed, the location of a device with a specific Wi-Fi fmgerprint can be identified based on the information of the radio map. There are several different ways of deciding the location of a device with a certain Wi-Fi fingerprint. kNN method identifies k nearest neighbor Wi-Fi fingerprints of the target Wi-Fi fmgerprint [8]. The center of the k nearest neighbor Wi-Fi fingerprints is regarded as the location of the device. Euclidean distance is usually used in finding the k nearest neighbor Wi-Fi fin gerprints. Probabilistic methods computes the mean and variance of Wi-Fi fingerprints for every location using the data collected in training stage [9]. Once the mean and va riance of Wi-Fi fingerprints of every spot or location are available, we can compute the probability for a device with a specific Wi-Fi fmgerprint to be in the location. The loca tion with the highest probability is regarded as the location of the device. Meanwhile the Wi-Fi fingerprints captured by one device shows slightly different patterns from those captured by other devices in terms of the number of signals and their strengths. This may severely affect to the accuracy of loca tion recognition. In order to cope with this problem, we need to adjust the Wi-Fi fmgerprints of one device to anoth er. Since there are dozens of devices and mobile phones, we prepare an adjustment table for the adjustments. The ad justment table is updated whenever a new device is intro duced. Therefore when we collect Wi-Fi fingerprints for a radio map construction, the device information should be stored together with the Wi-Fi fmgerprints. III. A.
F309 (Meeting Room A)
FJO'
FJ02
d I
Figure 1. A Part of 3,d Floor, Main Building, KAIST ICC Munji Campus.
B. Path Finding Once a source and a destination are specified and marked on a map, finding a shortest path from the source to the des tination follows in the next step. In order to perform the path-finding, the map is converted into a graph where pre designated points on the map are represented by nodes, and links between the points are represented by edges. We as sign a point to the center of each room or a small area and use many number of points to represent corridors. The door or entrance information is useful in constructing such a graph. Then a shortest-path finding algorithm is applied for a given source and a destination. Figure 1 shows a part of the map of 3rd floor, main building, KAIST Munji Campus and an associated graph. Among several algorithms for solving the shortest-path finding problem, an algorithm invented by Bellman-Ford is often used for path-finding in navigation [10]. We use
INDOOR NAVIGAnON
Source and Destination Specifications
Source and destination specification is two essential ele ments for an indoor navigation service. Like outdoor navi-
845
Bellman-Ford's path-fmding algorithm for the system. It is a simple dynamic programming based algorithm with single source and single-destination. Unlike other algorithms, Bellman-Ford path-finding algorithm allows not only posi tive but also negative weights in a graph, as long as there is no negative cycle. Once the shortest path is identified, the path from the source to the destination is drawn in line on the map of the building so that the users can figure out their current locations, directions to move, and destinations to go. IV. A.
with Eclipse. Figure 2 shows a path drawn on a map in Google android phone. From the tests for arbitrarily chosen sources and destinations, the navigation system recognized the source locations and successfully drew the paths in most cases. V.
SUMMARY
In this presentation, we introduced an open radio map based indoor navigation system. Although we confirmed that the open radio map based indoor navigation service was possible at the limited indoor spaces we tested, there still remains a long way to go to make the system complete in terms of coverage and precision. Nevertheless, the open radio map based indoor navigation service deserves to be studied further because there is a high potential that it can provide a useful service in an indoor space with complex structures. We need to continue to accumulate Wi-Fi fmger prints and associated maps all over the places for the service. Once the level of service that the system can deliver to gen eral users reaches to an acceptable state, the integration of indoor and outdoor navigation service would be the appar ent next step.
IMPLEMENTATION
Radio Map Construction Tool
We developed a tool to help the construction of a radio map. The tool provides utilities to capture Wi-Fi finger prints and to specify the points or places where the Wi-Fi fingerprints are captured on a map. The tool also provides utilities to import maps. A map drawn by users or a CAD map can be imported by the tool. After capturing finger prints, users have to specify the points where the finger prints captured either by attaching place information to the fingerprints or marking the points on the map. For rooms or closed small spaces, attaching place information may enough for specification. The center of the space is consi dered as a representative point in that case. However for the large open space or narrow long space like a corridor, mark ing the point where the fingerprints captured on the map is more suitable. The tool generates a graph based on the marked points and representative points. In order to make the graph more complete, the tool may need a help from users in specifying corridor information in the graph.
ACKNOWLEDGEMENT
This work was supported by the MKE, Korea, under the ITRC support program supervised by the NIPA (NIPA2009-(C 1090-0902-0036)). REFERENCES
[I)
Golding, A R. and Lesh, N. Indoor navigation using a diverse set of cheap, wearable sensors, Proceedings of the third International Symposium on Wearable Computers. San Francisco, California, pp. 29-36, October, 1999.
[2]
R. Want, A Hopper, V. Falcao, and 1. Gibbons, The Active Badge Location System, ACM Transaction on Information Systems 10, I
[3]
A Ward, A Jones, and A Hopper, A New Location Technique for the Active Office. IEEE Personal Communications 4, 5 ( 1997), pp.
( 1992),9 1- 102.
42-47.
[4]
N. Priyantha, A Chakraborty, and H. Balakrishnan, The Cricket Location-Support System, In Mobile Computing and Networking (MOBICOM 2000) (2000), pp. 32-43.
[5]
J. Hightower, G. Borriello, and R. Want, SpotON: An Indoor 3D Location Sensing Technology Based on RF Signal Strength, University of Washington CSE Tech-nical Report #2000-02-02
(2000).
[6) [7]
Figure 2. A path is drawn on a Google android phone.
B. Radio Map Construction
[8]
Wi-Fi fingerprints to construct an open radio map were collected on 3rd floor, main-building, KAIST Munji Cam pus, Korea. We collected Wi-Fi fingerprints for rooms and laboratories through a participatory approach, whereas for corridors by two master students i.e. by trained users. 4000 Wi-Fi fmgerprints were collected in total. C.
Ubisense Website, http://www.ubisense.net. R. Orr, and G. Abowd, The Smart Floor: A Mechanism for Natural User Identification and Tracking. In Proceedings of the 2000 Conference on Human Factors in Computing Systems (2000). P. Baht, and Y.N. Padmanabhan, RADAR: An In-Building RF-based User Location and Tracking System, In IEEE INFOCOM (2000), pp.
775-784. [9]
M. Youssef, A Agrawala, and AU. Shankar, WLAN location determination via clustering and probability distribution, In Proceedings of IEEE Pervasive Computing and Communications (2003), pp. 143- 15 1.
[ 10] Dimitri P. Bertsekas, A Simple and Fast Label Correcting Algorithm for Shortest Paths, Networks, Vol. 23 ( 1993) pp. 703-709.
Client VI Implementation
A Google android phone was used for a client VI imple mentation. It was developed in android developing package
846
