A SOFTWARE BASED INDOOR RELATIVE LOCATION MANAGEMENT SYSTEM Zhe Guang Zhou, Aruna Seneviratne, Richard Chan and Prawit Chumchu School of Electrical Engineering & Telecommunications The University of New South Wales, Sydney, Australia {zheguang, aruna, chumchu}@ee.unsw.edu.au
[email protected]
Abstract Wireless local area networks (WLANs) are becoming more popular with the availability of the IEEE802.11 standard and relatively low cost equipment which conform to it. Together with the deployment of next generation wireless networks are expected enable the users to be "always-connected". To exploit the benefits of being always connected, it will be necessary and desirable to provide location-based services to mitigate the differences in operating environment and to enhance the provided services. The provision of location based services is predicated upon the system being able to determine the location of the mobile devices. Current location determination schemes rely on additional hardware to establish the location. However these hardware-based schemes are expensive and difficult to configure. This paper presents a software based location management scheme which exploits the fact that indoor environments will have fixed devices at known locations, to achieve greater accuracy than any of the schemes that have been proposed to date. The viability and the accuracy of the system are demonstrated through a prototype implementation.
Key Words Location management techniques, wireless networks, ad-hoc networks
mobile
tracking,
1. Introduction Wireless local area networks (WLANs) are becoming more popular with the availability of the IEEE802.11 standard and relatively low cost equipment which conform to it. Together with the deployment of next generation wireless networks are expected enable the users to be "always-connected". To exploit the benefits of being always connected, it will be necessary and desirable to provide location-based services to mitigate the differences in operating environment and to enhance the provided
services. In the first case, one can suggest 1 the most suitable application and settings to use for a given application, such as using a proxy for distilling all web contents. In the second case, it will be possible to guide users to designated destinations or connect to nearby resources such as printers. In addition, it can be used by network operators to better manage network services and resources such as establishing network initiated handover that provides seamless network connectivity, and load balancing for the base stations. In the past few years, considerable efforts have been devoted to develop mechanisms for detecting and tracking the physical location of mobile devices. This work can essentially be categorized into two groups. One requires additional hardware to provide higher accuracy and the other does not need additional hardware but acceptable accuracy. As the users need to purchase additional hardware, the hardware based schemes result in cost increase and system configuration requirement. Moreover, these methods are not yet standardized, hence usage is very limited, and will remain so for the foreseeable future. The software based schemes overcome these obstacles as they can be used in conjunction with standard WLAN equipments. However, only very few software based solutions have been proposed due the limited accuracy that can be attained. In this paper, we present the findings of an investigation that was carried out to establish the limitations of software based location management, and a scheme that provides better accuracy. The proposed scheme is also based on RF signal strength, but uses fixed devices in the operating environment together with basic triangulation techniques to determine the physical coordinate of the mobile device on a two-dimensional plane. We show that this provides better accuracy than any of the schemes which have been proposed in the literature to date. The rest of this paper is structured as follows. In Section 2, we discuss the related work in the mobile location 1
Can be automatically configured as well.
management. Section 3 presents overview of the relative location management scheme and describes the prototype implementation. In Section 4, the relative location management scheme is described in details and Section 5 presents the results. Section 6 presents the conclusions.
2. Related Work Numerous methods of mobile location detection and tracking indoors have been proposed and deployed. These proposed schemes can be divided into schemes that utilize additional hardware, and those that are purely software based.
2.1 The hardware based systems Several hardware based schemes have been reported in the literature, of those the most widely discussed are the Active Badge system, the Bat system and the Cricket Compass system. The Active Badge system [9] uses infrared (IR) signal for position inference. The mobile device is attached with an additional hardware unit (badge) which emits a unique IR signal periodically (15sec). IR sensors are placed at known positions such as high up on walls or the ceiling of the office to pick up the IR signals. The picked up signal is transferred to a PC on the wired network that executes location estimation software. This system has several limitations to compute the mobile user location. The badge and the receivers have to have line-of-sight as the reflections due to the walls, partitions and furniture are unpredictable. The system requires high-density sensors to be installed in the building and reported to provide high level of accuracy. However, no exact figures have been published to our knowledge. The Bat system [5] is similar to the active badge system. A small hardware, a Bat (wireless transmitter), is attached to the mobile devices. In addition, there are RF base stations scattered through out the space which broadcast RF signals addressed to each bat in the system. These RF broadcasts will be received by both the bat and the sensors of the system. When the bat receives the RF signal, it broadcasts an ultrasound signal with its identity. This ultrasound signal is received again by all the sensors within it can reach. The time difference between the RF signal and the ultrasound signal is used to determine the distance from the receiving sensor to the bat. The Bat system has a reported accuracy of a few centimeters. The Cricket Compass system [7] is similar to the Bat system in terms of using RF and ultrasound signals. The difference between them is that the Cricket Compass is decentralized. In the system, a hardware unit called the Cricket Compass (CC) is attached to the mobile device. It consists of five ultrasonic receivers which are placed as a
“V” shape. Again, beaconing transmitters are placed on the ceiling. These transmitters emit RF signal as well as ultrasound signal to the CCs. The “V” shape receivers detect the phase difference of the ultrasound signals to determine its orientation. In addition, it uses the time interval between the RF signal and the ultrasound signal to determine distance of the transmitter from the CC. This system reported orientation accuracy of a few degrees and location accuracy of sub-centimeters. As can be seen from above discussions, hardware based location management systems provide accurate location information. However they suffer several drawbacks. The IR based system has a limitation due to the optical path requirement. And the scalabilities of the IR based system are poor because of the limited range of the IR devices. All of the hardware based systems require additional devices for signal transmission. This significantly incurs the cost of installation and maintenance.
2.2 The software based systems The advantage of the software based systems is that they require no additional hardware. Again, numerous software based systems have been reported in the literature, one of the most applicable is the RADAR system. The RADAR system [2][3] proposed by Microsoft researchers is a RF based system for locating and tracking the mobile users inside the building. During the configuration phase, a database of RF signal strength (SS) at a set of fixed and known locations is built. This is obtained by walking along the floor of the building and clicking on a map of the floor that is displayed on the Mobile Host (MH). The coordinates of the MH and the SS from a set of Access Points (APs) at that spot are then recorded to form the Radio Map. When operational, a set of SS from a set of APs is measured by the MH. This distance information is used to locate the MH by using the Euclidean distance estimation with several nearest neighbors. The information of the nearest neighbors is the SS measured in the configuration phase. The RADAR system reported a median error distance of 2.65m and 4.3m for empirically and mathematically constructed Radio Map respectively. It also reported that to construct a Radio Map of about 980m2, it is necessary to have more than 40 pre-measurement points. Furthermore, at each pre-measurement point, several measurements are necessary with different mobile orientations. As expected, software based system has lower accuracy compared to the hardware based systems. Although the software based system has lower installation and maintenance costs, the RADAR system has two other major drawbacks in general: 1. The construction of the Radio Maps: uncertainties occur due to the random interference factors such as the existence of individuals in the building;
2.
The accuracy of Radio Maps: the accuracy highly depends on the building structure and layout. This reduces the flexibility of the system as any changes on the building structure will affect the accuracy of Radio Map. Furthermore, any changes to the network architecture such as the addition/removal of an AP will also affect accuracy the Radio Map due to the interference, and changes in signal strength.
devices in the area to form more sub-cells to increase the triangular structure. The only difficulty to construct such network architecture is the development of the wireless devices driver and the packet routing protocol. However, it results in a simple and low cost architecture which does not require any additional hardware. Backbone network
Access Point
3. Relative Location Management
PC
Access Point PC
Printer
Laptop
The software based schemes provide most promise for the foreseeable future, as the standardization hardware based schemes have not yet been contemplated. Thus, our work has focused on improving the accuracy of software based schemes and overcoming the drawbacks associated with pre-measurement for constructing the Radio Maps. We believe that this can be achieved by introducing the concept of Relative Location Management.
3.1 Operational Environment The computer related equipment used in an indoor area can be divided into two types: stationary and mobile devices. The stationary devices, such as printers, PCs and Access Points (APs), are usually at fixed locations due to their size and use. The mobile devices will be carried around by an individual to best suit the working environment. All computer devices will be capable of wireless communication and will be configured as either infrastructure or ad-hoc configuration. In infrastructure configuration, all Mobile Hosts (MHs) communicate via an AP or several APs. In ad-hoc configuration, MHs communicate to each other directly. The APs will usually have coverage overlap to ensure complete coverage. Under this environment, the location of the MH can be detected by using triangulation if it is within the overlapping area provided by the three APs since most of the wireless devices are capable of receiving multiple AP signals simultaneously. However, using APs to provide overlapping area for mobile location management purpose is not cost effective. Thus with relative location management, we propose to combine the infrastructure and ad-hoc configurations.
3.2 Combined Configurations The objective of the combined scheme is to use the infrastructure configuration for providing the MH to access network, and the ad-hoc configuration is for detecting its location. The proposed architecture is shown in Figure 1. Each triangular cell, i.e. a sub-cell, is formed by carefully selecting the stationary devices. For the larger buildings or working areas, the network can be extended by duplicating more triangular with the existing stationary
Printer
PC
Figure 1. Combined Configurations The operation of the proposed scheme is as follows: when a MH is turned on and detects a network, it broadcasts a message to all stationary devices to indicate that it has joined the network. The system becomes operational if a minimum of three stationary devices respond. Since these three or more devices are at fixed locations and their coordinates are stored by the server, the triangle is formed. The server can be connected to the backbone network or the wireless network as long as it can communicate to all client devices. Then the stationary devices start to monitor the location of the MH. The edges of the triangle are used as the sub-cell boundaries since the walls of the room cannot unfortunately be used due to the different materials of the walls, the RF-signals attenuate in different levels [4]. Since the location management server is able to keep track of the MH, then as the MH moves from one sub-cell to another sub-cell, the server can change the monitoring devices automatically without informing the MH. There is a couple of added advantage for this design. Firstly, it is not necessary to consider the layout and partition in the office environment. Secondly, the system is independent of the type of building, i.e. the material used, the thickness of the walls etc.
3.3 Prototype Implementation To test the viability of the proposed relative location management scheme, a prototype of the system was implemented. However, as the current wireless network can only be operated in either the infrastructure configuration or ad-hoc configuration. The goal of this prototype is only aimed at demonstrating the MH location detection capabilities rather than network connectivity. In other words, the system operates only in the ad-hoc mode, and no data transfers between the MHs. The prototype used four laptops, three emulating stationary devices and one as the MH. For the rest of this paper, the stationary devices will be referred as Fixed Points (FPs) and since no data transfer, they represent APs
as well. All of them are Pentium laptops of different speeds, running Microsoft Windows operating systems. Each laptop was equipped with an Orinoco [1] WaveLan PC card. This network interface card (NIC) operates in the 2.4GHz license free ISM band with data rate between 11Mbps and 1Mbps. Its coverage range for open, semiopen and closed areas are 160m, 50m and 25m respectively. The experiment was conducted at a coverage radius of 25m to provide the best coverage overlap. The WaveLan card driver (the client manager [1]) provides information on Signal Strength (SS) and Signalto-Noise Ratio (SNR). In our experiment, we used the SS as the metric to compute the location of the MH rather than the SNR because of the noise level is affected by random fluctuation. IEEE802.11 standard [6] specifies that the minimum beacon interval is 1024µs. The Orinoco WaveLan card driver reads the hardware in 4samples/sec. Hence, in each sample, it computes the signal information in terms of average, minimum and maximum values.
4. Location Estimation Model The location estimation model for the relative location management scheme was done in two phases. Firstly, a simple indoor propagation model was derived. Then this model was used to compute the location of the mobile device relative to FPs at known locations, and thereby determining its physical location.
the surrounding and building type. In our experiments, n is equal to 1.65. And d0 is the close-in reference distance and d is the separation between the RF signal transmitter and receiver. The term, Xσ, is a normal random variable in dB. Hence, the indoor signal propagation model is given by: Pr (dBm) = Pt (dBm) − PL(dB ) where Pt is the transmission power of the transmitter. Xσ has to be defined so that the equations fit into our system.
4.2 Line-of-Sight (LOS) Experiments The experiment measured the SS of the indoor signal propagation in LOS along the corridor of the Electrical Engineering Building at UNSW. This was done by setting up two laptops in peer-to-peer mode. One laptop acted as a FP, and was located at one end of the corridor. The other acted as a MH moving away in a straight line. At each specified point, more than 30 samples of the SS readings were recorded for four different orientations, and the average was used as the SS at that point in each orientation. Figure 2(a) illustrates how the SS varies with distance and also the variation due the orientation of the receiver. The results show a variation of 2.3dB to 12.5dB on the SS at the same point for different orientations. The fluctuation is due to the corridor acting as a wave-guide for the RF signals. The average value and the best-fit line (which is the same as the theoretical negative logarithm curve) of the data recorded are shown in Figure 2(b).
4.1 Indoor Signal Propagation Model The signal propagation in an indoor environment is dominated by reflection, diffraction and scattering. The transmission path between the transmitter and the receiver vary from line-of-sight (LOS) to one that is severely obstructed by the structure of the building (multipath). The multipath within buildings is highly influenced by the layout or partitions of the building, the construction material used, and the objects in the building.
(a) Signal variations
Furthermore, the mobile user her/himself is also a signal obstacle. When the mobile user is facing to one of the FPs, the mobile device has a LOS signal transmission to the facing FP, but the user obstructs the signal from FPs on the opposite direction. Similarly, human movement in the building also affects the signal propagation. Thus, it is very difficult to characterize the signal loss. Therefore a reasonable indoor signal propagation model must be determined to compute the distance of the MH to the FPs.
Figure 2. Indoor LOS Experimental Results
Theoretically, the indoor signal path loss obeys as the distance power law [8]: PL(dB) = PL (d 0 ) + 10n log10 (d d 0 ) + X σ where n is the path loss exponent that indicates the rate at which the path loss increases with distance. It depends on
Although the average asymptote is very close to the theoretical equation, at an arbitrary point, different orientation can result in a maximum difference of 12.5dB. That is a difference in distance between 7 to 10m when using the average asymptote as the reference curve. The experimental data also shows that the signal propagation
(b) Average results
around the FP is not purely symmetrical. However, it could be considered to be circular for simplicity. The error distance due to this assumption is discussed later. During the experiment, it was observed that a human body attenuates the signal by approximately 6.4dB and a 15cm thick wall by approximately 2.1dB. Thus, we mainly considered the path loss is due to human and orientation of the MH. Then the term, Xσ, in above equations could be given by: X σ = m × AF , where AF is the attenuation factor. An AF=3 was used as it gives an acceptable approximation for attenuation due to walls. By the use of the constant multiplier, m, associated with AF=3, we can obtain the attenuation due to human (i.e. 6dB=mxAF, with m=2) and the orientations (9dB in average with m=3). More details about this will be discussed in next section.
4.3 Location Estimation
•
Ideal case In this case, the SS read from the log file accurately represents the distance from the MH to the FP. In other words, this SS fits into the negative logarithm curve of the indoor propagation model. Thus, by substituting this SS into the signal propagation equations, it is possible to found out the exact distance (the distance circle) of the MH and one FP. Three distance circles from different FPs should intersect at one point as shown in Figure 4, thus enabling the physical coordinates of the MH to be found as described in following: the angle θ, can be computed by using the cosine rule since the distance from the MH to the FP0 and FP1 are known as well as the coordinates of all FPs. The distance from MH to FP2 can be used to determine whether the MH is outside the triangle or not. FP2 M dm1
FP0 θ (x0, y0)
Non-ideal case 1 - three overlapping distance circles In non-ideal case 1 shown in Figure 3(a), the SS is not on the negative logarithm curve of the indoor signal propagation model. The measured SS is smaller than the value on the curve with the same distance. After the mathematical conversion, the estimated distance is slightly larger than the exact distance. Under these conditions, the overlapping circles result in an overlapping triangle inside the triangle of the FPs. We use the centre of mass of the overlapping triangle as the location point of the MH. FP2
FP2
FP2 r2
FP1
FP0
(a) Case 1
FP1
FP0
FP1
r1 FP0
(c) Case 3 (b) Case 2 Figure 3. Non-ideal Cases
•
As discussed above, the distance between the MH and FP is computed by the signal propagation equations. In the experiments, the log file of the NIC driver provides a set of SS including average, minimum and maximum values for each sample. The average SS is used to compute the distance from the MH to FP. However, the minimum and maximum SS indicate that there exists an upper and lower bound distance for each sample reading. Therefore, in this subsection, we discuss the basic computations for the location estimations. Due to the constrains of this paper, we only discuss the ideal case for the fundamental triangulation computation and a few non-ideal cases that show how to account for the signal variations.
dm0
•
FP1 (x1, y1)
dFP
Figure 4. Ideal Case
Non-ideal case 2 - non-overlapping distance circles Figure 3(b) corresponds the case where there are no intervening walls between the FPs. In this case, the problem can be dealt with by scaling up the radius of each distance circle by a ratio of the distance to the center of mass of the original triangle and the radius of the distance circle. This is necessary to ensure appropriate scaling. Then the centre of mass at the intersection of the triangle is taken as the location of the MH since it has a very high probability that the MH appear at that point.
•
Non-ideal case 3 - outrange distance circles Figure 3(c) depicts the case that often happens in indoor environments when the actual attenuation is larger than the estimated attenuation. This arises as a result of the walls not having the same thickness (i.e. attenuation), due to other obstacles behind it, such as a cabinet. Furthermore, the attenuation due to human is higher than the walls and the orientation results in unpredictable attenuation. Due to these factors, the result in measured SS will be lower than what we expected. Figure 3(c) shows the distance circles that result in the case if only considering the wall attenuations. The figure illustrates the MH is outside the area of the triangle bounded by three FPs, but in reality the MH is indeed inside the triangle. As discussed in Section 4.2, we use a scaling factor, m, as a multiple of attenuation factors (AF) to dynamically increase the path loss factor Xσ. Therefore, this case can be handled by scaling down the radius of the distance circle. Since the sub-cell boundary is the edge of the triangle, the scaling down can be achieved by subtracting the multiple of AF until it is less than the longest edge from the current FP to another.
•
Non-ideal case 4 - signal strength fluctuation The final case is when the received SS fluctuates, even when the MH is stationary. This is because of the SS written by the NIC driver is not the same for every sample and the difference can be significant. This will result in the estimation of the mobile location changing rapidly as
well. We believe this SS fluctuation is due to the multipath phenomenon for the indoor environment. To overcome this, a circular buffer is used to store the recorded SS. The median value of the last ten SS samples are then used compute the current location. Obviously, this method will cause some delays in estimating the current location. However, since there is 4samples/sec, the maximum time lagging in the location estimation is about 2.5s for a ten-value circular buffer. In general operation, the time lagging is about 1s. This will not significantly affect the accuracy of the system, as movements within building are relatively slow.
5. Experimental Evaluation The experimental area for the relative location detection presented above was 436.8m2 with dimension 18.2m x 24m. We have developed a user interface application using the techniques discussed above to demonstrate the relative mobile location detection. Figure 5 shows a screenshot of the Location Viewer Application. It provides a simple map of our experimental floor and the basic partitions of the rooms. This viewer also shows the location of FPs and the MH. The estimated distances are displayed adjacent to the path connecting the MH and each of the FPs. In the experiments, the actual distance from the MH to the FPs is compared with the estimated distance to determine the absolute error distance. Twenty measurement points inside the small room, the corridor, and the big rooms were taken. The results of the experiments are shown in Table 1. Most of the error distance recorded in experiments is around 1m with only a few exceptions which up to 2.95m. The average error distance in our experiment is 1.3m. It should be noted that, the error distance stems as a result of the distance computation as well as the inaccuracies of the floor plan. The scale of the floor plan is 1:100. A 1mm error on the floor plan results in an error of 0.1m. We believe that the error in the computation is primarily due to the assumption of purely symmetric signal propagation around the FP. Absolute error distance to each Fixed Point Error distance (e) FP0 FP1 FP2 14 points 15 points 14 points 0m ≤ e < 1m 3 points 3 points 3 points 1m ≤ e < 2m 3 points 2 points 3 points 2m ≤ e < 3m
Table 1. Indoor Experimental Results
6. Conclusions and Future Work This paper presented a software based relative location management scheme. The scheme takes advantage of the fact that most indoor environments have fixed devices at known locations. In addition, it exploits the ability of the new wireless LAN interface cards to operate in dual modes to realize the system without any additional hardware. The validity of the proposed scheme was demonstrated through a prototype implementation. The
Figure 5. Screen Capture of the Location Viewer experimental results indicate that with the use of fixed devices as suggested, it will be possible to provide an accuracy of less than 3m. This provides the accuracy level not greater than any other software based systems, without the burden of having to construct the Radio Maps. We are currently investigating the possibility of improving the robustness and accuracy of the relative location management scheme. We believe that this will be possible using the movement histories, and exploiting the fact that the mobile devices can only move in a limited area that surrounds its current location.
7. Acknowledgement This work was carried out through an Australian Research Council Linkage Postgraduate Research Award with Optus Communications.
Reference: [1] ORiNOCO PC card User Guide. [2] P. Bahl and V. N. Padmanabhan, Enhancements to the RADAR user location and tracking system. Technical report, Microsoft Research, Feb. 2000. [3] P. Bahl and V. N. Padmanabhan, RADAR: An in-building RF-based user location and tracking system. INFOCOM2000. [4] J. S. Davis, Indoor propagation at 2.4 GHz. [5] A. Harter, A. Hopper, P. Steggles, A. Ward, and P. Webster, The anatomy of a context-aware application. ACM MOBICOM1999. [6] O’Hara and A. Petrick, The IEEE802.11 Handbook: a designer’s Companion. (Standard Information Network, IEEE Press, 1999). [7] N. B. Priyantha, A. K. L. Miu, H. Balakrishnan, and S. Teller, The Cricket compass for context-aware mobile applications. July MOBICOM2001. [8] T. S. Rappaport, Wireless Communications: Principles and Practice. (Prentice Hall, 2000). [9] R. Want, A. Hopper, V. Falcao, and J. Gibbons, The Active Badge location system. ACM Transactions on Information Systems,91–102, Jan. 1992.
