On indoor position location with wireless LANS

Abstract

Location aware services are becoming attractive with the deployment of next generation wireless networks and broadband multimedia wireless networks especially in indoor and campus areas. To provide location aware services, obtaining the position of a user accurately is important. While it is possible to deploy additional infrastructure for this purpose, using existing communications infrastructure is preferred for cost reasons. Because of technical restrictions, location fingerprinting schemes are the most promising. In this paper we present a systematic study of the performance tradeoff and deployment issues. In this paper we present some experimental results towards such a systematic study and discuss some issues related to the indoor positioning problem.

Full text

ON INDOOR POSITION LOCATION WITH WIRELESS LANS
P. Prasithsangaree
1
, P. Krishnamurthy
1
, P.K. Chrysanthis
2
1
Telecommunications Program, University of Pittsburgh, Pittsburgh PA 15260, {phongsak, prashant}@mail.sis.pitt.edu
2
Department of Computer Science, University of Pittsburgh, Pittsburgh PA 15260, panos@cs.pitt.edu
Abstract – Location aware services are becoming
attractive with the deployment of next generation wireless
networks and broadband multimedia wireless networks
especially in indoor and campus areas. To provide location
aware services, obtaining the position of a user accurately is
important. While it is possible to deploy additional
infrastructure for this purpose, using existing
communications infrastructure is preferred for cost reasons.
Because of technical restrictions, location fingerprinting
schemes are the most promising. In this paper we are
presenting a systematic study of the performance/tradeoff
and deployment issues. In this paper we present some
experimental results towards such a systematic study and
discuss some issues related to the indoor positioning
problem.
I. INTRODUCTION
Mobile computing has evolved over the last several years
and has aimed at providing mobile users anytime, anywhere
access to the right information at the right time. The position
location of users is an important component of mobile
computing to assist them with their desired goals, and
making either the smart workplace or home really
meaningful. Knowledge of the positions of users combined
with user profiles could significantly help in network
planning, in load balancing, caching of information closer to
the user, radio resource management and designing other
performance enhancement methods. Position location is also
receiving increased importance for public safety issues.
It is possible to obtain the position location of a mobile
station (MS) in two ways: by using a special infrastructure
for positioning such as the global positioning system (GPS)
or by enhancing the existing communications infrastructure
to determine the location of users. GPS is not suitable for
indoor areas because of the lack of coverage and it is very
expensive in terms of labor, spectrum and capital costs to
implement a specialized infrastructure in indoor areas solely
for position location. As such, it is preferable to employ the
existing wireless communications infrastructure to
determine the location of users within the network. In indoor
areas, the wireless communications infrastructure is
primarily based on wireless local area networks (WLANs),
in particular the IEEE 802.11b standard that supports raw
data rates of 11 Mbps [1]. Our focus here thus lies on
experimental results with an IEEE 802.11b WLAN.
With existing communications, there are three basic
methods for determining the location of users: (a)
triangulation that requires at least three distinct estimates of
the distance of the MS from known fixed locations
1
, (b)
using the direction or angle of arrival (AOA) of at least two
distinct signals from known locations and (c) employing
location fingerprinting schemes. Owing to the harsh
multipath environment in indoor areas, techniques that use
triangulation or direction are not very attractive and often
can yield highly erroneous results [2-4]. Location
fingerprinting refers to techniques that match the fingerprint
of some characteristic of the signal that is location
dependent. The fingerprints of different locations are stored
in a database and matched to measured fingerprints at the
current location of an MS. Some companies such as [5] have
used the multipath characteristics of a signal as its
fingerprint. Such techniques require specialized hardware in
every base station (BS) (or access point - AP) to correlate
the multipath characteristics. In WLANs, an easily available
signal characteristic is the received signal strength (RSS)
and this has been used in [8-12] for fingerprinting. The RSS
is a highly variable parameter and issues related to
positioning systems based on RSS fingerprinting are not
understood very well.
There is a significant cost in the comparison of measured
data with the stored information. This may not very costly
for small areas, but it becomes an increasingly important
component as the area to be covered and the number of
users becomes large. However, due to the physical and
technological limitations associated with other techniques,
location fingerprinting schemes remain the most feasible
solution for indoor position location. While the work in [6-
9] addresses the issue of accuracy of using location
fingerprinting, very little has been done to systematically
approach this problem. The tradeoffs between deployment
issues, accuracy, size of the database, robustness of the
system if some access points fail, and performance are
lacking. In this paper we discuss these issues and present
some preliminary experimental results on the deployment
issues, performance, accuracy and robustness of location
fingerprinting using the RSS. Our analysis is based on data
collected in our building where an IEEE 802.11b WLAN is
deployed.
In Section II, we elaborate on position location issues and
provide a quick overview of related work. Our experimental
testbed, methodology, data collection and the creation of the
database are described in Section III. Section IV discusses
the analysis and experimental results.
1
Distance estimates can be obtained from the times / time differences of arrival of signals (using the
speed of light) or from the RSS that falls as a function of distance.

II. ISSUES IN RSS BASED LOCATION FINGERPRINTING
A. RSS-based Techniques
The big advantage of RSS-based techniques is that we can
use the existing infrastructure to deploy a positioning system
with minimum additional devices. It is far easier to obtain
RSS information than the multipath characteristic, the time
or angle of arrival that require additional signal processing.
The RSS information can be used to determine the distance
between a transmitter and a receiver in two ways. The first
approach is to map the path loss of the received signal to the
distance traveled by the signal from the transmitter to the
receiver. With the knowledge of the RSS from at least three
transmitters, we can locate the receiver by using
triangulation [3]. Here, there is no database search and the
positioning delay is just related to the communication and
computation. However, inside a building, the variation of
the RSS with distance (the inaccuracy of the path-loss
model) is significant due to obstructions and multipath
fading effects. As such it is usually not reliable to use the
RSS in this manner. This method is used in [9] along with
training and interpolation to improve accuracy. Another way
is to use the path loss models to compile an artificial
database of RSS values [6]. The measured RSS values are
then compared with this database to obtain the MS’s
location. This seems counter-intuitive as it retains the
inaccuracy of the path-loss models and the delay associated
with the database search.
In order to employ RSS-based techniques with greater
accuracy, the second approach is to use the RSS in a
fingerprinting scheme. In [6], a system called RADAR that
consists of three Pentium-based PCs as access points and a
laptop computer as an MS is installed in a building. The
three access points (APs) measure the RSS from the client.
This is thus a remote positioning system where a central
point computes the location of the MS. The measurements
are then correlated with entries in a database that are filled
up with similar measurements performed with MSs at
known locations. These measurements consist of average
values of the RSS (the average computed using several
samples at the known location). In summary the RSS values
at three APs are used as the location fingerprint. Privacy can
be a problem as the APs can determine a client's location
even when the client does not want anyone to know his/her
location. Additionally, when the number of mobile clients
increases, the BSs could be overloaded.
The experiments in the RADAR system are set up only in
the hallway where the RSS is stable and strong. The
measurements are all in a single floor with three APs. A
problem with the multifloor environment is that two or more
very different locations could potentially have the same RSS
location-fingerprint. In [8, 10], instead of storing average
RSS values, the joint or marginal distributions of the RSS
are used in fingerprinting locations. The experiments are
once again conducted in hallways and the accuracy ranges
between 5 and 20 feet in most cases.
B. Issues in RSS-based Location Fingerprinting
There are several issues in location fingerprinting based on
the RSS that need further evaluation [11]. These are briefly
discussed below. We elaborate on many of these issues in
our results and expect to continue work in those that are not
adequately addressed in this work.
1. Self-positioning: Instead of three AP's measuring the RSS
from a MS, the MS could measure the RSS values from
multiple APs. As and when it requires its location, it can
request the information from the network. This improves the
privacy of the MS and reduces the continuous computational
burden on the network. We implement a self-positioning
mechanism in our work.
2. Granularity: The database entries are collected on a grid
of points within the building. The spacing between grid
points influences the granularity of the position estimate.
Decreasing the spacing (e.g. taking RSS measurements
every foot) will increase the database size but are unlikely to
yield a better accuracy because the RSS values measured a
foot apart will be more or less the same. On the other hand,
if the spacing is very large, it may reduce the search space
but drastically decrease the accuracy. We consider two
specific grid spacings: 5 ft (1.5 m) and 10 ft (3 m) to
evaluate the tradeoffs between performance and delay.
3. Algorithms: There are two basic algorithms for comput-
ing the location of the MS. These are both based on the
signal distance between the measured fingerprint and
fingerprint entries in the database. The generalized weighted
L
p
distance between a measured RSS vector [x
1
x
2
…x
N
] and
a database entry [x
1
’
x
2
’
…x
N
’
] is given by:
p
N
i
p
ii
i
p
xx
wN
L
/1
1
'
11








−=
∑
=
(1)
The two algorithms are:
a) Choose the location corresponding to the fingerprint with
the minimum distance to the measured fingerprint. The
Manhattan L
1
distance (p = 1) and the Euclidean L
2
distance
(p = 2) are considered with w
i
= 1 for all entries and
measurements in related works like [6].
b) Choose M closest database entries (those with the
smallest signal distance) and estimate the location based on
the average of the coordinates of these M points.
In addition to these algorithms, intuitively, we may consider
enhancing the algorithms to take into account the way the
data was collected or using the information resulting from
the search. The weight w
i
can be used to bias the distance by
a factor that could indicate how reliable a database entry or a
RSS measurement is. Since the database entries are average
values of the RSS at a location, the number of samples used
to compute this average could be an estimate of the
reliability of the database entry. We evaluate such
modifications to algorithms and our results surprisingly
indicate that the accuracy improvement is negligible.
4. Fault-tolerance: An important issue with position
location mechanisms will be their reliability. In many cases,

some access points may be disabled because of local power
failures, management, upgrades etc. In such cases, the user
must still be able to obtain some position location service. In
this paper we shut down the AP closest to a MS and
evaluate the algorithms used for positioning to determine
their robustness in terms of accuracy with an AP unavailable.
5. Performance: Issues related to the performance [12] of
positioning are very important. The accuracy of the location
information (the error in the estimated location of the MS),
the delay in making the position location estimation (delay
in making the position location and conveying this
information to the requested party), the capacity (how many
requests for location estimation can be processed in unit
time) and coverage (the area where the position location
service is available) are important performance measures.
The architecture of the building could result in layers of
accuracy - for instance, accurate to 5' in certain areas,
accurate to 10' outside of these areas but within other limits
and accurate to 20' outside these limits. We address these
issues in this paper.
III. EXPERIMENTAL TESTBED
The experimental testbed is located in our eight-story
building. The WLAN consists of ten access points located
opportunistically on multiple floors. Consequently, this is a
multi-floor experiment although in this work we conducted
our experiments only on locations in the 4th floor of the
building. The layout of the 4th floor is shown in Figure 1.
The floor has dimensions of 45 feet by 105 feet and includes
more than 10 rooms. The APs are shown as satellite dishes
in this floor plan. The other APs that can be seen from these
locations are on the third, fifth and sixth floors.
The data collection to populate the database for correlating
the location fingerprint consisted of recording the RSS from
each AP as a function of the location. The number of APs
seen by the mobile host can vary depending on the RSS,
path loss, interference, and multi-path fading at different
locations, and this creates the location fingerprint. We
measure the RSS at various locations such as in a hallway
and in various rooms. These locations have very different
fingerprints. In the hallways (where APs are located), the
RSS is strong. Therefore, the MS can receive signals from
many different APs (which can be considered as a good
fingerprint). On the contrary, the RSS is weaker in the
rooms, and the MS may not have a good fingerprint. At each
location, we calculated the average of 40 samples. The 40
samples are composed of 5 samples in each of the MSs
orientation at two different times of the day. We use four
orientations at each location as explained in [6]. This is
because the RSS at a given location can vary up to 5 dB due
to the MSs orientation. We used two very different times of
day when the presence or absence of people in the building
significantly affects the RSS values. In summary, our
database contains fingerprints for 60 locations. We created
two databases with different resolutions of 10' and 5' feet,
and their total sizes are about 20 KB and 50 KB respectively.
The MS’s location is then calculated from the RSS
information received from all available APs that are seen by
it. The calculation process involves matching the RSS
information with that in the database. For matching, we used
a simple a linear search algorithm since our database size is
rather small. However, for a larger database, we need search
mechanisms that utilize efficient access methods. While this
is an important issue, we leave the database performance to
future work.
Figure 1: Plan of the 4
th
Floor and grid of locations
We first match the AP IDs of the RSS information with the
AP IDs in the database. If a match is found, the signal
distance is calculated with the two algorithms as described
in Section II. We used the weighted L
p
distance where [x
1
x
2
…x
N
] is the real-time RSS measured by the MS (x
i
is the
RSS from AP
i
) and [x
1
’
x
2
’
…x
N
’
] is the RSS vector in the
database corresponding to those very same AP.
Measurements were not taken in a few rooms in Figure 1.
This is not because the RSS values were unavailable, but
because access to these rooms is restricted.
IV. ANALYSIS AND EXPERIMENTAL RESULTS
A. Algorithms and accuracy
The RSS average tends to vary a lot and it is tempting to use
some enhancements to the basic distance measure. We have
avoided the probabilistic method as this tends to increase the
database size. Increasing the value of p in the L
p
distance
fails to increase the accuracy. So, we used two weighting
schemes, namely the number of signal samples (NSS) and
the standard deviation (SD) of the RSS samples used to
compute the average RSS in the database as a measure of
how reliable the database entry was. We used the NSS and
SD values in (1) for w
i
. The idea here was to see whether
simple techniques like these can improve the accuracy of the
estimate of the position without resorting to complicated
distribution matching schemes. For these algorithms
(referred to as NSS-weight and SD-weight in Figure 2), we
used p = 2. These algorithms belong to the first category –
the search returns the location in the database that has the
smallest weighted signal distance to the measured RSS
vector.
In contrast, the second type of algorithm (see section II)
returns M estimated locations after searching database
corresponding to the M smallest Euclidean distances (p = 2
and w
i
= 1 for all i). From the M locations, the location is
estimated as the “average”. This average corresponds to the
centroid of the M locations. For example, if we return the
two closest locations (M = 2 and called 2-best in Figure 2),

the average location is the midpoint of the straight line
between the two locations. If three closest locations are
returned (M = 3, 3-best), the average is the centroid of the
triangle formed by the three locations.
Figure 2: Performance of different algorithms
Figure 2 shows the accuracy of these algorithms. It is quite
clear that there is no improvement in weighting the distance
with the SD or NSS values. Using the centroid has a better
95% performance compared to all other schemes.
Figure 3: Performance with Closeness-elimination
We also evaluated what we call “the closeness elimination
scheme” i.e., we return K > 3 locations from our database.
We compute the distances between each pair of points and
eliminate those that are farthest in terms of these distances,
keeping only the three with the closest distances between
each other. We then compute the “average” as in the case of
the second algorithm and estimate this as the location. In
Figure 3, the error in the estimates is shown as a function of
the number of returned locations K. Returning more than 8
points tends to induce more error because the 'average'
location of points tends to shift from the MSs’ correct
location due to the closeness elimination. The more the
number of returned points, the greater is the tendency for
them to be scattered. This scheme performs at the same level
as the one with the three closest locations (3-best).
B. Performance-Accuracy Tradeoffs
As discussed in Section II, we also consider the tradeoff
between accuracy and performance by increasing the
granularity of the grid in the database. Table 1 shows the
accuracy (in terms of the mean distance error) and search
times associated with databases having a grid spaced 10'
apart and 5' apart. Increasing the granularity of the grid only
slightly improves the positioning accuracy. However, the
size of database entries is increased by about three times,
and search time for matching the fingerprint is also
significantly increased as expected.
Table 1: Performance Vs Accuracy Tradeoff
Database Granularity Five Feet Ten Feet
Average Distance Error
(× 10 ft)
2.17 2.5
Time to obtain match
(second)
10.43 1.27
C. Fault tolerance of location fingerprinting
Our third experiment was to evaluate the fault-tolerance of
the algorithms in the case where one of the APs on the 4th
floor is disabled (when the MS is measuring the RSS for
position location). This AP incidentally provides the best
RSS to most locations. The solid lines show the error
without failure and the dashed lines with the AP shut down.
Figure 4: Performance with the failure of an AP
As shown in Figure 4, the average distance error when one
AP is down is slightly increased compared to the case with
no AP failure. As before, the algorithm that uses the
centroid of the 3 closest matches tends to perform better
than the other algorithms even with the AP down. This
indicates that the position location is quite robust to failures.
The reason for the robustness is the fact that there are few
other entries in the database that match the location of the
MS.
D. Deployment Issues
An important unanswered question with the studies on
indoor position location is how the positioning system
should be deployed – how many access points are required
for a given level of accuracy (what coverage in terms of
accuracy does an AP provide), how close the grid spacing

should be (having a finer granularity in certain areas could
increase accuracy), whether there should be a mixture of
algorithms, and whether it is feasible to provide the required
accuracy by performing some tradeoffs.
Figure 5: Performance Vs Architecture
In order to gain some insight into answering these questions,
we need to correlate the performance measures (accuracy,
search time etc.) to the physical architecture of the building.
Every location in the building is separated from each AP by
some (or no) walls. This number can be obtained by
determining how many walls intersect the straight line
joining a location and an AP. For each location, we have n
i
walls separating it from the i-th AP. Let n
min
be the
minimum over all visible APs at the location (n
min
= Min
i
{n
i
}). We group locations by n
min
and compute the average
error associated for such locations as a function of n
min
. We
use the Euclidean distance in this case. In Figure 5, we show
how the average of distance error increases for locations
with larger values of n
min
. The results indicate the problem
of user positioning in an indoor area where there is a number
of obstructions. As the number of obstructing walls
increases, the accuracy of the positioning technique is
decreased. The slope of the straight-line fit to the error
values is 0.18. This means that for every additional wall,
there is approximately 1.8 ft increase in the error in the
position estimate. Consequently, to provide a required
accuracy in a building, APs may have to be placed in such a
way, not simply to provide communications coverage, but
also to satisfy the positioning coverage. In our case, two
APs can cover one floor of the building for communications,
but four APs are required to keep the average distance error
to less than 20 feet.
In order to assess the effect of mixed granularity, we used a
hybrid database which has a 5' grid resolution in some areas
and 10' resolution in other areas. Table 2 shows the results
of this experiment. It is possible to slightly improve the
accuracy of the position error by increasing the granularity
of the grid in rooms (where the number of obstructions
between the APs and MS locations is larger). As previously
observed, there is no improvement by increasing the
granularity in hallways where the number of obstructions
between APs and MSs is smaller. The conclusion here is
that hybrid schemes do provide better accuracy and also
good performance by reducing the search time.
Table 2: Using a hybrid database
10 feet 5 feet Database Grid
Resolution
Average
error x 10'
Search
Time (s)
Average
error x 10'
Search
Time (s)
Only
Hallways
1.40 0.32 1.53 1.23
Only Rooms 2.49 0.23 2.16 3.98
Both Areas 2.31 3.77 2.26 11.78
Hybrid 1
1
2.41 2.84
Hybrid 2
2
2.26 6.98
1
5' grid in hallways and 10' grid in rooms
2
5' grid in rooms and 10' grid in hallways
V. FUTURE WORK
This paper has provided a framework for systematically
analyzing indoor position location using WLANs. We have
addressed some of the issues, but more data need to be
collected and analyzed to establish models and
methodologies for deploying a positioning system. We only
have preliminary results on the tradeoffs in the positioning
system and how they relate to the physical building
architecture. We are investigating further these issues – how
many access points are required to provide a given accuracy
with a given granularity of the grid in the database, how the
database should be organized for better searching speed,
whether matching distributions of RSS as in [10] in
locations that are severely obstructed from APs is preferable
to simply matching the average RSS, etc.
ACKNOWLEDGEMENTS
The authors would like to thank Sohail Hirani for collecting the extensive
RSS data used in this work. We also acknowledge NSF grants IIS-9812532
and EWF-0081327 for partially funding this effort.
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