ON THE USE OF VARIOUS POWER LEVELS TO IMPROVE

WIRELESS LAN-BASED POSITIONING

Using Multiple Power Levels With Fingerprinting Algorithms

Hendrik Lemelson, Thomas King, Stephan Kopf and Wolfgang Effelsberg

Department of Computer Science IV, University of Mannheim, Germany

Keywords:

Wireless LAN, positioning systems, ﬁngerprinting, transmission power, context-aware applications, location-

based services.

Abstract:

When talking about location-based services, one of the key factors inﬂuencing the overall service quality is the

positioning accuracy that the system can rely upon. Nowadays, wireless LAN hardware supports the variation

of the transmission power in a wide range. In this paper, two novel algorithms suitable for extension of

common wireless LAN-based positioning systems are presented. These novel algorithms exploit the additional

information that can be gained by using one or more non-standard transmission power levels. Our ﬁndings

indicate that the overall positioning accuracy and reliability can be increased with such an approach.

1 INTRODUCTION

In the past few years we have seen an on-going minia-

turization and increase of features on mobile devices.

This, in conjunction with increasing wireless band-

width available on these devices, opens the door for

new mobile applications. Especially in the research

focus here is a new type of application where the

user’s device supplies additional information to cre-

ate a more feature-rich and user-adaptive experience.

The application or service can vary its behaviour

depending on the user’s preferences, locational infor-

mation or other context information such as weather

data, available credit cards or simply, the time-of-the-

day (Chen and Kotz, 2000). These kinds of applica-

tions are called “context-sensitive applications”.

A subset of these context-sensitive applications

are the location-based ones. Leaving all other con-

text information aside, here, the service is only sup-

plied with information about the current user loca-

tion. This already increases the overall service qual-

ity a lot. Considerable research was done on location-

based services in the past few years and many promis-

ing approaches were found.

One of the major challenges in this area is the ac-

curate and reliable localisation of an object or user.

When talking about outdoor scenarios, satellite-based

systems such as GPS or Galileo (Kaplan and Hegarty,

2005) offer an almost optimal solution; however in

the indoor scenario there is still room for improve-

ment. Satellite-based systems fail to work here be-

cause the received signals are either too weak to be

used or the time-of-ﬂight measurements are very im-

precise due to signal shading, reﬂection and multipath

propagation. One of the more promising approaches,

therefore, is the positioning based on wireless LAN

access points.

Even though wireless LAN is already a well

known technique, there is still much development

done especially regarding the hardware. One of the

new features that many up-to-date wireless LAN ac-

cess points offer, is the possibility to vary the trans-

mission power as needed.

Most promising positioning systems based on ﬁn-

gerprinting and using wireless LAN though still take

the access points’ transmission power as ﬁxed. Our

approach is to increase the amount of available infor-

mation for positioning by using multiple transmission

power levels. This paper describes the development

of novel algorithms that beneﬁt from this increase in

available information. On the basis of several well-

known wireless LAN-based ﬁngerprinting algorithms

we implemented novel ﬁngerprinting algorithms ac-

cording to our approach and evaluated these to con-

ﬁrm their advantages.

225

Lemelson H., King T., Kopf S. and Effelsberg W. (2007).

ON THE USE OF VARIOUS POWER LEVELS TO IMPROVE WIRELESS LAN-BASED POSITIONING - Using Multiple Power Levels With Fingerprinting

Algorithms.

In Proceedings of the Second International Conference on Wireless Information Networks and Systems, pages 209-215

DOI: 10.5220/0002148802090215

 SciTePress

Fingerprinting algorithms are split into two

phases. In the ofﬂine phase, data is collected at

given reference positions. This data is used to cre-

ate so-called “ﬁngerprints” for these reference po-

sitions. They reﬂect the unique properties of the

signals at each position. In the subsequent phase,

called positioning, live, or online phase, collected

live samples are compared to each ﬁngerprint to es-

timate the position using an algorithm-speciﬁc met-

ric. To sample the signal strength of access points the

so-called active scanning approach is typically used

(King et al., 2007).

The remainder of this paper is structured as fol-

lows: In Section 2, an overview of related projects in

the area of positioning with wireless LAN and ﬁnger-

printing is given. Section 3 introduces the algorithm

we used as a basis and describes the extensions that

are necessary to use the algorithm with multiple trans-

mission power levels. In Section 4, an overview of the

evaluation testbed as well as the hard- and software

used is given. The methodology of the evaluation is

presented in Section 5. Finally, Section 6 concludes

the paper and gives an outlook to our future work.

2 RELATED WORK

In the area of indoor positioning, much effort and

work were done during the past few years (Want et al.,

1992; Priyantha et al., 2000). One of the newer

projects regarding this topic is the Landmarc position-

ing system (Ni et al., 2004). It uses RFID technology

to estimate the position of objects that are equipped

with RFID tags. One of the key ideas of this system

is the use of several concentric reading ranges to de-

termine properties of the signal space and to increase

the accuracy of distance estimates using this informa-

tion. The Landmarc system was the key motivation

to develop and examine a wireless LAN positioning

system that uses different transmission power levels.

Due to the bad signal propagation properties

that wireless LAN signals show indoors (Rappaport,

2001), the general consensus is that a ﬁngerprinting-

based approach is a good solution for such a scenario.

We chose to base our novel algorithms on the RADAR

positioning system (Bahl and Padmanabhan, 2000)

and the system described in (Haeberlen et al., 2004).

Both positioning systems use wireless LAN and ﬁn-

gerprinting but utilize different metrics. To refer to

the latter one, we use the name RICE for the remain-

der of this paper, even though this is not its ofﬁcial

name.

In this paper, only the results regarding the RICE

positioning system are presented due to the limited

space available.

The RICE positioning system adopts a probabilis-

tic approach. The ﬁngerprints for the reference posi-

tions contain either histograms of the collected signal

strengths of each access point or values for average

and standard deviation of the signal strengths com-

puted from the histograms. The values represent a

normal distribution with these parameters. For both

variants, in the positioning phase a value is computed

for each reference position using the collected live

sample and the corresponding ﬁngerprint. This value

reﬂects the overall probability to be at that position.

The algorithm then selects the reference position with

the highest probability. We use the variant based on

the Gaussian distributions in our evaluation.

As the RADAR system offers a feature called K-

Nearest-Neighbors (Bahl and Padmanabhan, 2000)

that was not available in the original implementation

of the RICE algorithm we extended the RICE system

to support this feature as well for comparability and

fairness. How the extension works in detail is ex-

plained in Section 3.2.

3 ALGORITHMS

The following sections provide a brief overview over

the original implementation of the RICE algorithm,

the K-Nearest-Neighbors extension and our two novel

algorithms that use multiple transmission power lev-

els.

3.1 Original Algorithm

The original RICE algorithm that was taken as a ba-

sis for some of our novel algorithms uses normal dis-

tributions to describe the distribution of the received

samples’ signal strengths. In contrast to the one using

histograms, this variant is more robust and requires

a reduced amount of ofﬂine samples to compute the

ﬁngerprints.

In the positioning phase a sample is collected and

compared to all stored ﬁngerprints using the follow-

ing metric:

The probability P

r,ap

) to receive the signal of

a certain access point ap with a certain signal strength

at a given reference position r is deﬁned by:

r,ap

) =

s+0.5

s−0.5

d f(avg

r,ap

, stddev

r,ap

) (1)

where d f is the density function of the normal

distribution with the average avg

r,ap

and the standard

deviation stddev

r,ap

for the access point ap. These

values are taken from the ﬁngerprint for position r.

Because the probability for a single value is zero by

deﬁnition, we consider the probability for an interval

WINSYS 2007 - International Conference on Wireless Information Networks and Systems

226

of s ± 0.5 instead. The interval of s ± 0.5 is used to

map the discrete signal strength values received from

the hardware (-102dB to 0dB) to the continuous num-

berspace of the normal distribution.

The overall probability computed for one refer-

ence point is now deﬁned by:

(s) = Π

i=1

r,ap

) (2)

Hereby, n is the number of access points that are

found in the collected live sample, s

is the signal

strength collected for the access point ap

and r is the

current reference position of which the ﬁngerprint is

taken as a comparator.

After having compared the collected sample to the

ﬁngerprints of all reference positions, the algorithm

selects the reference point as the estimated position

that has the highest overall probability.

3.2 K-Nearest-Neighbors

As mentioned earlier, we have extended the RICE

algorithm by a feature called K-Nearest-Neighbors

taken from the original RADAR positioning system.

The details of this modiﬁcation are described in (King

et al., 2006). Such an approach was necessary to en-

sure a fair comparison between our novel and the ex-

isting algorithms.

The K-Nearest-Neighbors extension not only uses

the best matching reference position to make a posi-

tion estimate but takes the K best matches into consid-

eration. The coordinates of these K best matching ref-

erence positions are averaged and the result of these

computations is taken as the position estimate. Sensi-

ble values for K are 2 to 4, according to the authors of

RADAR.

3.3 Power Level Aware Algorithms

Using the RICE or RADAR algorithms with a single

transmission power level that simply differs from the

stock one does not require any modiﬁcations as long

as the ﬁngerprints and the live samples are collected

using the same transmission power level.

Using more than one transmission power level

though, requires some changes to the way the algo-

rithm handles the multiple samples and results. The

ﬁrst thing to mention here is, that of course for each

used transmission power level the algorithm has to

be supplied with a ﬁngerprint database for that power

level. Furthermore, if a power level is used multiple

times, the algorithm should be given a distinct ﬁnger-

print database for each occurrence.

In the positioning phase, the algorithm has to be

supplied with live samples for each used transmission

power level or occurrence of a transmission power

level as well. Afterwards, for each reference posi-

tion and sample a probability is computed using the

sample itself and the corresponding ﬁngerprint.

r,tx

) = Π

i=1

r,ap

,tx

) (3)

Here, n is again the number of access points that

are found in the collected sample for power level tx,

,tx

is the signal strength collected for ap

in power

level tx and r is the current reference position whose

ﬁngerprint is taken as the comparator.

When the algorithm has ﬁnished the computation

of the probabilities for all transmission power levels,

there are several ways to handle these results.

3.3.1 Distinct

One way is to handle each probability independently

from the others. For each power level, the reference

position with the highest probability is selected using

the computed probabilities for that power level. This

results in one position estimate for each power level.

Since these estimated positions for the single power

levels can differ from each other, they have to be post-

processed afterwards to produce one ﬁnal result. This

can be done by computing the centroid over all posi-

tion estimates like it is done by K-Nearest-Neighbors

for example.

The advantage of this approach is that no changes

to the core algorithm itself are necessary. Only the

sourrounding logic that supplies the algorithm with

the ﬁngerprint databases and the live samples and

that processes the results has to be adapted. It has

to supply the corresponding samples and ﬁngerprint

databases for each power level and to handle the mul-

tiple results in an appropriate manner.

The direct benchmarks for this algorithm devel-

oped by our team in Mannheim is the RICE algorithm

extended by the K-Nearest-Neighbors feature. Using

a similar approach, our algorithm still has the advan-

tage of using K times the most probable position esti-

mate instead of using the K best position estimates.

3.3.2 Combined

A second possible way of handling the various sub-

probabilities is to merge them into one ﬁnal probabil-

ity per position. In this case, all sub-probabilities for

each reference position are multiplied, thus resulting

in one ﬁnal probability per position. The goal of this

approach is to exploit interference effects between the

single transmission power levels in such a way that

some power levels can absorb outlying values of an-

other power level. This is similar to supplying more

than one live sample to the original algorithms, again

ON THE USE OF VARIOUS POWER LEVELS TO IMPROVE WIRELESS LAN-BASED POSITIONING - Using

Multiple Power Levels With Fingerprinting Algorithms

227

with the major difference of using different transmis-

sion power levels and ﬁngerprint databases in our ap-

proach:

(s) = Π

tx=tx

r,tx

) (4)

When given a sample set s, the overall probability

(s) to be at the reference position r is the product of

all sub-probabilities for the transmission power lev-

els tx

to tx

occurring in the sample set to be at that

reference position (also see Equation 3).

4 EVALUATION SETUP

In the following, we brieﬂy describe the setup of the

evaluation.

4.1 Evaluation Environment

The evaluation environment is the second ﬂoor of the

building A5,6 B at the University of Mannheim in

which the ofﬁces of our department of Computer Sci-

ence are located. The area is split up into two hall-

ways, several ofﬁces and three smaller rooms in the

middle of the hallways (see Figure 1). The two hall-

ways are measured 30 x 6 meters and 15 x 4 meters

respectively covering an area of approximately 240

square meters.

Figure 1: Floor plan of the evaluation environment.

4.2 Hard- and Software

To build up the wireless LAN infrastructure, ﬁve

WRT54GL V1.1 access points manufactured by

Linksys/Cisco are used. They are spread over the

evaluation environment as depicted by the black

squares in Figure 1. These access points do not

support the variation of the transmission power per

se. But as their ﬁrmware is based on Linux and

is Open Source, several alternatives to the manufac-

turer’s ﬁrmware exist. In addition, some of these do

support the variation of the transmission power. We

decided to use the ﬁrmware DD-WRT v23 RC1 de-

veloped by the DD-WRT project

. It offers the pos-

sibility to vary the transmission power in 1 mW steps

from 1 mW to 251 mW and can be controlled using a

web interface or via ssh and telnet.

To collect the ofﬂine as well as the live samples,

we used an IBM Thinkpad R51 laptop computer run-

ning Suse Linux 10.1. Due to driver limitations,

we could not collect our data with the internal Intel

2200bg network card. It was therefore switched off

and a plug-in Lucent Silver PCMCIA card was uti-

lized instead.

On the software side, the samples were collected

with the LocEva framework (King and Kopf, 2007)

which is available in Java. The application to col-

lect the samples uses the Java Native Interface and

a small wrapper written in C to interact with the op-

erating system kernel’s wireless extensions interface

by system calls. This makes it possible to request the

communication parameters and connection informa-

tion from the wireless LAN card’s driver.

Regarding the variation of the access points’ trans-

mission power levels, the application used to collect

the samples was extended by the ability to switch

all access points simultaneously to one transmission

power level. This is done by sending commands to

the web interface of the devices using HTTP requests

and monitoring the status replies to verify the success-

ful execution. As the wireless link was occupied for

sensing, we used a wired ethernet link to communi-

cate with the infrastructure.

4.3 Data Collection

To get a sufﬁcient amount of data for the evaluation

and to achieve stable statistical results, 186 reference

points were laid out in the evaluation environment us-

ing a grid of one meter side length (see the grey dots

in Figure 1). The samples collected at these positions

are the foundation for the ﬁngerprint databases in our

evaluation.

Additionally, 63 live points were randomly spread

over the hallways (see the black dots in Figure 1). The

samples collected at these positions are used to emu-

late a user requesting a position estimate.

The selected transmission power levels for the

evaluation are 3 mW, 13 mW, 23 mW, 33 mW, 43 mW,

53 mW and 63 mW. The lower boundary 3mW was

chosen due to some ﬂuctuations in the signal strength

we observed below that transmission power level. As

we can see in Figure 2, the signal strength decreases

from 0 mW to 3 mW and only afterwards begins to

DD-WRT Project Website: http://www.dd-wrt.com

WINSYS 2007 - International Conference on Wireless Information Networks and Systems

228

increase almost monotonically. The upper bound-

ary for our measurements was selected to stay below

governmental regulations regarding the maximal al-

lowed EIRP (Effective Isotropic Radiated Power) for

devices using the 2.4 GHz band in Germany. Finally,

the stepsize of 10 mW was chosen to get noticeable

differences between adjacent power levels.

−100

−90

−80

−70

−60

−50

−40

0 10 20 30 40 50 60

Signal strength [dB]

Transmission powerlevel [mW]

8m away

24m away

Figure 2: Irregularities regarding the signal strength.

During the data collection phase, 110 samples per

power level were collected for each reference position

and live position. Hereby, the application switched

all access points to the next power level automatically

after having collected 110 samples. This was repeated

until all power levels had been processed.

The collected samples contain a timestamp, the

MAC address of the collecting wireless LAN card, the

current position, the current transmission power level,

and for each received access points the MAC address,

the channel and the RSSI (received signal strength in-

dicator) value. All samples were stored in a logﬁle for

easy reference during the following evaluation.

5 EVALUATION

In this section, the results of the evaluation of our

novel algorithms are presented.

The key questions for this evaluation was whether

a combination of several different power levels or at

least the multiple use of one non-standard power level

would outperform the others. For this reason, we eval-

uated different combinations of transmission power

levels, namely 127 different permutations of the se-

lected seven power levels as well as 49 doublets. A

doublet in this context means that one power level was

used not only once but up to seven times by supplying

the algorithm with multiple samples and ﬁngerprint

databases for a given transmission power level.

For each permutation and doublet, a total of 500

runs were performed for each algorithm. For each run

the algorithms were given 20 randomly selected sam-

ples per power level and reference position to build

the ﬁngerprint database. Afterwards, one sample per

power level and live position was supplied to com-

pute a position estimate. The difference between the

estimate and the real position, called positioning er-

ror, was calculated and stored for later reference and

analysis. In addition, the average positioning error as

well as an error distribution was computed and stored.

5.1 Number of Power Levels

This section summarizes the results on the inﬂuence

of the total number of used power levels on the accu-

racy of the position estimate. As we can see in Figure

3, the number of transmission power levels has a con-

siderable inﬂuence on the average error.

2.2

2.4

2.6

2.8

3.2

3.4

1 2 3 4 5 6 7

Avgerage error [m]

Number of transmission powerlevels

COMBINED

DISTINCT

Figure 3: Inﬂuence of the number of transmission power

levels.

The more power levels are used, the better the re-

sults are due to the higher amount of data available for

positioning. This can be compared to feeding more

than just one online sample to the original variants of

the algorithms.

Also visible is the advantage the DISTINCT algo-

rithm takes by computing a single position estimate

for each power level and merging these afterwards to

one ﬁnal result. Instead of using all the data to com-

pute just one position at the end, this approach pro-

duces better results and a later saturation of the gain.

At ﬁrst, when several position estimates are com-

puted by using several live samples, it is likely that the

estimated positions are located somewhere around the

real position. The error vectors of the single position

estimates therefore are likely to point into different

directions. If these vectors are merged, the interme-

diate errors interfere with each other resulting in a re-

duction of the overall error (Bahl and Padmanabhan,

2000).

ON THE USE OF VARIOUS POWER LEVELS TO IMPROVE WIRELESS LAN-BASED POSITIONING - Using

Multiple Power Levels With Fingerprinting Algorithms

229

2.2

2.4

2.6

2.8

3.2

3.4

3.6

3.8

1 2 3 4 5 6 7

Average error [m]

Number of transmission powerlevels

3mW

13mW

63mW

43mW

23mW

33mW

53mW

Figure 4: Inﬂuence of the transmission power level. when

increasing the number of power levels.

Secondly, averaging several position estimates

makes it possible to also reach positions in between

the grid points that would not be accessible by sim-

ply matching to grid positions. This has a smooth-

ing effect on the overall error distribution because

of the ﬁner granularity of the possible positions

(see Figure 6).

5.2 Selection of Power Levels

The selection of power levels clearly inﬂuences the

results as well. As we recognise in Figure 4, some

of the power levels perform far better than others, no

matter if a transmission power level is used only once

or up to seven times.

While it might not be suprising that the very low

settings perform worse due to their very instable sig-

nals, a mere raising of the transmission power is

clearly also not the best strategy. According to our

evaluation, the best results can be achieved by us-

ing a transmission power level of 53 mW. A further

increase of the transmission power reduces the accu-

racy again (see Figure 5). The reason for this behavior

are the low ﬂuctuations the signals have in that power

level and the good differentiation between different

reference positions in signal space. These ﬂuctuations

are also the reason for the unexpected good result of

the powerlevel 23 mW. In this power level the stan-

dard deviation of the signal strengths was lower than

those of the adjacent power levels such resulting in a

better position accuracy.

Of course, this can not be generalized based on

our test environment as e.g. hardware properties as

well as the structural environment could inﬂuence the

results. To verify the general validity, tests using dif-

ferent hardware as well as a different testbed are nec-

essary.

From our collected results, it is clear, that the use

of different power levels performs worse compared to

3.1

3.2

3.3

3.4

3.5

3.6

3.7

3.8

3.9

4.1

4.2

0 10 20 30 40 50 60 70

Avgerage error [m]

Transmission powerlevel [mW]

COMBINED

DISTINCT

RICE

Figure 5: Inﬂuence of the transmission power level.

using the best power level several times. The 53mW

doublet of a certain size n outperforms all the permu-

tations of the same size regardless of their composi-

tion in nearly all setups. The reason here is that using

only the best intermediate results leads to a better es-

timate than taking worse estimates into consideration,

which is done when using different power levels. Ad-

ditionally, no exploitable synergies between different

power levels are visible that could absorb this disad-

vantage.

5.3 Advantages of Different Power

Levels

When comparing the original algorithm to our novel

algorithms, only minor differences in the accuracy of

the position estimates are detectable. Using the same

power level, the results of the original algorithm given

20 samples per reference position to build the ﬁnger-

print database and s samples during the online phase

are - leaving statistical ﬂuctuations aside - the same

as those of the COMBINED variant using the power

level s times (see Figure 6). This suggests that the use

of several different ﬁngerprint databases for the same

power level does not make much sense if the number

of samples used for each ﬁngerprint is high enough

to create stable values. In this case, the ﬁngerprints

in the different databases for the same power level are

very similar, making more than one database obsolete.

5.4 Advantages of Multiple Power

Levels

Another interesting observation is the high beneﬁt of

computing several independent position estimates and

averaging these afterwards in comparison to summa-

rizing the computational results for all online sam-

ples and then making one position estimate. The ﬁrst

method achieves far better and more stable results. As

WINSYS 2007 - International Conference on Wireless Information Networks and Systems

230

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.1

0 2 4 6 8 10 12 14 16 18 20

Probability

Positioning error [m]

DISTINCT

RICE (K=4)

COMBINED

RICE (7 samples)

Figure 6: Similar results regarding the COMBINED Variant

and the original algorithm given 7 samples, and the DIS-

TINCT variant and the original algorithm merging the 4

best matches respectively.

already mentioned earlier, when merging the position

estimates for each power level, the probability of the

error vector pointing to different directions is quite

high.

A good strategy to deﬁne the parameters for a

wireless LAN based positioning system based on ﬁn-

gerprinting would thus be the following: At ﬁrst, a

well suited transmission power level should be se-

lected. In our case, this would be 53 mW as this trans-

mission power level produced the best results (see

Figure 5).

Secondly, as the gains in position accuracy by in-

creasing the amount of samples tend to fade out at

higher amounts, it seems to make sense to supply up

to three live samples per position estimate to the algo-

rithm (see Figure 3).

Keeping in mind that the merging of several posi-

tion estimates highly increases the accuracy, at least

two to four position estimates should be computed

and merged afterwards. In opposition to the original

approach, a further increase of the number of com-

puted neighbors would not decrease the overall posi-

tion accuracy as the single estimates are calculated in

independent runs.

Finally, the ﬁngerprints stored in the database

should be computed using enough ofﬂine samples to

allow the values to stabilize.

6 CONCLUSIONS

In this paper, we presented novel algorithms using

multiple transmission power levels for ﬁngerprinting-

based positioning with wireless LAN.

We experimentally veriﬁed that the usage of mul-

tiple different transmission power levels for our wire-

less LAN positioning algorithms has minor advan-

tages. In addition, the use of multiple ﬁngerprint

databases has almost no positive inﬂuence on the

achieved results when using one transmission power

level.

We further demonstrated that the selection of a

special non-standard transmission power level has a

remarkable inﬂuence on the positioning accuracy and

that the merging of several independently computed

sub-estimates helps to increase the quality of the re-

sults signiﬁcantly. We presented a strategy for a good

selection of the number of supplied live samples as

well as the number of sub-estimates that leads to an

overall gain in accuracy and stability.

REFERENCES

Bahl, P. and Padmanabhan, V. N. (2000). RADAR: An In-

Building RF-Based User Location and Tracking Sys-

tem. In Proc. of the 19th IEEE InfoCom.

Chen, G. and Kotz, D. (2000). A Survey of Context-

Aware Mobile Computing Research. Technical Report

TR2000-381, Dartmouth College.

Haeberlen, A., Flannery, E., Ladd, A. M., Rudys, A., Wal-

lach, D. S., and Kavraki, L. E. (2004). Practical Ro-

bust Localization over Large-Scale 802.11 Wireless

Networks. In Proc. of the 10th ACM MobiCom.

Kaplan, E. and Hegarty, C., editors (2005). Understand-

ing GPS: Principles and Applications. Artech House

Incorporated, second edition.

King, T., Haenselmann, T., Kopf, S., and Effelsberg,

W. (2007). Overhearing the Wireless Interface for

802.11-based Positioning Systems. In Proc. of the 5th

IEEE PerCom.

King, T. and Kopf, S. (2007). Loclib - A Loca-

tion Library. Website: http://www.informatik.uni-

mannheim.de/pi4/lib/projects/loclib/.

King, T., Kopf, S., Haenselmann, T., Lubberger, C., and Ef-

felsberg, W. (2006). COMPASS: A Probabilistic In-

door Positioning System Based on 802.11 and Digital

Compasses. In Proc. of the 1st ACM WiNTECH.

Ni, L. M., Liu, Y., Lau, Y. C., and Patil, A. P. (2004).

LANDMARC: indoor location sensing using active

RFID. Wireless Networks, 10:701–710.

Priyantha, N. B., Chakraborty, A., and Balakrishnan, H.

(2000). The Cricket Location-Support System. In

Proc. of the 6th ACM MobiCom.

Rappaport, T. S. (2001). Wireless Communications: Princi-

ples and Practice. Prentice Hall PTR, second edition.

Want, R., Hopper, A., Falcao, V., and Gibbons, J. (1992).

The Active Badge Location System. ACM Transac-

tions on Information Systems, 10(1):91–102.

ON THE USE OF VARIOUS POWER LEVELS TO IMPROVE WIRELESS LAN-BASED POSITIONING - Using

Multiple Power Levels With Fingerprinting Algorithms

231