Tunable Transmission Power to Improve 2D RSSI Based Localization

Algorithm

D. Polese, L. Pazzini, A. Minotti, L. Maiolo and A. Pecora

IMM-CNR, Via del Fosso del Cavaliere 100, 00133 Roma, Italy

Keywords: Indoor Localizazion Algorithm, RSSI, Wireless Sensor Network.

Abstract: Radio frequency wireless technology is surely one of the most used technologies in indoor localization. RF-

signals have been utilized in several ways to estimate the distances among the anchor nodes and the mobile

nodes and, probably the methods based on the measure of the Received Signal Strength (RSS) are the most

explored ones. RSS depends on the transmission medium and environment and this affects also the distance

measurement performances. To mitigate the external influences, transmission parameters, as for example

the transmission channel and transmission power, can be tuned. To this purpose, in this work the influence

of the power transmission on the localization algorithm performance is investigated. In particular a method

to select the power transmission that allows the best localization performance is presented. The results show

that the localization performance depend on the transmission power. Moreover, a method to establish the

best power transmission for the specific environment is presented and tested.

1 INTRODUCTION

Nowadays, localization of objects or humans in

indoor environment is gaining growing interest. It is

used in a huge number of applications, from the

indoor navigation to the logistic, up to the

environmental monitoring (Fu et al., 2009,

Mainwaring et al. 2002, Vicentini et al. 2014, García-

Hernández et al. 2007). These kinds of applications

are generally based on radio frequency (RF) wireless

technologies, even if examples of applications based

on infrared and ultrasound technologies have been

also developed (Randell and Muller, 2001). The RF

technologies have the main advantages of a wider

range of use compared to other technologies, and

moreover, a direct line of sight between anchors

nodes and mobile node is not required.

Although many algorithms for the evaluation of

the mobile nodes position have been introduced, they

can be roughly classified in angulation and lateration

algorithms. The former uses the angle of arrival of

the signals measured from the anchor respect to the

same reference (typically the magnetic North) while

the latter uses the distances from the anchor nodes.

Due to simpler hardware that is required to evaluate

the distance, the lateration algorithms are largely the

most utilized. Whichever positioning algorithm is

implemented, several methods can be used to

estimate the anchor to mobile node distances: Time

of Arrival (TOA), Time Difference of Arrival

(TDOA) and Received Signal Strength (RSS).

Among them, mainly due to its simplicity of

implementation, the measurement of the RSS has

been extensively investigated.

The principal advantage of the measurements of

the RSS to estimate the distances between two

antennas is due to the integration of this

measurement in the more recent RF transceivers.

Indeed, the measurement of RSS is defined and

sometimes mandatory required and standardized by

the last communication protocols (IEEE 802.11-

2012, IEEE 802.15.4f-2012). Moreover, another

reason relies in the simple relation that connects

Received Signal Strength Indicator (RSSI) and

distance:

RSSI  10∙η∙log



dA

(1)

where



is the signal propagation constant, d is the

distance between sender and receiver and A is the

RSSI at a distance of one meter. The two parameters



and A depend on the medium and on

environmental factors. Therefore, several error

factors can affect the RSSI estimation as multipath,

presence of barriers between source and receiving

antennas, angle among them, environmental

electromagnetic interferences and interferences of

151

Polese D., Pazzini L., Minotti A., Maiolo L. and Pecora A..

Tunable Transmission Power to Improve 2D RSSI Based Localization Algorithm.

DOI: 10.5220/0005330801510156

In Proceedings of the 4th International Conference on Sensor Networks (SENSORNETS-2015), pages 151-156

ISBN: 978-989-758-086-4

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

other transmitting systems.

In order to limit these problems some

transmission parameters can be modified or some

misbehavior can be compensated (Polese et al., 2014,

Wu et al. 2012). For example the transmission

frequency and power are two easily accessible and

tunable parameters that could improve the

localization performance.

In transmission systems, the choice of the

transmission frequency depends on the analysis of

the quality of the transmission channel. A largely

used parameter to estimate the better transmission

frequencies in presence of noise and/or interference

is the Signal to Interference plus Noise Ratio (SINR)

(Jeske and Ashwin, 2004, Shin et al, 2007). An

estimation of this parameter allows to choose the best

working frequency. On the other hand, the

transmission power is generally chosen considering

only the power consumption or the maximum

transmission distance. However, more often,

transmission power and frequency are not selected

with any regards to the goodness of the distance

measurements.

In this paper, we take into consideration the effect

of the power transmission on the correct distance

estimation. In particular, a method to estimate the

best power transmission will be presented and a

classical lateration algorithm will be used to compare

the localization performances adopting different

transmission powers to show the goodness of the

method.

The remainder of this paper is organized as

follows: in section 2 the method for the estimation of

the best transmission power is presented. In section 3

the localization algorithm is explained. In section 4

the experimental set-up is described. In section 5 the

results are shown and finally, section 6 concludes the

paper.

2 HOW TO ESTIMATE THE

BEST POWER TRANSMISSION

The signal between the transmitting and the receiving

antenna can follow different paths in addition to the

direct line of sight. Thus, the power of the signal

detected by an antenna is a weighted sum of the

power of the signals coming from the different

directions. In indoor application, the two parts, the

power carried by the reflected signals and the direct

signal, can be comparable. This effect, in addition to

the limit of detection of the transceivers can produce

different profiles of power decay inside the close

environment that are highlighted by .

To perform a better estimation, the sensibility

(D'Amico and Di Natale, 2001) of the RSSI on the

distance should be maximized inside the working

area. Practically, a greater sensibility allows to detect

smaller variation of the antennas distance.

The sensibility can be easily calculated using

equation 1:

S

∂RSSI

∂d



ln10

∙η∙

(2)

and it is maximized for the power transmission that

maximizes .



However, even if each anchor independently

performs the RSSI measurements and, in this way,

also the estimation of the distance from the mobile

node, the localization algorithm uses the whole set of

distances to evaluate the position of the mobile node,

so, the performances of the localization algorithm

could be invalidate by the less efficient antenna. To

take into account the behavior of the whole system

and to estimate the best transmission power, a single

parameter that considers the magnitude of the



and

its variability among the different anchors has been

chosen:

N

μη



∙





RSSI









η



∙





RSSI







(3)

where 



∙



represents the mean operation among the

anchors, 



 and 



are the signal propagation

constants of the i

anchor, whereas, 



∙



represents

the standard deviation operation among the anchors

and finally, 



is the minimum RSSI detectable

by the transceiver. The transmission power used by

the mobile node to communicate on the network that

maximizes N should also improve the performance

of the localization algorithm.

3 LOCALIZATION ALGORITHM

To investigate the influence of the transmission

power on the localization performance a classical

localization algorithm based on lateration is used to

compare the localization performances as function

of the mobile node transmission power.

Localization algorithm uses an optimization

procedure to seek the coordinates that minimize the

error between the distance measured using a signal

characteristic, in our case the RSSI (R

), and the

Euclidian distances calculated with the estimated

mobile node coordinates (D

) (Zanca et al. 2008):

SENSORNETS2015-4thInternationalConferenceonSensorNetworks

152



min



 wi





R







∈

(4)

It is important to note that the coordinates of the

anchor nodes are known. In this case the weight w(i)

are chosen quantized inversely proportional to the

distance experimentally measured through the

equation 1, i.e. each weight is taken in the set























 as they are ordered by distances R

In this way, each weight contributes exactly the half

of the previous and the double of the next one in the

Equation 4. The result of this approach is that nearer

anchors contribute more in the position estimation.

4 EXPERIMENTA SET-UP

A standard office room, furnished with classical

furniture as desks, cabinets and work bench is

arranged with a wireless sensor network composed of

5 nodes. Inside the working space several testing

points, at well-known position, have been installed.

In figure 1 a schematic representation of the room is

shown, it is important to note the mobile node placed

on a tripod and the anchor nodes placed on the room

walls.

Figure 1: Schematic representation of the test room

arrangement. It is possible to note the mobile node on a

tripod and the anchors node attached on the walls

highlighted by the circles.

The network is composed of commercial wireless

sensor nodes Z1 Zolertia (Zolertia, 2013) equipped

with an external pigtail antenna (see figure 2). In this

experiment, only one mote acts as a mobile node,

whereas the others are used as anchor nodes. The

anchor nodes are disposed along the walls.

During the experiment, the mobile mote has been

put in six different testing points and the RSSI values

of the mobile node signal is measured by the

different anchor nodes. The gateway node collects

100 RSSI values for each one of the 8 transmission

power levels available on the CC2420 transceiver.

The RSSI values are measured by the anchor node

according to transceiver specifications. The whole set

of RSSI data is sent to the PC through a USB

connection.

Figure 2: The two wireless sensor nodes equipped with

external pigtail antenna.

5 RESULTS

5.1

Calibration

Equation 1 connects distance and RSSI

measurements using two parameters that have to be

evaluated in the working environment, namely A and



The parameter A is easily estimated using its

definition i.e. the received power when the antennas

are placed at 1 meter of distance. Therefore, to

estimate the diverse A parameters the mean values of

100 RSSI packets measured placing the mobile node

at 1 m of distance from each anchor are used.

To estimate



the mobile node is placed at

different known positions and 100 RSSI packets are

measured by the anchor nodes for each position.

Using the equation 1, the value of  that best fits the

experimental data is implemented in the following

localization algorithm. The fitting has been

performed in MATLAB environment.

5.2

Test Points

The mobile node is placed in six different positions

inside the working area. For each position and for

each power level the RSSI is measured by the anchor

TunableTransmissionPowertoImprove2DRSSIBasedLocalizationAlgorithm

153

Figure 3: Variation of the parameter N for different power

levels.

nodes and stored. Five of the six data sets are used to

estimate the parameter  and the sixth position is

used to validate the result. This kind of approach is

called Leave One Out Cross Validation (LOO-CV)

and it has the main advantage of allowing an

estimation of the prediction error when the dataset is

not very large (Hastie, 2009).

Figure 3 shows the values of the parameter N for

the different eight power levels provided by the

CC2420 transceiver. Since for each power level six

possible calibration subsets can be used, the standard

deviation showed in figure 3 takes into account the

variation of the parameter N along the subsets.

Finally, it is important to note that the maximum of

the parameter N is obtained for the 6th power level.

5.3 Position Estimation

Following a LOO-CV approach, the position of each

testing point is evaluated using the previously

described localization algorithm adopting the

parameters A and



estimated during the calibration

procedure. Figure 4 shows the points classified using

the algorithm when the transmission power is

changed. The circles show an area of 0.5 m around

the correct testing point where the algorithm should

classify the point. It is possible to note that the

performance of positioning changes accordingly with

the chosen transmission power.

To evaluate the different performances of

positioning as function of the transmission power, the

mean error between the estimated position and the

real position is reported. In the figure 5, the mean

error and its standard deviation calculated on the 100

measurements performed for each position, are

shown. It is possible to observe that the positioning

error has different behaviors depending on the test

positions. In particular, position 2 and 5, that are in

opposite corners of the testing area, have a better

positioning performance with an error around 0.5 m

or even less for position 2. However, the figure

shows that for each position there is a power

transmission that minimizes the mean error.

Figure 6 summarizes the results of figure 4

showing the Root Mean Square Error Cross

Validation (RMSECV) for the different transmission

powers. The profile of the RMSECV shows that the

Figure 4: In the figure the point estimated using the different transmission powers are shown.

SENSORNETS2015-4thInternationalConferenceonSensorNetworks

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transmission power PW level 6 provides the better

positioning results with a RMSECV error less than

0.5 m.

Figure 5: Mean positioning error and its standard deviation

calculated on 100 measurement performed for each

position are shown as function of the different power

levels.

Figure 6: RMSECV for the different power levels.

Comparing the results of figure 6 with the results

of figure 3 it is possible to note that the parameter N

was maximized by the 6

power level, as expected.

6 CONCLUSIONS

In this work the possibility to improve the indoor

localization by selecting the most suitable

transmission power has been investigated. In

particular, a simple calibration method that takes

into account also the best transmission power related

to the specific indoor environment has been

presented. The final results have shown that the

mean error in the localization decreases almost three

times respect to the worst power selection.

ACKNOWLEDGMENTS

This research was partially supported by the Flagship

Project "Factory of the Future" FACTOTHUMS of

the National Research Council.

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