RELATIVE NODES LOCALIZATION IN WIRELESS NETWORKS

USING RECEIVED STRENGTH SIGNAL VARIATIONS

Mohamed Salah Bouassida and Mohamed Shawky

Heudiasyc UMR 6599 CNRS, Universit´e de Technologie de Compi`egne

Centre de Recherche B.P. 20529, 60205 Compi`egne Cedex, France

Keywords:

Wireless Networks, Localization, Strength Signal.

Abstract:

The geographical localization of entities in a wireless network is one of the most important issues of neigh-

borhood awareness. A precise localization provides an advantage for the geographically-located services.

However, the geographical localization within a wireless network should take into account the characteris-

tics and the speciﬁcities of such environment. In this paper, we present a technique allowing a receiver to

localize a sender within its range, without additional devices, as a GPS (Global Positioning System). We use

only 3 RSSIs (Received Strength Signal Indicators) measurements, under the assumption that the sender sends

messages with the same signal strength.

1 INTRODUCTION AND

MOTIVATIONS

Wireless networks allows to connect a set of nodes in

an efﬁcient and fast manner, using limited infrastruc-

ture support or even without any ﬁxed infrastructure

as in ad hoc networks. The development of wireless

networks is increasing, due to the emergence of new

technologies and standards (e.g. 802.11

1

, wimax

2

)

and the exponential deployment of autonomous and

advanced equipments and devices. Furthermore, the

deployment of user-oriented services within wireless

networks brought new issues and problems. One of

the most important is the geographical localization.

Geographical localization within wireless net-

works provides important information, which can

help in several applications:

• localization of users, clients or devices

• localization for eradication of radio interferences

sources,

• localization of access points in a network,

• tracking of the motion of an entity in the network

to facilitate the local guidance based applications

...

Geographical localization techniques should take

into account the characteristics of the wireless net-

1

http://grouper.ieee.org/groups/802/11

2

http://www.wimaxxed.com

works, such as mobility and dynamicity of nodes,

low capacities in term of computation, bandwidth, en-

ergy and memory. Thus, the most suitable solution

to deal with these requirements should not use ad-

ditional devices, implying expensive overheads. In

this context, we propose in this paper a relative lo-

calization technique within wireless networks, based

on the received strength signal variations, without any

knowledge about the environment (pre-established ra-

dio map). Our localization technique is dedicated to

operate within wireless networks, composed of small

number of nodes, even 2 nodes only, for low-speed

oriented applications (eg. walking-speed oriented ap-

plications).

To present our contributions, this paper is struc-

tured as follows. Section 2 presents related works

concerning geographical localization in wireless net-

works. In section 3, we describe our technique to

locate a non mobile transmitter within LoS environ-

ment. In Section 4, we show how to calibrate the

signal attenuation model within LoS environment, to

produce the most exact RSSI measurements Section

5 presents the typical applications integrating our lo-

calization technique: a tracking mechanism of nodes

within wireless network and a combined positioning

technique using identiﬁed beacon nodes. Section 6

presents analysis and results, and ﬁnally section 7

concludes this paper and presents our future work.

195

Salah Bouassida M. and Shawky M. (2008).

RELATIVE NODES LOCALIZATION IN WIRELESS NETWORKS USING RECEIVED STRENGTH SIGNAL VARIATIONS.

In Proceedings of the International Conference on Wireless Information Networks and Systems, pages 195-202

DOI: 10.5220/0002020601950202

Copyright

c

SciTePress

2 RELATED WORK

The GPS (Global Positioning System) (Hofmann-

Wellenhof et al., 1997; Stoleru et al., 2004) is the

most known localization technique. Each entity holds

a sensor which receives and process signals from a

satellite constellation, to deﬁne a 3-dimensional lo-

calization having an error margin evaluated to 10m

to 20m. This localization method is widely used by

mobile devices. However, it still expensive to deploy

within wireless networks, in addition to the low relia-

bility of the satellite signal reception indoors.

During the last years, were developed several

localization techniques in wireless networks. We

present in this section some of them, divided into two

approaches: range free and range based techniques

(He et al., 2003).

2.1 Range Free Localization Techniques

In these techniques also called topologicaltechniques,

no physical measurements are used. The localization

is only based on the data links established by the node

to situate, with its neighbors. Within these techniques,

reference nodes called beacons are chosen, having

self localization capabilities such as GPS. The mech-

anisms belonging to this approach are as follows:

• Centroid algorithm (Bulusu et al., 2000): a node

that needs to localize itself, computes an average

of the coordinates of the reference nodes that it

receives. The obtained localization may have a

large error margin.

Figure 1: DV-HOP localization technique.

• DV-HOP (Niculescu and Nath, 2001b) each node

estimates its position via the diffused coordinates

of the beacons nodes, the number of hops to reach

these nodes and the average size of one hop within

the network. This average size is estimated by

the beacon nodes and diffused within the net-

work. Figure 1 illustrates this localization tech-

nique. The node D to situate, is at 2 hops from

the beacon A (of size average

A

), 2 hops from the

beacon B (of size average

B

) and 3 hops from the

beacon C (of size average

C

).

The main disadvantageof this technique is that the

average size of one hop in the network could not

be determined precisely. To solve this problem,

another technique called Amorphus Positioning

(Nagpal et al., 2003) is deduced from DV-HOP,

while taking into account the density of nodes in

the network.

• PIT (He et al., 2003): each node within the

network evaluates its position according to the

formed triangles between the beacon nodes. Each

result allows to reﬁne the computed localization.

This technique can produce only estimations of

the node localization (cf. Figure 2).

Figure 2: PIT localization technique.

2.2 Range based Localization

Techniques

These techniques also called topographic techniques

are based on physical measurements data, carried out

for each wireless link established between the node

to localize and its environment. The mechanisms be-

longing to this approach are presented in the follow-

ing:

• Angle of Arrival (AOA): the localization of a node

is computed by a triangulation using the angles

of reception according to three beacon nodes (cf.

Figure 3). The APS (Ad-hoc positioning system)

(Niculescu and Nath, 2001a) technique uses the

AOA localization within wireless networks. APS

proposes a method for all nodes to determine their

orientation and position in an ad hoc network,

where a fraction of nodes have positioning capa-

bilities (GPS) and under the assumption that each

node has the AOA capability. These requirements

make APS restricted to a speciﬁc context of wire-

less ad hoc networks.

• Time of Arrival (TOA): the localization of nodes

is computed via the propagation times between

the concerned entity and the beacon nodes. Both

one-way propagation time and round trip time are

used. The Cricket (Priyantha et al., 2000) tech-

nique uses the TOA localization mechanism, in

addition to the combination of the RF and ul-

trasound hardware to enable a sensor, attached

to each node, to compute the distance to beacon

WINSYS 2008 - International Conference on Wireless Information Networks and Systems

196

Figure 3: AOA localization technique.

nodes as follows: a beacon node sends informa-

tion about the space over RF, together with an ul-

trasound pulse. A listener receives the RF mes-

sage and then the ultrasound pulse, which arrives

later due to the speed difference between RF and

ultrasound waves. The time difference between

the reception of the RF message and the ultra-

sound signal determines the distance to the bea-

con node.

• Time Difference of Arrival (TDOA): the local-

ization of nodes within the network is carried

out according to the relative moments of detec-

tion of a common event (such as ultra-sound mes-

sage reception). This technique supposes a syn-

chronization between the nodes, hardly applicable

within wireless networks, due to their lack of ﬁxed

infrastructure. The Pushpin technique (Broxton

et al., 2005) is a typical example of TDOA tech-

nique.

• Received Signal Strength Indicator (RSSI): dis-

tance between nodes is estimated according to an

attenuation model of the received signal strength

with distance. Path loss models represent the dif-

ference in dB in signal strength between trans-

mitter and receiver via RSSI measurements. The

most known model to evaluate the path loss is the

Friis Free Space Path Loss Model (described in

section 3).

The localizations techniques presented above al-

low a node within a wireless network to situate itself,

according to reference nodes in the network (range

free techniques), or to physical measurements car-

ried out between the node and its environment (range

based techniques). The majority of these approaches

needs additional conﬁgurations or equipments which

make them restricted to a speciﬁc wireless network

context.

To deal with this inconvenient, we elaborate a fast

and reliable localization technique based on the RSSI

measurements, to operate within small wireless net-

works, without the need of any additional device or

conﬁguration (cf. section 3).

3 RELATIVE LOCALIZATION OF

A NON MOBILE

TRANSMITTER WITH LOS

ENVIRONMENT

We propose in this section a technique allowing a

mobile receiver to localize a ﬁxed sender within its

range, with only 3 RSSI measurements, in LoS (Line

of Sight) environment and assuming that sent mes-

sages are with the same signal strength.

The path loss model we use to evaluate the dis-

tance between a sender and a receiver is the Friis Free

Space Path Loss Model, which represents the signal

attenuation when there is a clear line of sight between

the transmitter and the receiver. This model stipulates

that:

PLfs(d)[dB] = 20.log

10

(4πd/λ)

Where:

• λ is the wavelength of the propagation wave. λ

is evaluated as λ = c/ f, c is the light speed

(3.10

8

m/sec) and f is the frequency of the sig-

nal. For 802.11g (the dominating frequency is

f = 2.4Ghz), λ = 0.125m,

• d is the distance between the transmitter and the

receiver.

To generalize the previous equation with any dis-

tance d, we use the following path loss expression,

which integrates a received power reference point

(d0). We can choose d0 = 1m without loss of gen-

eralization:

PL(d)[dB] = 2.PLfs(d0)[dB] + 10.n.log

10

(d/d0)

Where n is the path loss exponent which repre-

sents the increase of path loss with increase in the

distance between the transmitter and the receiver. For

free space, n is equal to 2, but it would be better to

calibrate this parameter, depending on each network

characteristics.

Figure 4 illustrates a typical example where the

receiver needs to localize the sender by determining

the angle β between them. For that, the receiver starts

by evaluating the received strength from the sender,

at positions P

1

and P

2

. The distance between these

positions is L. Then, using the Friis path loss model,

the receiver can evaluate the distances d

1

and d

2

at

positions P

1

and P

2

.

We suppose that the distance x between the re-

ceiver and the sender is equal to the average between

the distances d

1

and d

2

, where d

1

,d

2

>> L.

x =

d

1

+ d

2

2

RELATIVE NODES LOCALIZATION IN WIRELESS NETWORKS USING RECEIVED STRENGTH SIGNAL

VARIATIONS

197

Figure 4: Evaluation of the angle between the sender and

the receiver.

The followingrelations allow computingthe angle

β:

d

2

1

= (x+ L/2.sinΘ)

2

+ (L/2.cosΘ)

2

d

2

2

= (x− L/2.sinΘ)

2

+ (L/2.cosΘ)

2

⇒ d

2

1

− d

2

2

= 2.L.x.sinΘ

⇒ sinΘ = (d

2

1

− d

2

2

)/2Lx

⇒ β = arccos((d

2

− d

1

)/L)

Let the coordinates of the receiver at the posi-

tion P

0

be (x

0

,y

0

), and the coordinates of the sender

(x

s

,y

s

). Because of cos(x) = cos(−x), two localiza-

tions of the sender are possible, verifying the equation

β = arccos((d2 − d1)/L). From Figure 5, we show

that:

x

s

= x

0

+ d.sinβ or x

s

= x

0

− d.sinβ

y

s

= y

0

+ d.cosβ or y

s

= y

0

+ d.cosβ

Figure 5: Localization of the sender using the angle β.

To be able to decide which position to choose for

the sender, the receiver can measure the received sig-

nal strength from the sender, in the direction of one

of the two localizations. Depending on the increase

or the decrease of the received signal strength, the

receiver decides which localization to choose for the

sender. In ﬁgure 5, if the received signal strength in-

creases at position P

test

, the sender is at the localiza-

tion 1; otherwise, it is at the position 2.

3.1 Advantages of Our Localization

Technique

The main advantages of our localization technique are

the following:

• No additional equipment has to be added to the

wireless nodes to situate. Our localization tech-

nique uses only the history of received signal

strength, to deliver a reliable and fast localization

estimation.

• The node which wants to localize itself can move

within the network and does not need to be ﬁxed,

as others localization techniques based for exam-

ple on triangulation mechanisms.

• The higher is the number of measurements of the

received signals strength, the more is the localiza-

tion precision. Indeed, the measurements of RSSI

can calibrate the path loss attenuation model used

to compute the distance between the sender and

the receiver (cf. section 4).

• Our localization technique can be integrated to

other advanced mechanisms. We present in Sec-

tion 5 a tracking technique of a node within wire-

less networks and a combined positioning tech-

nique using identiﬁed beacon nodes.

4 CALIBRATION OF THE

EXPONENT LOSS FACTOR

WITHIN LoS ENVIRONMENT

To avoid errors on the RSSI measurements, we should

calibrate the exponent loss factor n used in the Friis

Loss equation presented in section 3. With d0 = 1,

we have PL(d)[dB] = 80 + 10n.log

10

(d). Thus, the

distance d and the loss factor n are computed as fol-

lows:

d = 10

(PL(d)[dB]−80)/10n

n = (PL(d)[dB] − 80)/10.log

10

(d)

To calibrate n, we use a second formulation of the

Friis Model, which stipulates that: P

r

/P

t

= (λ/4πd)

2

;

where P

r

is the received signal strength and P

t

is

the transmitted signal strength. For two successive

received signals from a transmitter, we show that

P

r1

/P

r2

= (d

2

/d

1

)

2

. We thus have:

d

2

= d

1

.

p

P

r1

/P

r2

Our calibration algorithm, illustrated in Figure 6,

consists of computing the distance between a trans-

mitter and a receiver as the average between the two

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198

Figure 6: Calibration of the path loss factor.

values produced by using the two formulation of the

Friis Loss equation. The exponent loss factor n is

then computed as a function of the computed aver-

age distance. We add to our calibration algorithm a

trust level, which represents the number of calibra-

tions carried out by a node in the network.

5 TYPICAL APPLICATIONS OF

OUR POSITIONING

TECHNIQUE

5.1 Tracking of a Mobile Node Within

Wireless Networks

Our objective is to allow a mobile node A to track

another mobile node B within the network, by only

using RSSI measurements (cf. Figure 7).

Figure 7: Tracking of a node in the network.

To compute the angle between two mobile nodes,

we suppose that the ﬂow of data sent by a node to an

other is sufﬁciently high to consider that the motion

of the two nodes can be divided into small sequential

segments (each of the two nodes moves in its turn).

We can thus use the localization technique described

in section 3 to compute the angles α and β between

the two nodes A and B.

Nodes A and B send periodically and mutually the

angles α and β between them, as is illustrated in Fig-

ure 7, computed via our localization technique pre-

sented above. To track node B, the node A should

deviate its direction by π− α− β.

We are currently working on a two-nodes encoun-

tering application within a wireless network. This

application consists of bringing together two mobile

nodes, which periodically send to each other their lo-

calization information. Each node compute the angle

between its direction and the direction of the other

node and deviates its direction in order to encounter

the other node, while decreasing the distance between

them.

5.2 Combined Localization using

Identiﬁed Beacon Nodes

We present in this section a technique allowing a

node to identify its trajectory within a wireless net-

work, where beacon nodes are chosen having local-

ization capability by GPS (cf. Figure 8). The bea-

con nodes sends periodically localization messages to

their neighbors in one hop (TTL=1). Each localiza-

tion message contains the coordinates of the beacon

node and the time of transmission.

Figure 8: Identiﬁcation of the trajectory of a node in the

network.

The mobile node, moving within the wireless net-

work, receives the localization messages sent by the

beacon nodes allowing it to identify its trajectory ac-

cording to the following algorithm:

while ()

Receive Localization-Message from beacon node i

if Receive 3 Localization-Messages from the

same node i

Compute the localization according to the beacon

node i

Store the localization within a positions history

RELATIVE NODES LOCALIZATION IN WIRELESS NETWORKS USING RECEIVED STRENGTH SIGNAL

VARIATIONS

199

end if

if Trajectory Identiﬁcation

Linearization of the node trajectory using

the positions history

end if

end while

To validate the applicability of our main contribu-

tions presented above, we present in the next section

analysis and simulations we have done to calibrate

the parameter L of our localization technique, simu-

late our calibration algorithm and ﬁnally evaluate the

localization error margin depending on the different

parameters of our approach.

6 ANALYSIS AND SIMULATIONS

6.1 Analytical Results

In the new localization technique presented in section

3, the parameter L should be well chosen to improve

the reliability and the exactness of the results. Figure

9 (L = 10) shows the angle β, computed as a function

of the difference between the two distances d

1

and d

2

.

We show for example that when d

1

= d

2

, the sender

is at 90

o

from the receiver.

Figure 9: Angle β by (d2-d1) with LoS environment and

L=10.

The choice of L is important to have a fast and

reliable measurement of the angle β. Indeed, a large

L compared to d

1

and d

2

, allows more opportunities

to measure exactly the angle β (the interval [−L,L]

is large). On the other hand, measuring the received

signal with a small L is faster and easier.

From Figure 9, we show that to enhance the ex-

actness of our localization technique, we have to en-

sure the following inequality: |d2 − d1| < L. Let’s

T

measure

deﬁne the time measurement period (the time

between two RSSI measurements) ; and V is the max-

imum speed of the receiver. The distances differ-

ence (|d2 − d1|) should thus be limited to V.T

measure

.

Hence, the parameter L should be chosen as follows:

L > V.T

measure

For example, for T

measure

= 1sec and for V = 10km/h

(average walking speed), L should be equal to 3m.

Figure 10: Calibration of the n loss factor.

In a second step of our analysis, our objective is

to verify our calibration mechanism of the n loss fac-

tor n, presented in section 4. We choose a simulation

example, in which we start initially by n = 2, the re-

ceived signal strength Pr = 150w and the signal loss

PL = 70w. We calibrate the n loss factor 50 times

according to the algorithm presented in Figure 6. At

each calibration, we add a random value (between 0

and 1) to the value of PL. The result of our veriﬁca-

tion example is presented in Figure 10. We show in

this Figure how the exponent loss factor can be ad-

justed depending on the RSSI measurements.

6.2 Simulation Results

In this section, we use the network simulator NS2

3

to simulate our localization mechanism, described in

section 3.

Our simulation parameters under NS2 are as fol-

low:

• channel type: wireless,

• propagation model: Free Space,

• MAC protocol: 802.11,

• antenna model: omni-directional,

• number of nodes: 2,

3

http://www.isi.edu/nsnam/ns/

WINSYS 2008 - International Conference on Wireless Information Networks and Systems

200

Figure 11: Localization Error by L (d = 180.27m).

• trafﬁc: node 1 sends a CBR trafﬁc to node 0,

which receives these packets, evaluate their RSSIs

and compute the distance to reach node 1.

We carried out simulations to evaluate the local-

ization error of our approach. In a ﬁrst step, we eval-

uate the localization error according to the distance L

between the two positions P

1

and P

2

, the distance d is

ﬁxed to 180.27 (cf. Figure 11). Then, we evaluate the

localization error according to the distance d, while

ﬁxing the L parameter (L = 3m) (cf. Figure 12).

We show in Figure 11 that the localization error

is strongly dependant on the parameter L. The small-

est the parameter L is, the smallest is the localization

error. For L = 3m, the localization error is equal to

0.0043m. However, we cannot decrease indeﬁnitely

the parameter L in order not to distort the RSSI mea-

surements.

Figure 12: Localization error by the distance d (L = 3m).

Figure 12 shows that the localization error is de-

pendant on the distance between the sender and the

receiver, with a ﬁxed L. We conclude that to have

a small localization error, the parameter L should be

much smaller then the distance d. In addition, we

have used this hypothesis to compute the angle β be-

tween the sender and the receiver (cf. Section 3).

7 CONCLUSIONS AND FUTURE

WORK

We presented in this paper a new localization tech-

nique, based only on the RSSI measurements. This

technique allows a mobile node to compute the po-

sition of a ﬁxed node in the network, by evaluating

the variation of the received signal strength of three

messages sent by this node. In a second step, we pre-

sented a calibration mechanism of the Friis attenua-

tion model within LoS environment. This mechanism

consists of calibrating the exponent loss factor n at

each achieved RSSI measurement. We deduced from

our positioning technique a tracking mechanism and a

combined localization technique using identiﬁed bea-

con nodes.

To validate our contributions, we carried out anal-

ysis and simulations to calibrate the different parame-

ters of our localization technique. We showed that the

choice of the parameter L is very important to mini-

mize the computed localization error.

The establishment of secure communications

within wireless networks remain a key issue be-

cause of the characteristics and the vulnerabilities of

such environment (Bouassida, 2006; Bouassida et al.,

2006). In this context, other research works in our

team are dealing with the assessment of security of

messages using signal characteristics. We envisage

to adapt our technique in order to obtain a ”distin-

guishability” degree between two nodes by analyzing

strength variations. This will contributeto detect sybil

nodes created by a malicious one.

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