Improvement of Localization Results in WirelessNetworks Using Estimation of Distances BetweenUnknown Nodes: Simulation and Real Testbed

Evaluation

Oleksandr Artemenko, Andreas Mitschele-ThielFaculty of Computer Science and Automation

Ilmenau University of Technology

98693 Ilmenau, Germany

Email: {Oleksandr.Artemenko, Mitsch}@fh-erfurt.de

Gunar SchorchtApplied Computer Science department

Erfurt University of Applied Sciences

Postfach 45 01 55, 99051 Erfurt, Germany

Email: Schorcht@fh-erfurt.de

Abstract—In this paper, several novel algorithms for improvingthe localization results of unknown wireless nodes are presented.All introduced algorithms extend the concept of Universal Im-provement Scheme (UnIS) presented in our previous publications[1], [2]. We describe the algorithms using mathematical modelsand show with simulation results the effectiveness of new solu-tions in comparison to previous approaches.Additionally, we prove the simulations with real testbed evalu-

ation results obtained with a wireless sensor network consistingof 3 prepositioned reference nodes (anchors) and 10 unknownnodes (mobiles). The introduced testbed was placed indoor andwas build using ZEBRA2411 modules from senTec ElektronikGmbH, based on the chipset ZRP1 developed by FreescaleSemiconductor [3].

I. INTRODUCTION

A. Motivation

The constantly increasing mobility of our world defines

strict requirements on development of new hardware and soft-

ware. Wireless communications have already become a very

important part of our everyday life. Plenty of new applications

and new challenges emerge daily. Simple, cheap and accurate

localization is still one of these challenges to provide, f.i.,

monitoring, tracking and navigation capabilities.

Much scientific research has been conducted in the area

of localization techniques. On the one hand, there are some

schemes that provide very accurate results and can meet

demands of many applications. Some significant examples

here are Ultra-wideband [4], inertial navigation systems [5],

ultrasound [6], iGPS [7], etc. Common drawback of such

approaches is the fact that the proposed schemes are very

expansive and present in most cases very proprietary solutions.

On the other hand, there are plenty of algorithms that do not re-

quire much costs but provide only a rough location estimation

(f.i., algorithms based on Received Signal Strength Indicator

(RSSI) [8], Link Quality Indicator (LQI) [9]). The main idea

of this paper is to improve the results of cheap localization

solutions without need of further hardware changes.

One possible solution to enhance the accuracy can be

represented by filters (f.i., Kalman Filter, LaSLAT (Bayesian

filter), Particle Filter, Map-filtering, Rao-Blackwellized parti-

cle filter, Gaussian Mixture Filter, etc.). The common problem

of filters is that they need lots of continuously measured

data sequences and appropriate results are given only after

some time of operation (e.g., LaSLAT: 60 seconds of track-

ing [10]). In some situations, faster start-up time with only few

available data is required (e.g., disaster scenarios). According

to the requirements mentioned above, alternative systems or

additional algorithms are essential for some ad-hoc network

environments.

Within the scope of this work, new possibilities to improve

the localization results are developed, implemented, simulated

and evaluated on the real testbed. The introduced algorithms

use additional information known a priori about the network.

This information is represented by distances known before the

experiment between pairs of mobile nodes (aka mobiles or

unknown nodes) being localized. Alternatively, mobile nodes

can estimate the distances between each other themselves

before localization process starts. Due to better conditions

in communication between mobiles (i.e., line-of-sight, short

ranges, less signal attenuation and disturbance), this estimation

is more accurate and can be used to refine the individual

localization results. In this paper, there is an error of 0.05 cm

in manually measured distances, however these are considered

to be negligible if compared with the localization error. So,

we work with a simplified situation where the distances are

estimated a priori with no error. Additionally, the proposed

algorithms can be combined with any further refinement

schemes (e.g., filtering).

B. Paper Organization

The remainder of this paper is organized as follows. In

section II, we briefly describe the basic Universal Improvement

Scheme (UnIS) and two algorithms derived from UnIS as

a part of our previous work. Section III introduces new

2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC)

978-1-4673-2569-1/12/$31.00 ©2012 IEEE 693

algorithms and their mathematical models. Thereafter, section

IV shows the simulation setup and corresponding results.

Section V describes the developed testbed, received results and

analysis of them. Finally, Section VI concludes this paper.

II. UNIS

A. Background

As mentioned above, all algorithms introduced in this paper

use additional information about the mobile nodes. This ad-

ditional information is represented by distances known before

the localization.

From [1], a practical application example of this idea

could be the known distances between nodes attached to an

object being monitored or tracked (e.g., several wireless nodes

attached to the automated robot that is used in some specific

industrial process and needs to be localized). If the distances

between pairs of these nodes are static they can be measured a

priori and can be used in the improvement process to enhance

the accuracy of localization results of each node.

Another example is represented by mobile nodes in some

wireless networks (f.i., Wi-Fi). Before localization process

starts, the mobiles estimate the distances between each other.

Due to better conditions in communication between mobiles

(i.e., line-of-sight, short ranges, less signal attenuation and

disturbance), this estimation is more accurate and can be used

to refine the location estimation.

B. Previous Work

During the first simulations [11] with three reference nodes

and two mobiles, a stable improvement of localization results

using the known distance between mobiles was detected. In

order to extend the idea on indefinite number of nodes, the

Pairwise Universal Improvement Scheme (P-UnIS) in [1] was

proposed and the simulation results for different number of

unknowns (from two to ten mobiles) were presented using

Received Signal Strength based localization method (RSS).

The basic step in the improvement was represented by pair-

wise recalculations of position coordinates using the known

distances between pairs of mobile nodes (e.g., reference dis-

tance d12 between nodes with real coordinates h1 and h2 in

Fig. 1a). After first two steps of general localization (distance

estimation and calculation of coordinates using e.g. trilater-

ation, MIN-MAX, etc.), virtual coordinates of each mobile

node are available (h1, h2, h3 in Fig. 1b) and the refinement

process begins. Regarding the improvement of virtual position

h1, following steps will be done:

• According to the pair h1,h2, we move h1 along the

improvement vector �υ12 so that

‖(h1 + �υ12)− (h2 − �υ12)‖ = d12.• Thereafter, the improvement vector �υ13 for the pair h1,h3

will be calculated in the same way as �υ12 and will be

applied as the next improvement step.

In Fig. 1b, vector �υ12 represents the movement of coor-

dinates h1 and h2 until the distance between them is equal

to the reference distance d12. In such a way, the P-UnIS

algorithm uses pairwise computations to refine the estimated

coordinates of mobile nodes. The obtained simulation results

in [1] were analyzed with the main aim of exploring how

the localization precision is affected by amount of a priori

known information between pairs of nodes. The analysis has

shown that the improvement rate of the proposed refinement

scheme yields better localization results reaching the average

improvement ratio of 4.0 in case of using more than four

mobile nodes with known distances between them.

During the further investigations [2], groupwise-based com-

putations were applied instead of pairwise one to improve

location estimation. The minor difference of the Groupwise

Universal Improvement Scheme (G-UnIS) from P-UnIS is

represented by the summarization of all the corresponding to

the node h1 refinement vectors in one movement vector �s1(Fig. 1b):

�si =N∑

j=1,j �=i

�υi,j . (1)

The new position of the node i according to G-UnIS is

defined then as:

hi[n+ 1] = hi[n] + μ

N∑

j=1,j �=i

‖hj − hi‖ − di,j

2‖hj − hi‖(hj − hi),

(2)

where μ is a parameter, that represents the step size of the

improvement process and can vary depending on accuracy of

the distance estimation.

Extending the idea of above described algorithms, we

present in this paper their enhancements as well as analytical

and experimental results.

III. NEW ALGORITHMS

From the analysis of P-UnIS and G-UnIS algorithms, sev-

eral questions emerged:

• What is the difference in impact on the accuracy of the

sequential or parallel refinement of mobiles?

• If there are more than two mobile nodes available for

refinement, what mobile must be selected to start with?

Random or deliberate selection? What order of selection

produces the best improvement?

According to the above problem statements, we have ex-

tended the basic UnIS algorithms and its modifications (P-

UnIS and G-UnIS) with following new features.

A. Sequential or Parallel Refinement

There are fundamentally two possibilities in the improve-

ment of nodes’ positions. On the one hand, the coordinates

of all mobile nodes can be improved in one refinement step

simultaneously. Let Ni represent all neighbors of node i. In

the case of parallel refinement, for the calculation of the new

n+ 1 position of the ith node the coordinates of nodes from

the previous time period n will be taken:

694

(0;5)A

3h 2h23d

5m1h

12d13d

23

1

(5;0)(0;0)B C

5m( ; )

(a) Real placement of anchors (A, B, C) and mobiles (h1, h2, h3)

(0;5)A

2h3h 2h23d

5m 3h1h

12d13d

23

12d

12��

13��

1s�

h

1

(5;0)(0;0)B C

1h

5m( ; )

(b) Refinement process for estimated position h1

Fig. 1. Improvement example for network with three anchors and three mobiles [2]

Pairwise Sequential Selective Ascending

Descend.RandomParallel

UnISGroupwise Sequential Selective Ascending

Descend.RandomParallel

Fig. 2. Classification of UnIS extensions

hi[n+ 1] = hi[hi[n]; hj [n], j ∈ Ni]. (3)

In opposite to the parallel improvement, the sequential

refinement uses for the calculation of the new n+ 1 position

of the ith node positions of neighbor nodes that can be already

improved and refer to the time period n+ 1:

hi[n+ 1] = hi[hi[n]; hj [n], j ∈ Ni; hj [n+ 1], j ∈ Ni]. (4)

B. Ascending or Descending Selection Order

With the aim to investigate the impact of different order

selection strategies, we have chosen two classical approaches:

ascending and descending selection order. As a criterion, the

averaged distance error between ith node and its neighbors

has been used according to the known distances:

1

|Ni|∑

j∈Ni

|‖hj − hi‖ − di,j |, (5)

where di,j is a reference distance between nodes i and j.According to the features described above, Fig. 2 depicts the

classification of the newly derived and previous algorithms.

IV. SIMULATION

In this section, we describe the simulation and its results

obtained running all presented above refinement algorithms.

The main aim of this analysis is to explore the effectiveness

of new approaches as well as their localization improvement

affected by the number of refinement rounds.

A. Simulation Setup

To enable a fair comparison of P-UnIS and G-UnIS schemes

with the simulation results of new algorithms, we used the

simulation platform and the simulation setup from our pre-

vious work [2]. All presented algorithms were implemented

according to their mathematical description ( (3), (4) and (5))

introduced in the previous section. For the distance estimation

process we have applied our radio channel model from the

previous work (see [1] for more details), where we consider a

simple path loss channel model, in which the generic ith node,

placed at distance di from the transmitter, receives a signal

with power Pi. This power is converted into RSS parameters

by the circuitry on a wireless node. The simulated order

of RSS values were randomly selected from the empirically

obtained RSS database only once and were used for all

simulations to enable the comparison of different algorithms.

Because G-UnIS represents an iterative process, in all our

simulations we tried to go over various numbers of iteration

rounds.

The implemented topology includes three reference nodes

(A, B, C) with 5 m spacing (Fig. 1) and ten mobile nodes

positioned in the middle of a 5 m by 5 m environment with

1 m spacing. To obtain explicit results, full information about

the distances in meters between all mobile nodes was provided.

For determination of the mobile nodes coordinates in 2d, the

trilateration method was used. This method refers to finding

695

0,6

1,2e

loca

lizat

ion

erro

r (m

) UnimprovedP-UnIS Sequential RandomP-UnIS Sequential Selective-ASCP-UnIS Sequential Selective-DESCG-UnIS ParallelG-UnIS Sequential RandomG-UnIS Sequential Selective-ASCG U IS S ti l S l ti DESC

0,30 20 40 60 80 100

aver

age

number of refinement rounds

G-UnIS Sequential Selective-DESC

Fig. 3. Simulation results of different UnIS modifications

the intersection point of (at least three) circles around the

references. For all the simulated schemes, the constant step

size of μ = 0.2 was applied. This step size was empirically

proved in [2] to be efficient in the given scenario.

B. Simulation Results and Their Analysis

Fig. 3 provides the average localization error as a function

of the number of refinement rounds according to the simula-

tion results of different UnIS modifications. As expected, all

algorithms tend to reduce the localization error: the higher

the number of refinement rounds the better the improvement

ratio. Best results are represented by the ”P-UnIS Sequential

Selective in Descending order” scheme. From our previous

work, we learned that P-UnIS has no stable state in opposite

to G-UnIS and continues converging during the whole im-

provement time. This can theoretically cause sometimes the

situation when the nodes diverge and the average localization

error increases. However, during our simulations using men-

tioned above step size, this never happened. Additionally, the

improvement ratio at the beginning is significantly higher as

seen in Fig. 3. Therefore, the descending order of selection

with the refinement of nodes with bigger distance errors first

results in better improvement ratio.

V. EXPERIMENT

To prove the simulation results, we evaluate the proposed

refinement scheme and its modifications on the real testbed

platform based on the Wireless Sensor Network (WSN).

A. Testbed

The implemented network consists of 3 prepositioned an-

chor nodes with known coordinates ([0; 0], [0; 5] and [5; 0])and 10 unknown nodes. The nodes were deployed indoor

in a university office room with standard furniture including

chairs, bookshelves, desks and two windows. The office room

used for the experiment represents the dynamic measurement

environment where people may come into and go out of

the room during the normal operation of the network, thus

modifying the characteristics of the actual radio propagation

channel.

0,8

1

1,2

1,4

lizat

ion

erro

r (m

)

Simulation: unimprovedSimulation: improved

0,2

0,4

0,6

0 20 40 60 80 100

aver

age

loca

l

number of refinement rounds

Real test: unimprovedReal test: improved

Fig. 4. Comparison of simulated and real testbed results

All the mobiles were placed on the perimeter of the 30 cm

by 40 cm wooden tablet, which was positioned in the coor-

dinate [2; 0]. The distances between mobiles were measured a

priori with a ruler (margin of 0.05 cm).

The introduced testbed was built using ZEBRA2411 mo-

dules from senTec Elektronik GmbH, based on the chipset

ZRP1 developed by Freescale Semiconductor [3]. This module

operates in the 2.4GHz ISM frequency band and allows

wireless communication over a distance of more than 500m

(line-of-sight).

ZEBRA contains a microcontroller, the High Frequency

circuitry and a chip antenna with low noise amplifier and

power amplifier stages. An integrated Freescale HCS08 MCU

serves as the base band controller and operates with the 8MHz

frequency. A protocol SMAC (Simple Media Access Con-

trol) [12] has been used for communication between nodes,

which were programmed using the Metrowerks CodeWarrior

development environment from Freescale.

B. Experimental Results and Their Analysis

According to the described above experimental setup, we

conducted a measurement campaign that took approximately

24 hours. The location estimation of each mobile node was

performed each second. With respect to the analysis of simu-

lation results, the ”P-UnIS Sequential Selective in Descending

order” scheme has been chosen for refinement because it

presented the best improvement ratio during the simulation.

The average localization error information with and without

improvement was collected during the experiment. The com-

parison of results obtained during the simulation and during

the experiment is presented in Fig. 4.

On the one hand, the unimproved results obtained with

the simulation and with the experiment differ significantly. It

means that the simulated RSS values do not reflect the com-

plexity of the multipath propagation environment represented

by the university office room in the experimental testbed.

An adaptation of the simulation engine should be performed.

On the other hand, the UnIS algorithm provides in both

cases significant improvement achieving approximately 0.4 m

696

average localization error. It is obvious that the improvement

ratio obtained empirically corresponds to magnitude of the

ratio received in the simulation. Furthermore, the improvement

ratio of experimental results is even better than that from the

simulation partially because of the worse starting position at

the beginning of the refinement process in the experiment

(bigger average localization error according to the unimproved

results).

According to the experimental evaluation of the UnIS

scheme, it has been proven that the proposed algorithm im-

proves the localization results significantly in the real scenario

with hard multipath propagation of radio signals.

VI. CONCLUSION AND FUTURE WORK

In this paper, we have introduced several new algorithms

based on the idea of UnIS from our previous work. We

presented their mathematical models and simulation results.

Furthermore, the experimental testbed was described and the

obtained empirical results were compared with those from the

simulation.

We have demonstrated that the proposed UnIS scheme and

its modifications improve the localization precision signifi-

cantly. The algorithm could improve the average accuracy

from 1.05 m to 0.35 m according to the simulation results; and

from 1.4 m to 0.38 m according to the experimental results.

The main drawback of the UnIS algorithm is represented

by the need of finding the distances between mobiles. There

are always some situations where it is possible to measure this

distance very accurate before the experiment although it takes

some overhead. There are also scenarios when the mobiles

can estimate the distances to their neighbors themselves with

some certain precision and use them as additional information

for refinement process.

The benefits of the proposed scheme can be listed as fol-

lows. The approach produces a very high improvement ratio,

can be distributed among nodes, does not require any extra

circuitry and can be combined with any location estimation

scheme and any other refinement method (e.g., filtering). Ad-

ditionally, the low complexity of the algorithm allows running

it on an embedded platform even with small computational

capacity.

In our future work, we are going to extend UnIS and develop

a weighted refinement algorithm which will consider the

difference in estimation errors of distances between mobiles

with respect to the reference distance known a priori. This

should reduce the influence of the most erroneous estimates

on the provided improvement.

REFERENCES

[1] O. Artemenko, G. Schorcht, and M. Tarasov, “A refinement schemefor location estimation process in indoor wireless sensor networks,” inIEEE Global Communications Conference (IEEE GLOBECOM 2010),Workshop on Heterogeneous, Multi-Hop, Wireless and Mobile Networks(HeterWMN), Miami, USA, Dec. 6–10, 2010, pp. 225–230.

[2] O. Artemenko, G. Schorcht, S. Mann, D. Schulz, and M. Tarasov, “Agroupwise universal improvement scheme for location estimation inwireless networks,” in Proceedings of 20th International Conference onComputer Communications and Networks (ICCCN 2011), aug. 2011, pp.1 –5.

[3] senTec Elektronik GmbH, “ZEBRA 24xx SM ZigbeeTM enabled boardfor radio applications,” Product Information 1.7., May 2007.

[4] R. Zekavat and R. Buehrer, Handbook of Position Location:Theory,Practice and Advances, 1st ed. Berlin, Germany: Wiley-IEEE Press,2012.

[5] R. Mautz and W. Ochieng, “A robust indoor positioning and autolocali-sation algorithm,” in Journal of Global Positioning Systems, vol. 6, no.1, 2007, pp. 38–46.

[6] J. Eckert, K. Koeker, P. Caliebe, F. Dressler, and R. German, “Self-localization capable mobile sensor nodes,” in IEEE International Con-ference on Technologies for Practical Robot Applications (TePRA 2009),nov. 2009, pp. 224 –229.

[7] J. Schwendemann, T. Mueller, and R. Krautschneider, “Indoor naviga-tion of machines and measuring devices with igps,” in InternationalConference on Indoor Positioning and Indoor Navigation (IPIN 2010),sept. 2010, pp. 1 –8.

[8] G. Zanca, F. Zorzi, A. Zanella, and M. Zorzi, “Experimental compar-ison of rssi-based localization algorithms for indoor wireless sensornetworks,” in in REALWSN 08: Proceedings of the workshop on Real-world wireless sensor networks, New York, USA: ACM, 2008, pp. 1–5.

[9] K. Srinivasan and P. Levis, “Rssi is under appreciated,” in Proc. ofthe Third Workshop on Embedded Networked Sensors, EmNets 2006,Boston, USA, 2006, p. 1.

[10] C. Taylor, A. Rahimi, J. Bachrach, H. Shrobe, and A. Grue,“Simultaneous localization, calibration, and tracking in an ad hocsensor network,” in Proceedings of the 5th international conferenceon Information processing in sensor networks, ser. IPSN ’06. NewYork, NY, USA: ACM, 2006, pp. 27–33. [Online]. Available:http://doi.acm.org/10.1145/1127777.1127785

[11] O. Artemenko, G. Schorcht, and M. Binhack, “An improved location es-timation algorithm in an ad hoc sensor network for indoor environment,”in Proceedings of the 54th IWK - International scientific Colloquium,Ilmenau, Germany, Sep. 7–11, 2009, pp. 1–5.

[12] Freescale-Semiconductor, “Simple media access controller (SMAC).users guide,” SMACRM, Rev. 1.5, March 2008.

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