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Estimation at Network Layer in WLAN Environment. Shibkali Bera1 ... Abstract- A lot of wireless localization schemes using time of arrival (TOA) have been ...
Evaluation of TOA-Based Localization Schemes using Range Estimation at Network Layer in WLAN Environment. Shibkali Bera1 School of Mobile Computing & Communication Jadavpur University, Kolkata, India [email protected]

Sanjoy Kumar Mondal2 Birbhum Institute of Engineering & Technology, Suri Birbhum, West Bengal, India [email protected]

Pampa Sadhukhan3 School of Mobile Computing & Communication Jadavpur University, Kolkata, India [email protected]

Abstract- A lot of wireless localization schemes using time of

is that it would incur a lot of expenses and face some

arrival (TOA) have been proposed in the literature with

technical challenges to replace the existing handset with

comprehensive performance comparisons in terms of accuracy, complexity and robustness. The major issue in TOA-based localization schemes is that the presence of error due to nonline-of-site (NLOS) propagation in the range measurements

GPS-equipped handsets. Thus, a lot of researchers have concentrated on network based localization [2] that does not require any changes to the existing handsets and utilizes the

introduces a lot of errors in location estimation. Thus we have

existing network infrastructure, i.e. cellular system or WLAN

focused on those TOA-based localization schemes that can

system to estimate the position of the Mobile Station (MS).

mitigate the effect of NLOS errors to provide reasonable location

accuracy.

Another

major

issue

in

TOA-based

localization is the computational complexity incurred by solving

Network-based localization using time-of-arrival (TOA), time difference-of-arrival (TDOA), angle-of arrival

the non-linear equations associated with the location estimation.

(AOA) and received signal strength (RSS) have been

Thus, among the various TOA-based localization schemes that

extensively reviewed in [2], [3], [4]. In RSS based technique,

work in NLOS environment, we have chosen two-step least

the distance between the MS and base station (BS) or access

square (LS) method for estimation of location of a mobile user

point (AP) is measured by the attenuation of signal

in WLAN environment. To the best of our knowledge, no such

propagated between the MS and the BS or AP. The accurate

real evaluation of two-step LS method does exist in literature. In this paper, we have proposed a novel technique for estimation of round trip time (RTT) between the Base Station (BS) and the Mobile Station (MS) of Internet Control Message Protocol (ICMP) packet in order to avoid the requirement for exact

distance measurement depends on the radio propagation model that defines how signal strength reduces with distance traversed by the signal. The continuously varying signal propagation condition makes the RSS-Based localization

synchronization between the MS and the BS. Our proposed

technique obsolete in outdoor environment. On the other

technique also shows a statistical post-processing step to reduce

hand, pattern matching localization technique based on RSS

the errors incorporated into TOA measurements. Moreover, in

requires constructing and maintaining a fingerprint database

this paper we have compared the performance of two-step LS

for a particular area that is very time consuming and

method in theoretical environment with that in WLAN

laborious. Among other localization techniques, Time

environment.

Difference

of

Arrival

(TDOA)

requires

precise

Keywords: Time-of-Arrival (TOA), Base Station (BS), Mobile

synchronization between the BSs whereas a small error in

Station (MS), Two-Step Least Square (Two Step LS), Wireless

angle measurement would produce a large amount of location

Local Area Network (WLAN). Average Location Errors (ALE)

estimation error in case of Angle of Arrival (AOA). Thus, we mainly focus on TOA-based localization techniques in this

I. Introduction

paper because the measurement of signal propagation time

Wireless localization has gained a lot of popularity as it is

between the BS and the MS from round trip time (RTT)

essential for providing location-based services such as

measurement does not require the precise synchronization

location sensitive billing, location-based routing, and

between the BS and the MS. However, the major issues in

intelligent transportation system and so on. Although, it is

TOA-based localization techniques are that the range

possible to estimate the location of a mobile user accurately

measurements contain errors due to system measurement

using Global Positioning System (GPS), it does not work

noises and non-line-of-sight (NLOS) propagation caused by

well in indoor areas because of its very poor signal strength.

blockage of direct path between the BS and MS. The large

Another problem with the wide use of GPS-based positioning

positive bias of NLOS error makes the range measurement

1

978-1-4577-1328-6/11/$26.00 2011 IEEE

larger than the true distance between the MS and the BS. A lot of TOA-based localization schemes that can mitigate the effect of NLOS propagation have been proposed in [5-9], [11] and [12]. However a major issue in these TOA-based localization schemes working under NLOS environment is the computational complexity incurred by solving the nonlinear equation associated with the location estimation of MS. Thus to avoid the heavy computation required to solve the

II. Overview of Two-step LS method: In this section, we have explored the two-step LS method proposed in [10] in brief. The two-step LS method estimates two-dimensional (2D) location of the MS from a set of TOA measurements under the presence of NLOS errors and a small amount of system measurement errors. The algorithm is divided into two steps and it has been described below.

non linear equations, we have chosen the localization method

y

proposed in [10] that only needs two computing iterations to

MS (x , y)

estimate the position of the MS under presence of NLOS error. To demonstrate the validity of above-mentioned localization method known as two-step least square (LS), authors in [10] have provided simulation results only by considering the error due to system measurement noise as a

BS (xi , yi )

Gaussian random variable with zero mean and the amount of NLOS errors incorporated into the set of measured distance

x

Fig.1 Localization in 2-D plane

as a constant positive value. In these simulations, measured distances between the MS and the BSs are generated by

Considering, (x, y) is the position of the MS and (xi, yi) is the

adding systems measurements errors and NLOS errors to the

position of the ith BS as shown in Fig. 1 and di is the

true distance between the MS and the BS. Thus, this sort of

measured distance between ith BS and the MS, which is

simulations does not guarantee that the algorithm would

always larger than the true distance between ith BS and the

achieve desired location accuracy in real environment. To the

MS in presence of NLOS errors, we obtain the following set

best of our knowledge, no evaluation of the above mentioned

of equations

localization scheme have been made in real environment, i.e.,

di2 ≥ (xi – x)2 + (yi – y)2

cellular network or WLAN environment. Thus in this paper,

= Ki – 2xix – 2yiy + x2 + y2 for i= 1,2,3 ,.., M……………..(1)

our aim is to evaluate the performance of the localization scheme proposed in [10] in WLAN environment by

Where, M is the number of BSs and Ki = xi2+yi 2

estimating the TOA measurement from the RTT of signal

After replacing x2+y2 by R, the following set of linear

propagated between the MS and the BS that is part of WLAN

equations are obtained

infrastructure

to

avoid

the

necessity

of

precise

synchronization between the BS and the MS. This paper is organized as follows. Section II presents an overview of two-step LS method and performance evolution

– 2xix – 2yiy + R≤ di2 - Ki for i = 1, 2, 3,….., M…………..(2) Considering Za =[x, y, R]T , equation (2) can be rewritten as Ga*Za ≤ h ,…………………………………………..…….(3)

of the method in simulation environment. The proposed technique for estimation of time delay or TOA in WLAN

Where,

2

environment is presented in section III. Section IV provides requirements and experimental testbed for Two-Step LS

h=

method is discussed and after that Experimental results and comparison between evaluated results and simulated results for above method are analyzed. Finally section V presents the conclusion and our future scope of research.

d1 - K1 2 d2 - K2 : : : : 2 dM - KM

Ga =

-2x1 -2x2 -2xM

-2y1 1 -2y2 1 : : -2yM 1

In case of LOS propagation between the MS and the BSs and if there is no system measurement error the equation (3) turns into equalities.

The intermediate location estimation of the MS after the first step of two-step LS method can be obtained as follows Za = (GaT Ψ-1 Ga)-1 GaT Ψ-1 h ……………….. (4)

among the above factors. According to the graphical representation it is shown that, when the number of BSs is changed from 5 to 10, then the value of ALE is changed 150 meter to 100 meter. So, from this graphical result it is cleared

Where, Ψ=4c2 BQB and B=diag {d1, d2,…..dM} and c is

that to improve the accuracy, it should necessary to increase

speed of signal propagation

the number of BSs.

Here Q is the Covariance matrix of measured noise. After obtaining the initial location estimation of the MS, the final location estimation of the MS can be obtained as follows Zf =[x2 y2]T =(Ga’ T Ψ ‘- 1 Ga ‘)-1 Ga’ T Ψ ‘- 1 h’ …………….(5) Where

[Za]12 h’ = [Za]22 [Za]3

G a’ =

1 0 0 1 1 1

Zf =[x2

y2]T

Now, Ψ’ = 4c2 B’QB’......................................................(6) Where B’ = diag {x0, y0, 0.5}

Fig.3. Average Location Errors (ALEs) are changed with Noise (N) in case of theoretical evaluation Two Step Least Square method.

Fig. 3 shows that ALEs increase slightly, when the So the final location estimate of the MS is Zp = √ Zf

…………………………...…….………(7)

Performance evolution in simulation environment: In the simulation, the average location error (ALE) is calculated by using following equation. 0 2

0 2

ALE = [√ {(x – x ) + (y – y ) }]….................(8) Where, (x, y) is the estimated position of MS and (x0, y0) is the original position of the MS.

power of noise increases, and it increase largely as the NLOS interference increases. III. Proposed Technique for Estimation of TOA in WLAN Environment: This section presents our proposed technique for estimation of time delay or TOA from the round trip time (RTT) taken by network-layer packet in order to avoid the need for precise synchronization between the MS and BS. Mobile station TTX data packet

Base Station Tp datapacket

tPROC data packet Fig. 2 Average Location Errors changed with the number of BSs (M) in

RTT

tP ACK

theoretical evaluation of two step LS method. Fig.4 RTT measurement using IEEE 802.11 data/ACK

The variance in system measurement noise denoted 2

2

packets RTT is the time taken by a signal to travel from transmitter to

as (σ ) is considered 100 m and the maximum number of

a receiver and return back. We have estimated RTT in

BSs is assumed ten and the possible maximum error (N) is

between MS and BS by measuring the time elapsed between

considered as 300 m. In Fig. 2 it is shown that the ALEs

sending a packet (Ttx data packet) from the network layer and

decrease slightly as the number of BSs increases. This means

reception of the corresponding acknowledgement packet

that the number of BSs plays a dominating role in the ALEs

(Tp ACK) as seen in Fig.4.

So, the measured Toa(τi) at ith BS is

τi =

TRTT

BS. Then from ten RTT values, average RTT is computed. By using the average RTT the distance between MS and ith

………..……………….(9)

2

BS is estimated.

Where, TRTT is the total RTT measurement. But this τi consist

Y

various delays i.e., processing delay, routing delay, queuing delay etc. For that reason the value of τ is increased, so that

.

BS6

the error in distance measurement will be highly increased. If

( 0,8)

BS7 ( 4, 8 )

BS8 (8,8)

these errors are reduced from the measure RTT then the effective

propagation

time

(τe)

is

calculated

from

BS4 (0, 4)

equation (11)

τe =

TRTT – 2 TPROC – 2 TQUE 2

………………(10)

BS5 (8,4)

X’ BS1 ( 0, 0)

BS9

Where, TPROC is the processing time and T QUE is the queuing

MS ( 4 , 4) )

( -4, 0

)

BS2 ( 4,0 )

X BS3 (8,0)

BS10 ( 0 , -4)

time. So, we can get the measured distance (di) between BS

Y’ Fig. 5 The position of BSs and MS during experiment to measure

and MS.

RTT in Two-Step LS method

di = τi * c ……………………………………….(11) Where, c is the propagation speed of signal and τi is the

IV.b Experimental results and comparison between evaluated results and simulated results for Two-Step LS method:

After measuring RTTs in a sequence between the

corresponding TOA measurement at ith BS.

MS and each BS, average RTT is computed and it is shown IV: Performance evaluation of two-step LS method in WLAN environment:

in Table 1. From the average RTT values measured distance between the BSs and MS is computed using equation 10.

The experimental testbed is required to evaluate two-step LS

Table 1 shows the values for average RTT and measured

method consist ten BSs and one MS and it is shown in

distance between each BS and MS that is located at fixed

Fig.

5. The position of each BS and the system configuration of the BSs and the MS provided in section IV.a and section IV.b provide the experimental result and then compares between evaluated results and simulated results for two-step LS method. IV.a Requirements and Experimental Testbed for Two-Step LS method:

position (4, 4). BS’s position (xi,yi)

MS

Avg RTT

Measured

position

(in ms)

distance

1.(0,0)

0.002989956

448m

2. (4,0)

0.002853606

428m

3. (8,0)

0.003010806

462m

0.002808156

421m

(4, 4)

The following devices have been used as BSs and

4.(0,4)

MS:- notebook, HP Compaq 2230s, Intel(R) Core™ 2 duo

5. (8,4)

0.002880006

432m

6.(0,8)

0.003035406

455m

7.(4,8)

0.002899056

434m

8.(8,8)

0.002898956

414m

Table 1. Now

9.(-4,0)

0.003391332

508m

they are all connected in an adhoc network. The MS is

10.(0,-4)

0.003401223

510m

CPU T5870,2.00 GHz, 956 MB of RAM running in Fedora 10. To evaluate the Two-Step LS method in WLAN environment, ten BSs and one MS are considered as shown in Fig. 5. The positions of ten BSs are given in

randomly positioned along the line y=4. A software routine is developed into all BSs and MS to measure RTT between BS and MS where the MS takes ten RTT measurements to every

Table 1: Distance measurement from RTT in WLAN environment

The measured distances between each BS and the MS shown

In

WLAN

environment

(SMCC

LAB,

Jadavpur

in Table1 incorporate additional error due to queuing delay and

University), to improve the location estimation accuracy in

processing delay at the network layer and data link layer.

WLAN environment, there are considered same scenarios as

Thus, to remove these additional errors, we have taken the help of another system that is called reference BS (Ref. BS). The Ref. BS is placed very near to the MS to measure average RTT between the Ref. BS and the other BSs as shown in

Table 2.

The Ref_RTT basically indicates the additional queuing & processing delay incorporated into RTT measured between the BSs and the MS. Ref_RTT can be represented by the following equation. Ref_RTT = 2*(TProc + TQue)…………………….…………..(12)

well as real evaluation of two-step LS method. Additionally one BS is considered as a Reference BS (BSr) which is placed as close as possible to MS’s position. Now they are all connected in adhoc networking mode where the MS takes ten RTT measurements to every BSs and the Reference BS. Then from ten RTT values, average RTT and reference RTT are calculated. After that the reference RTT is subtracted from the average RTT of ith BS. By using the subtracted RTT the distance between MS and ith BS are calculated. The measured distance between BS and MS from using this algorithm consists less noise.

Thus, effective propagation time (τe) can be obtained as follows τe =(TRTT – Ref_RTT) /2 …………………..………………(13) Table 2 shows the measured distance between each BS and MS computed using equation 13. Fig. 6 shows how the ALE varies in Two-Step LS method

A data set is given below to calculate the measured distance by using equation (10) and (11).

BSs PosiTion

MS

Avg_RTT (in ms)

1.(0,0)

0.002989956

2.(4,0)

0.001753334

Ref _RT T

Trans_ RTT (in ms) .0004054

Measured distan -ce 60m

.00026905

40m

.00049625

74m

.0002236

33m

.00029545

44m

while the number of BSs (M) is changed in simulation and real evaluation.

3.(8,0)

(4,4)

0.003080988

0. 00 25 84 55 64

4.(0,4)

0.002107793

5.(8,4)

0.001979787

6.(0,8)

0.002709315

.00045085

67m

7.(4,8)

0.001898871

.0003145

39m

8.(8,8)

0.002998775

.0003144

47m

9.(-4,0)

0.003391332

.0008067

121m

10.(0,-4)

0.003401223

.0008166

122m

Fig. 6 Comparison both results in case of ALEs, when the number of BSs (M) is changed.

It is evident from Fig. 6 that when the number of BSs is

Table 2: distance measurement from RTT in WLAN environment using reference Base Station

increased then the ALE will be gradually decreased. That means due to increase the number of BSs the location finding

Comparative study between the two methods in WLAN

accuracy will be higher because the value of ALEs are

environment.

decreased. But it is shown from the above figure, in real

By doing the real evaluation of the two methods it

environment the rate of decreasing ALEs is less than the

is shown that the ALE is proportional to increase with the

simulated scenario as per BSs number increased due to some

noise variance i.e. when noise variance is high the value of

additional error in real environment which are not considered in

ALE is also high as shown as Fig. 7. But with compare to

simulation environment.

both methods it is cleared that in a certain noise variance the value of ALE of two-step LS Method without using Ref. BS

is higher than the value of ALE in two-step LS method using

Finally it is concluded that the measured distance using

Ref.BS.

reference BS is less noisy than the measured distance without

It proves that using Ref. BS provides more accuracy to find

using Reference BS.

the MS position because in this method there is no concept of reference BS.

tprop =

(Total_RTT – RTT_Reference BS) 2

Section V: Conclusion and Future Scope During measurement of RTT in Network level, sending first packet may take more time and in some networks, the first packet will often be lost. This problem could be avoided by sending more than one packet and it should better to ignore the results for the first packet. In this paper evaluated algorithms for indoor localization technique only consider the static object not the mobility of the object in dynamic environment. In future it can be extended for localization of a Fig. 7 Comparison of ALEs changed with Noise Variance (N) between using Ref BS vs without using Ref BS.

During experiment MS sends the ICMP packet to different BSs to calculate the RTT. Actually, Total_RTT = {Processing time + Propagation time + queuing time + processing time + transmission time (ack) + queuing time} Where, processing time means the time difference between packet ready for sending and just before packet sending time. Queuing time means, when the transmitted packet is store in receiver queue but receiver did not receive it or read it, this waiting time in queue is known as queuing time. It may be considered that the processing time and queuing time are equal of all BSs like reference BS. So, it should necessary to keep the BSr as close as possible to MS., for which the propagation time is considered tens to zero ms. That means the Total_RTT of reference Base Station consists of only processing time, queuing time and very less propagation time and other various type of delays. When, the RTT is measured in WLAN environment according to Two-Step LS method, there is no reference Base Station. So it is not possible to subtract the processing time and queuing time from Total_RTT. So, the error in measured distance is obviously high in case of without using reference BS whereas using reference BS method, the processing time and queuing time is subtracted from Total_RTT, so that the effective propagation (tprop) time can easily be obtained. Now, by using the tprop and signal propagation speed (c) the distance between MS and BS are calculated.

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