International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 3- January 2016
Optimized Localization Approach for Non-Stationary Nodes
in Wireless Sensor Network
Pooja Singh1, Kanika Sharma2
Scholar, ECE Department, NITTTR, Chandigarh, India1,
Assistant Professor, ECE Department, NITTTR, Chandigarh, India 2
ABSTRACT: Wireless sensor networks gained a
lot of attention of users and researchers in last few
years because of their wide area of application.
Estimation of geographical coordinates of target
node is an important task to be accomplished in
WSNs. To achieve this task at lower cost RSSI
techniques are used, as every sensor node has
inbuilt tendency of determining strength of received
signal and quality of link communication? In this
paper a technique based on RSSI and LQI along
with maximum a posterior (MAP), named as MAPRSSI(D)- LQI is proposed to estimate position of a
moving target node, in which RSSI(D) involves a
statistical model of GMM that used offline RSSI
values. A comparison between proposed technique
and existing MAP-RSSI-LQI and mean-RSSI
techniques. The simulation results conclude that the
proposed technique gives better results than existing
techniques.
Keywords: Localization, MAP, RSSI, LQI.
I.
INTRODUCTION
Wireless Sensor Networks are consists of number of
tiny devices called sensor nodes, distributed in a
physical environment. These sensor nodes have
inbuilt capabilities of sensing the data, processing
the data and communicating with their neighbours
and so on. Due to these inbuilt capabilities WSNs
are rapidly adapted in various areas [1], such as
tracking the target, object detection, monitoring the
environment,
security surveillance etc. Almost all of these
applications require the information about the
geographic position of sensor nodes in WSN [2],
[13]. Localization
methods can be classified as: range-based method
and range-free methods [3]. It has been observed
that in order to achieve better accuracy and
scalability in mechanism of localization usually
range-based schemes are used. The range-based
localization methods depends on the assumption that
sensor nodes have inbuilt capability to determine the
angle and distance between the nodes by means of
any one of these following measurements: Received
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signal strength indicator, time of arrival, angle of
arrival, and time difference of arrival [4]. Except
RSSI almost all the measurement techniques
involves additional hardware which makes the
system costlier therefore, RSSI range-based
localization schemes are more popular. Despite of
these strength RSSI techniques have some
limitations too like they usually provides unreliable
and inaccurate results [5], because of the fact that
radio signals suffer from reflection multipath
interference, refraction etc. Various methods are
available to improve the accuracy of the RSSI
techniques like using filters to eliminate noise, using
MDS method which gives analytical results etc.
These methods can provide better RSSI values but
they require some accurate and highly complex
mathematical models.
To make the task of localization easier, finding the
relationship between RSSI and the distance is
preferred over establishment of an accurate
mathematical system. Therefore in order to improve
accuracy of RSSI techniques a method based on the
statistical property of the test data called Gaussian
mixture model is proposed, that uses RSSI-D model
for establishing relationship between offline RSSI
values and the distances.
After this, the EM algorithm is used to estimate
arguments of RSSI-D, which is an iterative method
for finding maximum likelihood or maximum a
posteriori(MAP) estimate of parameters in statistical
models. With the help of this model probability of
sub-model of RSSI-D can be estimated easily[6].
This paper is organised as: Section 1 gives
introduction about proposed technique, section 2
provides a brief review on previous work done on
localization of stationary nodes, section 3 describes
the proposed algorithm and section 4 gives
simulation results and 5 & 6 gives conclusion and
future scope of the presented work.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 3- January 2016
II.
RELATED WORK
Alot of work has been done in order to maximize
accuracy and reliability of localization in wireless
sensor network. Some of them are reviewed here:
In [7] two wireless systems have been proposed in
this paper in order to locate the electrostatic
discharge in an environment of hard disk drive, one
is based electromagnetic interference and the other
depends on received signal strength. The
electromagnetic interference based system consists
of four detectors that determine the position of ESD
event by using trilateration method whereas The
RSSI-based system includes three stationary sensor
nodes that provides reference positions and one nonstationary sensor node which is equipped with
electromagnetic
interference
detector
for
determining the position of electrostatic discharge.
In [8] a mobile assisted localization scheme called
as Perpendicular intersection (PI) is proposed. It
uses a tradeoff between range based and range free
localization techniques. In this, geometric
relationship of a perpendicular intersection is
utilized to estimate position of nodes by contrasting
RSSI values from the mobile beacon to a sensor
node.
In [9] the localization of non-stationary and
transceiver-free targets is achieved by processing
the values of received Signal Strength available at
the nodes in a Wireless Sensor Network (WSN).
After measurement of RSSI values, the probability
of the presence of unknown non-stationary objects is
estimated by customized classification approach and
the simulation results proves the feasibility of the
proposed approach.
In [10]
a low-communication-cost range-free
localization algorithm is proposed in which only
one-hop beacon broadcasting is required and the
simulation results shows that proposed algorithm
improves the accuracy as well as outperforms in an
irregular radio signal environments even.
III.
PROPOSED ALGORITHM
Localization mechanism in wireless sensor network
occurs in two steps [14]: At first step distance
between unknown nodes and anchors is determined
[15], [16] and at second step geographical position
of sensor nodes are estimated. Process of distance
estimation requires information about connectivity
between sensor nodes. Many techniques are
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available for estimation of distance between the
nodes, in this paper, an algorithm based on RSSI
distance estimation technique is used namely MAPRSSI(D)-LQI.
In this technique, RSSI-D [6] and LQI [11] are used
for estimating distance between whereas MAP i.e.
maximum a posterior estimation approach is applied
to determine the geographical position of nodes.
a. RSSI-D Model : As we know, path loss model
gives relationship between received power and
transmitted power as:
PL(d) = PL(d0) + 10n log1o (d/d0) + Xσ (1)
Where, PL(d0) is the received power at known
distance d0, n is constant and Xσ is a random
variable which reduces the accuracy of distance
estimation through RSSI values. Therefore, on the
basis of statistical properties of RSSI values, a more
model of RSSI named as RSSI-D is described in [6].
It gives relationship between distance and RSSI
values as follows:
(2)
Where dk is the distance corresponds to kth model,
M is number of selected models with probability
more than threshold and K is total number of models
as described in [6].
b. LQI (Link quality indicator): As described in
[11], link quality indicator is associated with
wireless signals and devices and provides an
idea about the quality of received signal as it is
strongly correlated with RSSI.
c. MAP (Maximum a posteriori): In some cases we
have a priori knowledge about the activities or
processes for which parameters has to be
estimated. These prior knowledge can be used to
calculate PDF of the parameter to be estimated.
Let the parameter be � , then P(� ) will be the
prior probabilities. These priors are referred to as
Bayesian inferences. According to Baye’s
theorem the estimation process incorporating
prior information can be expressed as[12]:
(3)
In this paper, MAP-RSSI(D)-LQI technique is used,
which is a point estimator that predicts the position
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International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 3- January 2016
of unknown target node by considering all the
observations simultaneously [11] and a comparison
between proposed technique and existing techniques
i.e. MAP-RSSI-LQI and Mean-RSSI[11] is done in
terms of root mean square of localization error.
IV. SIMULATION BASED COMPARISON
BETWEEN ESTIMATORS:
A number of simulations are carried out for a
comparative study with the help of matlab software.
The configuration of target node remains constant
for all simulations. Localization error and root mean
square
error is calculated for Mean-RSSI, MAP-RSSI-LQI
and MAP-RSSI(D)-LQI. Figure.1, Figure.2 and
table.1 shows a comparative study of three
estimators, and it has been shown that RMSE of
Mean-RSSI is 4.54m, RMSE of MAP-RSSI-LQI is
1.87m while RMSE of MAP-RSSI(D)-LQI is
1.42m. Therefore localization error is reduced on
using proposed technique instead of existing
techniques.
Table I. Performance analysis of proposed estimator
in comparison to existing estimators in terms of
RMSE and LE.
Estimator
MAPMEANLQIRSSI
RSSI
PROPOSED
RMSE
4.54m
1.87m
1.42m
-
58.81 %
68.72 %
-
-
24.06 %
% improvement
Over MEAN-RSSI
% improvement
Over MAP-RSSI-LQI
V.
CONCLUSIONS
A comparative analysis has been done in this paper
between three estimators: Mean-RSSI, MAP-RSSI-LQI
and MAP-RSSI(D)-LQI for reduction of error in
localization of snon-tationary nodes in a wireless
sensor network. The expirimental results and studies
shows that the proposed estimator gives 35.26% better
results than MAP-RSSI-LQI and 69.25% better results
than Mean-RSSI. Beside this, it is also computationally
efficient and takes less time in execution. Thus
according to the simulation results done by using
Matlab it has been proved that the proposed MAPRSSI(D)-LQI technique is an optimum technique for
determining the geographical positions of sensor nodes
in a wireless sensor network.
VI.
Figure1. Comparison between actual path and
predicted paths of a nonstationary node obtained by
using estimatosr: Mean-RSSI, MAP-RSSI-LQI and
MAP-RSSI(D)-LQI.
FUTURE WORK
In wireless sensor network accuracy of localization of
sensor nodes is an important task. So in this paper a
technique has been proposed to reduce the error in
existing localizing methods. Future works can be done
to furthur optimize the accuracy of localization and to
increase the reliability and scalability of localization
algorithms.
REFERENCES
[1] Y. S. E. C. I.F. Akyildiz, W. Su, “Wireless sensor networks: a
survey,” Elsevier, Journal on Computer Networks, vol. 38, no. 4,
pp. 393–422, 2002.
[2] H. Karl and A. Willing, “ Handbook on Protocols and
Architectures for Wireless Sensor Networks,” John Wiley &
Sons, 2005.
Figure2. Comparison of results of localization
between Mean-RSSI, MAP-RSSI-LQI and MAPRSSI (D)-LQI, for a single non-maneuvering target
node.
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[3] S. Meguerdichian, S. Slijepcevic, V. Karayan, and M.
Potkonjak, “Localized algorithms in wireless ad-hoc networks:
location discovery and sensor exposure,” in Proceedings of the
Second ACM International Symposium on Mobile Ad Hoc
Networking and Computing pp. 106–116, 2001.
[4] G. Blumrosen, B. Hod, T. Anker, D. Dolev, and B. Rubinsky,
“Enhanced calibration technique for rssi-based ranging in body
http://www.ijettjournal.org
Page 128
International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 3- January 2016
area networks,” Elsevier, Journal on Ad Hoc Networks, vol. 11,
no. 1, pp. 555 – 569, 2013.
[5] Z. J. Qiao Gangzhu, “An improved rssi localization method
suitable for dynamic environments,” Journal on Computer
Research and Development, vol. 47, no. 2, pp. 111–114, 2010.
[6] Xiaofeng Li, Liangfeng Chen, Jianping Wang, Zhong Chu
and Bing Liu, “ A Novel Method To Improve the Accuracy Of
the RSSI Techniques Based On RSSI-D”, Academic Publishers
Journal On Networks, Vol. 9, No. 12, 2014.
[7] K Thongpull, N. Jindapetch, W. Teerapabkajorndet,
“Wireless ESD Event Locator Systems in Hard Disk Drive
Manufacturing Environments,” IEEE Transaction on Industrial
Electronics, Vol.60, No.11, pp.5252-5259, 2013
[8] Guo Zhongwen, Guo Ying, Hong Feng, Jin Zongke, He Yuan,
Feng Yuan, Liu Yunhao, “Perpendicular Intersection: Locating
Wireless Sensors With Mobile Beacon,” IEEE Transaction
on Vehicular Technology, Vol.59, No.7, pp.3501-3509, 2010.
[9] F. Viani, L. Lizzi, P. Rocca,M. Benedetti, M. Donelli, A.
Massa, “Object tracking through RSSI measurements in wireless
sensor networks,” IEEE Electronics Letters , Vol. 44, No.10,
pp.653-654, 2008.
[10] Jen-Feng Huang; Guey-Yun Chang; Gen-Huey Chen, "A
Historical-Beacon-Aided Localization Algorithm for Mobile
Sensor Networks," IEEE Transaction on Mobile Computing,
Vol.14, No.6, pp.1109-1122, 2015
[11] B. Mukhopadhyay, S. Sarangi, S. Kar, “Performance
evaluation of localization techniques in wireless sensor networks
using RSSI and LQI,” in National Conference on
Communications, pp. 1-6, 2015.
[12]
Suyash
P.
Awate
,,
“Statistical
Inferences,”https://www.cs.utah.edu/~suyash/Dissertation_html/n
ode8.html.
[13] Zhetao Li, Renfa Li, Yehua Wei and Tingrui Pei, Survey of
Localization Techniques in Wireless Sensor Networks,
Information Technology Journal, Vol. 9, pp. 1754-1757, 2010.
[14] T. C. Karalar and J. Rabaey, “An RF ToF based ranging
implementation for sensor networks,” in Proceedings of IEEE
International Conference on Communication, Vol. 7. pp. 3347–
3352, 2006.
[15] T. He, C. Huang, B. M. Blum, J. A. Stankovic, and T.
Abdelzaher, “Range-free localization schemes for large scale
sensor networks,” in Proceedings of 9th Annual International
Conference on Mobile Computer Network, pp. 81–95, 2003.
[16] Q. Shi and C. He, “A SDP approach for range-free
localization in wireless sensor networks,” in Proceedings of IEEE
International. Conference on Communication, pp. 4214–4218,
2008.
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