International Journal of Engineering Trends and Technology (IJETT) – Volume 28 Number 9 - October 2015
Improved Localization Approach for Stationary Nodes in
Wireless Sensor Network
Pooja Singh1, Kanika Sharma2
Scholar, ECE Department, NITTTR, Chandigarh, India 1,
Assistant Professor, ECE Department, NITTTR, Chandigarh, India 2
Abstract: Estimating the geographical positions of sensor
nodes in wireless sensor networks is a primary objective to
be achieved at lower cost. For this, RSSI based techniques
are always preferred over other techniques, as every sensor
node has inbuilt tendency of determining strength of
received signal and quality of communication link. In this
paper a technique based on RSSI and LQI along with
maximum a posterior (MAP), named as MAP-RSSI(D)- LQI
is proposed in which RSSI(D) involves a statistical model of
GMM that uses offline RSSI values. A comparison between
proposed technique and existing MAP-RSSI-LQI and meanRSSI techniques is done and it can be concluded from
simulation results that the proposed technique gives better
results than existing techniques.
Keywords: Localization, MAP, RSSI, LQI.
I. INTRODUCTION
The main components of a wireless sensor networks are
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 by
numerous application fields [1], such as monitoring the
environment, tracking the target, object detection, security
surveillance etc. Approximately all of these applications
have the basic requirement of knowledge of geographical
position of sensor nodes in a sensor network [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. It has been assumed in range-based
localization methods 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 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
ISSN: 2231-5381
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 RSSID 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.
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] an iterative approach based on coding is used to
localization the target. Here, at every iteration an M-ary
hypothesis is solved by fusion centre in order to decide the
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International Journal of Engineering Trends and Technology (IJETT) – Volume 28 Number 9 - October 2015
area of interest for next iteration. The technique is found to
be worked well in presence of Byzantine sensors even. Also
a soft-decision decoding is used to reduce the effect of nonideal channels.
In [8] for 3-D indoor localization, a finger-print based radio
signal strength indicator is used. The technique solves the
problem of fluctuating signals in indoor applications by
using propagation mechanism in which k-nearest neighbour
algorithm is used as pattern matching algorithm.
In [9] a collaborative positioning based set-theoretic
approach is employed for wireless sensor network that uses
the concept of parallel projection method to estimate
location of nodes on the basis of noisy and incomplete internode distances. It has been demonstrated that for noncollaborative positioning, the proposed approach is
analytically equivalent to the parallel implementation of
Kaczmarz
Algorithm which provides guarantee of converge to a local
minimizer and so a stationary point and for collaborative
positioning, the proposed approach is computationally more
efficient than existing approaches.
In [10] a non-iterative localization approach is proposed for
wireless sensor network using multi-dimensional scaling
method. This method offers many advantages like it
calculates the location of all the sensor nodes in a network at
the same time not individually, provides accurate
localization, robust to environmental errors, provides the
relative locations of the nodes in the absence of anchor
nodes. The performance of all the algorithms is compared.
The simulation shows that the multidimensional scaling
method provides better performance in comparison to the
other localization algorithms like taylor, chan, fang, and
hybrid optimization methods. With just three anchor node
positions MDS is capable to build the entire network.
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 available for estimation of
distance between the nodes, in this paper, an algorithm
based on RSSI distance estimation technique is used namely
MAP-RSSI(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:
ISSN: 2231-5381
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 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 MeanRSSI[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 4m, RMSE of MAP-RSSI-LQI is 1.9m while
RMSE of MAP-RSSI(D)-LQI is 1.2m. Therefore
localization error is reduced on using proposed technique
instead of existing techniques.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 28 Number 9 - October 2015
Table I. Performance analysis of proposed estimator in
comparison to existing estimators in terms of RMSE and
LE.
Estimator
RMSE
% improvement over
MEAN-RSSI
% improvement over
MAP-RSSI-LQI
LQI
MEAN- MAPRSSI
LQIRSSI
4m
1.9m
-
52.5
%
-
-
PROPOSED
1.23m
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 MAP-RSSI(D)-LQI technique is an optimum
technique for determining the geographical positions of sensor
nodes in a wireless sensor network.
VI. FUTURE WORK
Accuracy of positions of nodes is an important parameter in
a wireless sensor network. 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.
69.25
%
35.26
%
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V. CONCLUSIONS
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three estimators: Mean-RSSI, MAP-RSSI-LQI and MAPRSSI(D)-LQI
for reduction of error in localization of
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