Optimum Sensor Node Localization in Wireless Sensor Networks

ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Vol. 2, Issue 4, pp: (30-39), Month: October - December 2015, Available at: www.paperpublications.org
Page | 30
Paper Publications
Optimum Sensor Node Localization in Wireless
Sensor Networks
1
Neha Verma, 2
Rakhi Rani, 3
Manju Kumari
1
M.Tech Student, 2
M.Tech Student, 3
Assistant Professor
1,2,3
Department of Electronics and Communication, YMCA University of Science and Technology Faridabad- 121006,
Haryana, India
Abstract: Scientists, engineers, and researchers use wireless sensor networks (WSN) for a wide array of
applications. Many of these applications rely on knowledge of the precise position of each node. An optimum
localization algorithm can be used for determining the position of nodes in a wireless sensor network. This paper
provides an overview of different approach of node localization discovery in wireless sensor networks. The
overview of the schemes proposed by different scholars for the improvement of localization in wireless sensor
networks is also presented. Experiments were performed in a testbed area containing anchor and blind nodes
deployed in it to characterize the pathloss exponent and to determine the localization error of the algorithm.
Details regarding the implementation of new algorithm are also discussed in this paper.
Keywords: Optimum; Node localization; Sensor Networks; Anchor node; precise positioning.
I. INTRODUCTION
Wireless sensor networks (WSN) consist of hundreds to thousands of low-power multifunctioning sensor nodes,
operating in an unattended environment, with limited computational and sensing capabilities. Awareness of location of
these nodes is one of the important and critical issue and challenge. It is an crucial requirements in designing of solutions
for various issues related to WSN. Being used in environmental applications to perform the number of task such as
environment monitoring, disaster relief, target tracking, defences and many more, node localization is inherently one of
the system parameters.
In order to take advantage of these of wireless sensor nodes, we need to account for certain constraints associated with
them. Since the sensor nodes are equipped with small, often irreplaceable, batteries with limited power capacity, it is
essential that the network be energy efficient in order to maximize the life span of the network [7, 8]. Conventional
techniques such as direct transmissions from any specified node to a distant base station have to be avoided. Although
Clustering and generating a sink node can be benificial but if the exact location of destination node is known, the
transmitted energy can be estimated well in advance and thus lifetime of sensor can be predicted. Thus we can say that
sensor node localization plays a very important role in any energy efficient algorithm at both inter-cluster and intra-
cluster levels.
II. RELATED WORK
The introduction of mobile sinks in WSN s has gained more and more attention to balance energy consumption and
prolong network lifetime. In 2005 and 2006, some predefined sink movement traj ectories were studied. In [5],the authors
investigated the benefits of a heterogeneous architecture for WSN s composed of a few resource-rich mobile nodes and a
large number of simple static nodes. They studied WSN s with one mobile sink and one mobile relay individually. In [6],
the authors studied a base station which moves along a predetermined movement path to collect data from cluster heads

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(CHs). Sensor nodes which were closest to mobile sink trajectory will be chosen as CHs, and CHs sent data to mobile
node as it passed by.
But how will a sensor node keep track of the neighboring nodes? The answer is localization. Localization in sensor
networks can be defined as identification of sensor node's position for any WSN, the accuracy of its localization
technique is highly desired but the main problem we face in attaining it is locating the geometrical position of the sensor
node in the network. Localization problem is an estimation of position of wireless sensor nodes and to coordinate with
one another. Localization is a challenge which deals with wireless sensor nodes and it has been studied from many years.
There are different solutions and they are evaluated according to cost, size and power consumption.
Localization is important when there is an uncertainty of the exact location of some fixed or mobile devices. One example
has been in the supervision of humidity and temperature in forests and/or fields, where thousands of sensors are deployed
by a plane, giving the operator little or no possibility to influence the precise location of each node.[9,10]. Therefore, the
network localization problem, namely, the problem of determining the positions of nodes in a network, has attraction of
many engineering field and have been researched for many years. The device whose location is to be estimated is called
localization node, and the network entity with known location is called localization base station.
III. LOCALIZATION TECHNIQUES
Wireless sensor network consists of a large set of inexpensive sensor nodes with limited processing and computing
resources. Thus, algorithms designed for wireless sensor networks need to be both memory and energy efficient. In most
of the algorithms for wireless sensor network, it is assumed that the sensor nodes are aware of their locations and also
about the locations of their nearby neighbors. Hence, localization is a major research area in wireless sensor networks.
Nodes can utilize a global positioning system, but this solution is typically verycostly. Many researchers are focusing on
designing different algorithm but paying less attention on range measurement inaccuracy Localization is usually carried
out by measuring certain distance dependent parameters of wireless radio link between the localization node and different
localization base stations.
Existing location discovery approaches basically consists of two basic phases:
(1) Distance (or angle) estimation
(2) Distance (or angle) combining
The most popular methods for estimating the distance between two nodes are described below:
Received Signal Strength Indicator (RSSI): RSSI measures the power of the signal at the receiver and based on the
known transmit power, the effective propagation loss can be calculated. Next by using theoretical and empirical models
we can translate this loss into a distance estimate. This method has been used mainly for RF signals. RSSI is a relatively
cheap solution without any extra devices, as all sensor nodes are likely to have radios. The performance, however, is not
as good as other ranging techniques due to the multipath propagation of radio signals.
Time based methods (ToA, TDoA): These methods record the time-of-arrival (ToA) or time-difference-of-arrival
(TDoA). The propagation time can be directly translated into distance, based on the known signal propagation speed.
These methods can be applied to many different signals, such as RF, acoustic, infrared and ultrasound. TDoA methods
are impressively accurate under line-of-sight conditions. But this line-of-sight condition is difficult to meet in some
environments. Furthermore, the speed of sound in air varies with air temperature and humidity, which introduce
inaccuracy into distance estimation. Acoustic signals also show multi-path propagation effects that may impact the
accuracy of signal detection.
Angle-of-Arrival (AoA): AoA estimates the angle at which signals are received and use simple geometric relationships
to calculate node positions. Generally, AoA techniques provide more accurate localization result than RSSI based
techniques but the cost of hardware of very high in AoA.
For the combining phase, the most popular alternatives are:
Hyperbolic trilateration: The most basic and intuitive method is called hyperbolic trilateration. It locates a node by
calculating the intersection of 3 circles as shown in Fig. 1(a).

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Triangulation: This method is used when the direction of the node instead of the distance is estimated, as in AoA
systems. The node positions are calculated in this case by using the trigonometry laws of sines and cosines (shown in Fig.
1(b)).
Maximum Likelihood (ML) estimation: ML estimation estimates the position of a node by minimizing the differences
between the measured distances and estimated distances (shown in Fig. 1(c)).
Fig. 1: Localization techniques (a) hyperbolic trilateration (b) Triangulation (c) Maximum Likelihood Estimation
IV. RELATED WORK
Consider the case when we have deployed a sensor network consist of N sensors at locations S = {S1, S2,…….,SN}. Let
Sx
i
refer to the x-coordinate of the location of sensor i and let Sy
i
and Sz
i
refer to the y and z coordinates, respectively.
Constraining Sz
i
to be 0 suffices the 2D version of this problem. Determining these locations constitutes the localization
problem. Some sensor nodes are aware of their own positions; these nodes are known as anchors or beacons. All the other
nodes localize themselves with the help of location references received from the anchors. So, mathematically the
localization problem can be formulated as follows: given a multihop network, represented by a graph G = (V, E), and a
set of beacon nodes B, their positions {xb, yb} for all b  B, we want to find the position {xu, yu} for all unknown nodes u
U.
In this section, we present a probabilistic position estima-tion algorithm that considers range measurement inaccuracies.
Nodes in a sensor network can belong to two different classes, namely beacons and unknowns. We assume that the
beacons have known positions (either by being placed at known posi-tions or by using GPS), while the unknown nodes
estimate their position with the help of beacons. The first step in RF-based localization is range measurement, i.e
estimating the distance between two nodes, given the signal strength received by one node from the other. RF-based
(a)
(b)
(c)
Beacons or Anchor Nodes
Unknown Node that needs to be localized

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signal strength measurements are usually prone to inaccuracies and errors and, hence, calibration of such measurements is
inevitable before using them for localization. For this algorithm to work, extensive preliminary field measurements and
calibrations were carried out as discussed in the following subsections.
At a high level, the guidelines are a result of the fact that the probability ofextremely high location error results from
anchor nodes being roughly in a geograph-ically straight line. As the anchor nodes are spread out from a straight line, the
probability of high errors decreases, leaving network designers a relatively simple chore when choosing anchor nodes
locations.
a. Basic Framework:
As far as empirical evidence is concerned, authors will come across anchor placement by accident and discuss it basedon
their own empirical evidence. Shang, et al. [2] and Li, et al. [3] both choose anchors at random within the network.
Although, Shang, et al., do mention that a co-linear set of anchors chosen in one example ”represents a rather unlucky
selection”, without supporting evidence of why this is unlucky. Earlier work by Doherty, et al. [1] requires anchor nodes
to be placed at the edges, and ideally at the corners of the network. In this case, however, the algorithm is a simple
constraint problem. One constraint requires that all the unknown nodes be placed within the convex hull of the anchors,
and therefore, better results are obtained when anchors are at the corners.
An undirected topology of the sensor networks is described by the tuple T = (N; E; W), where N, E and W represent the
set of sensor nodes, edges connecting pair of nodes and weights assigned to edges between each pair of nodes
respectively. Weights in this context refer to the received signal observations assuming one node is transmitting and other
is receiving. The weight is invariant in both direction i.e. {wij = wji}  R. The location of sources are assumed to be
known. Location of the ith source is represented by ( , 2 S , wherei S is the set of source nodes. ( xi
t; yi
t; zi
t), i 
N denotes the location of unknown ith mobile node at each instant t. The problem of localization is to find the location of
these mobile nodes at each instant. To estimate the location of mobile nodes, distances from each of the sources are
computed.
The distance between pair of nodes is estimated based on received signal strength [12, 13] and shown below.
( ) ) + X (1)
Where, Pt and Pr are the transmitted and received signal powers in dB respectively. d, ne and X denote the distance
between pair of nodes, path loss exponent and noise respectively. Gt, Gr and are the transmitting antenna gain,
receiving antenna gain and carrier wavelength respectively. In the following Section, relationship between received signal
and distance is established experimentally for different modalities of signal.
b. Major sources of Error and Calibration
AOA measurements are impaired by the same sources discussed in the TOA section: additive noise and multipath. The
resulting AOA measurements are typically modeled as Gaussian, with ensemble mean equal to the true angle to the
source and stan-dard deviation σα. Theoretical results for acoustic-based AOA estimation show standard deviation
bounds on the order of σα=2◦
to σα=6◦
, depending on range [49]. Estimation errors for RF AOA on the order of σα=3◦
have
been reported using the RSS ratio method [14].
It is not likely that sensors will be placed with known orienta-tion. When sensor nodes have directionality, the network
local-ization problem must be extended to consider each sensor‟s orientation as an unknown parameter to be estimated
along with position. In this case, the unknown vector θ is augmented to include the orientation of each sensor. The models
presented earlier are sufficient to find bounds on localization performance in cooperative localization. These lower
bounds are not a function of the particular localization algorithm employed. Thus we have presented some of these
performance limitions in this section before discussing algorithm of this research.
In particular, each sensor estimates its coordinates by finding coordinates that minimize the total squared error between
calculated distances and estimated distances.
Sensor j's calculated distance to seed i is:
dji = √ [ (xi - xj)2
+ (yi - yj)2
(2)
and sensor j's total error is:

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∑ (3)
In equation (2) and equation (3), n is the number of seed sensors and dˆji is the estimated distance computed through
gradient propagation. The coordinates are then incrementally updated in proportion to the gradient of the total error with
respect to that coordinate.
V. PROPOSED APPROACH
The goal of proposed algorithm is to determine specific blind node‟s location within the distributed nodes along the
testbed area. If the positions of the blind nodes are not known in a network, the event these monitor and report can not be
located if need be. The primary obstacles to localization in wireless sensor network is the sparse anchor node problem,
hence, this algorithm is structured to solve the problem.
Features of required Algorithm:
 Interferomatric ranging based localization that takes error propagation into account.
 Attack the challenges of Information Asymmetry.
 Secure and robust.
 Accuracy.
 Incresed flexibility by taking obstacles into account.
Fig. 2: Difference in hop count distance in the presence of an obstacle.
Consider Fig. 2 where the hop count distance between 1 and 5 node is four hops due to the obstacle, but the real distance
is far lesser than four values. Thus it is important for algorithm to readjust itself in the presence of an obstacles. It requires
substantial node density before its accuracy reaches an acceptable level as the shown hop count is not reliable in
measurement because environmental obstacles can prevent edges from appearing in the connectivity graph that otherwise
would be present as shown in Fig 2.
In this section, we present a probabilistic position estima-tion algorithm that considers range measurement inaccuracies.
Nodes in a sensor network can belong to two different classes, namely beacons and unknowns. We assume that the
beacons have known positions (either by being placed at known posi-tions or by using GPS), while the unknown nodes
estimate their position with the help of beacons. The first step in RF-based localization is range measurement, i.e
estimating the distance between two nodes, given the signal strength received by one node from the other. RF-based
signal strength measurements are usually prone to inaccuracies and errors and, hence, calibration of such measurements is
inevitable before using them for localization.
The proposed algorithm is made up of two phases: startup phase and the resultant phase.
a. Startup phase:
The first phase is a heuristic that produces a graph embedding which looks similar to the original embedding. The authors
assume that each node has a unique identifier and the identifier of node i is denoted by IDi and the hop-count between
nodes i ad j is the number of nodes hi,j along the shortest path between i and j. The algorithm first elects the five reference
nodes in which four nodes n1, n2, n3 and n4 are selected such that they are on the periphery of the graph and the pair (n1,
n2) is roughly perpendicular to the pair (n3, n4). The node n5 is elected such that it is in the middle of the graph. At first
the node with smallest ID is selected.
3
1
2
5
4
Obstacle
Nodes

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Next the reference node n1 is selected to maximize h1,2. After that n3 is selected to minimize |h1,3– h2,3| and the tie-
breaking rule is to pick the node that minimizes h1,3+ h2,3. In the next stage n4 is selected to minimize |h1,4– h2,4| and the
ties are broken by picking the node that maximizes h3,4. Now for all nodes ni the heuristics uses the hop-counts h1,i, h2,i,
h3,i, h4,i, and h5,I from the chosen reference nodes to approximate the polar coordinates (ρi, θi) where:
(4)
– – (5)
b. Resultant phase:
In the second phase, each node ni calculates the estimated distance dî,j to each neighbours nj and it also knows the
measured distance ri,j to neighbour nj. Now if vî,j represent the unit vector in the direction from pî to pˆj ( pî and pˆj at the
current estimates of i and j respectively) then the force Fi,j in the direction vî,j is given by:
Fi,j = vî,j ( dî,j– ri,j) (6)
And the resultant force on node i is given by
Fi = ∑i,,j Fi,j (7)
The energy Ei,j of nodes ni and nj due to the difference in measured and estimated distances is the sequence of the
magnitude of Fi,j and the total energy of node i is equal to:
∑ ∑ – (8)
And the total energy of the system E is given by:
E = ∑i Ei (9)
Now the energy Ei of each node ni reduces when it moves by an infinitesimal amount in the direction of force Fi. In the
optimization, the magnitude of Fi for each node ni is zero and the global energy of the system E is also zero and the
algorithm converges.
VI. SIMULATION RESULTS AND ANALYSIS
To study the robustness of the proposed localization algorithm, a MATLAB program was developed; this program
implemented the algorithm using the input statement and other matlab statements which is more interactive and better for
analysis.
a. Data Collection
The Table1 is based on the existing experiments which uses a different randomly-generated topology and a Zigbee
CC2420 Module.
Table 1: Total Average Receive Signal Strength w.r.t distance
Distance (m) RSSI (dBm)
1 -44.8
2 -47.7
3 -48.7
4 -53.1
5 -55.6
6 -61.8
7 -67.2
8 -66.5
9 -69.0
10 -67.3
From Table 1 the data collected was used to develop a graph and for computing the pathloss exponent n of the testbed
area using eq. (1). From the computation, as shown in fig. 3 was computed to be 2.2. Hence, n = 2.2 will be used as the
pathloss exponent in this research work.

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Fig. 3: The average values of RSSI obtained vs. distance
b. Processing collected Data
As per an experiment [15] they measured the signal strength and noise in intervals of 2.5m up to 50m. For each distance,
we measured the data at 16 different positions. It was noted that a significant change in signal strength as a function of
distance, while the noise remained almost the same; so, we considered only the signal strength information in our
analysis.
Fig. 4: Probability distribution of signal strength with distance
Fig. 4 shows a graph with a normal fit for data collected with a received signal strength obtained by Zigbee module used.
Themean and standard deviation for each signal strength was noted and tabulated as a function of distance.
Fig. 5: Distribution of distances for a received signals and obtained RSSI
-80
-75
-70
-65
-60
-55
-50
-45
-40
0 2 4 6 8 10
RSSIValues(dBm)
Distance (m)
0
0.05
0.1
0.15
0.2
0.25
12.5 15 17.5 20 22.5 25 27.5 30 32.5 35 37.5 40
ProbabilityDistributionofRSSI
values
Distance (m)
0
0.01
0.025
0.1
0.2
0.075
0.025
0
0
0.05
0.1
0.15
0.2
0.25
0 10 20 30 40 50
ProbabilityDistributionFunction
Distance (m)

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A graphical view of the table is shown in Fig. 5 for signal strengths ranging from 66 to 90. As per our proposed
algorithm, the method uses this table for ranging; i.e., any node receiving a beacon packet will estimate itself to be
located on a surface that has a probability distribution dictated by themean and standard deviation corresponding to the
signal strength received. Note that the bound on standard deviation of local-ization error is proportional to (1/γ )1/2
. It
makes sense that the localization error is proportional to σT for TOA and σα for AOA.
c. Probabilistic Localization
Fig. 6. Shows results when only the distance based localization of a single target is considered. There can N anchor nodes
in the system and one mobile node, we use the measured distances and we find the error in location of the mobile node
using eq. (3).
Fig. 6: Implementing localization using 4 anchor and 1 mobile user to find detection error.
Initial Parameters:
N = 4 (number of anchors)
M = 1 (number of mobile nodes)
NoisePow = 20;
The estimated error w.r.t to correct postion of anchor is found out to be 1.8668m when we have considered a
100mX100m plot. It means that the accuracy of distance measurement is 90 % for instance the inaccuracy of a 1m
measured distance is around 0.1 meter. Thus Impliementing c. Probabilistic localization concept for self optimization of
sensor node is acheived.
d. Implementing Localization on a randomly deployed wireless network
The first hypothesis was to choose nodes as anchors that we expect to have high location accuracy. Because we are trying
to provide an a priori design technique for network planners, the information chosen to reach this goal must be applied to
the randomly deployed network, as shown in Fig. 7. With this implementation we found that on an average, increased
network density results in higher localization accuracy [2, 16]. Since the range-free algorithms depend heavily on
network connectivity, the theory is that nodes with more neighbors will have lower location errors. The lower error in the
anchors themselves should then translate into a more accurate transformation of the entire network.
Fig. 7: The sample WSN used for showing localization of randomly selected mobile node.

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A simulator can be very useful in geographic routing application of sensor localization where the use of the coordinates of
sensors can reduce routing tables and simplify routing algorithms. Localization errors, however, can adversely impact
routing algorithms, leading to longer paths and delivery failures [18]. For the purposes of routing efficiency, actual
geographical coordinates may be less useful than “virtual” coordinates [19] (i.e., a representation of a sensor‟s „location‟
in the graph of network connectivity).
Researchers who are testing localization algorithms, like those presented in the later section on algorithms for location
estimation, can use WSN localization simulators as well. These kind of softwares provide public access to a multifeatured
MATLAB-based code and GUI for the calculation of the localization as shown in Fig. 8. It allows the inclusion of device
orientation and clock biases as unknown nuisance parameters. Sensors can be arranged visually using the GUI and the
bound can be calculated.
Fig. 8: Using WSN localization Simulator for deploying MATLAB program designed for single mobile node in multiple node
environment
VII. CONCLUSION
Some localization schemes have fewer merits and greater demerits and some of them have less demerits and greater
merits. These merits and demerits were the main source for proposing the idea of a unique approach which is the
enhanced composite approach. In this paper, we have described a novel, robust and dis-tributed algorithm for localizing
nodes in sensor networks. The approach is probability based and takes range measurement inaccuracies into account,
which, according to our knowledge, have not yet been pursued in literature. Our Extensive outdoor field measurements
and calibration indicate that a received signal strength is inaccurate and the proposed approach uses this information
directly for ranging.
In this paper, we have described a novel, robust and dis-tributed algorithm for localizing nodes in sensor networks. Also,
this algorithm accounts for the impact of wireless channel and variation of received signal observation with time and due
to environmental conditions with the help of the volume constraints. The approach is RSSI based and takes range
measurement inaccuracies into account, which, according to our knowledge, have not yet been pursued in literature. The
proposed algorithm performs consistently well irrespective of the variation in the localization duration and radio
communication range. Our focus of future work is on developing a three dimensional localization algorithm in mobile
sensor networks using mobile nodes and anchors.

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