Artificial Bee Colony algorithm for Localization in Wireless Sensor Networks

Asian Journal of Applied Science and Technology (AJAST)
Volume 1, Issue 2, Pages 200-205, March 2017
© 2017 AJAST All rights reserved. www.ajast.net
Page | 200
Artificial Bee Colony algorithm for Localization in Wireless Sensor Networks
S.Sivakumar
Assistant Professor (Selection Grade), Department of ECE, PSG College of Technology, Coimbatore, India. E-mail: sivas_cbe@yahoo.co.in
Article Received: 10 March 2017 Article Accepted: 19 March 2017 Article Published: 20 March 2017
1. INTRODUCTION
Wireless Sensor Network (WSN) is a kind of ad hoc network
that consists of autonomous sensors with low cost, low energy
sensing devices, which are connected by wireless
communication links. These sensor nodes are tiny in size and
possess limited resources namely processing, storage, sensing
and communication [1]. They are usually deployed in large
numbers over the region of interest for object monitoring and
target tracking applications. The densely deployed sensors are
expected to know their spatial coordinates for effective and
efficient functioning of WSNs. Location awareness is
significant for high-level WSN applications like locating an
enemy tank in a battlefield, locating a survivor during a
natural calamity and in certain low-level network applications
like geographic routing and data centric storage.
Localization is a fundamental problem which can be defined
as the process of finding the position of the sensor nodes or
determination of spatial coordinates of the sensor nodes.
Localization is especially important [2] when there is an
uncertainty on the exact location of fixed or mobile devices.
Localization is the process of making every sensor node in the
sensor network to be aware of its geographic position [3]. The
usual solution is to equip each sensor with a GPS receiver that
can provide the sensor with its exact location. As WSNs
normally consist of a large number of sensors, the use of GPS
is not a cost-effective solution and also makes the sensor node
bulkier [4]. GPS has limited functionality as it works only in
open fields and cannot function in underwater or indoor
environments. Therefore, WSNs are required of some
alternative means of localization.
Currently the existing non-GPS based sensor localization
algorithms [5] are classified as range-based or range-free.
Range-based localization schemes rely on the use of absolute
point-to-point distance or angle estimate between the nodes to
determine the position of unknown sensor nodes using some
location-aware nodes. Location-aware nodes are also called
as anchors or beacons. Typical range-based localization
techniques used are Received Signal Strength Indicator
(RSSI) [6], Time Difference of Arrival (TDoA) [7], Time of
Arrival (ToA) [8], and Angle of Arrival (AoA) [9].
Depending on the signal feature used, the position estimation
is found using geometrical approaches such as Triangulation,
Trilateration or Multilateration. Range-based methods give
fine-grained accuracy but the hardware used for such methods
are expensive. In range-based mechanisms, the nodes obtain
pair wise distances or angles [10] with the aid of extra
hardware providing high localization accuracy. Due to cost,
the use of range-based methods will not be preferred.
Range-free or proximity based localization schemes rely on
the topological information, e.g., hop count and the
connectivity information, rather than range information.
Range-free localization schemes may or may not be used with
anchors or beacons. These schemes do not involve in the use
of complex hardware and are cheaper when compared to
range-based schemes. Range-free methods use the content of
messages from anchor nodes and other nodes to estimate the
location of non-anchor (unknown sensor) nodes. Centroid
Algorithm [11] and Distance Vector Hop (DV-Hop) method
[12] are certain range-free algorithms. Range-free algorithms
sometimes use mobile anchors [13] for localization.
Range-free algorithms are not costly but they provide
coarse-grained accuracy.
Localization in Wireless Sensor Networks is intrinsically an
unconstrained optimization problem [14]. Localization can be
viewed as an NP-hard optimization problem. Evolutionary
algorithms are local search methods, capable of efficiently
ABSTRACT
Localization in wireless sensor networks (WSNs) is one of the most important fundamental requisite that needs to be resolved efficiently as it plays
a significant role in many applications namely environmental monitoring, routing and target tracking which is all location dependent. The main idea
of localization is that some deployed nodes with known coordinates termed as anchor nodes transmit beacons with their coordinates in order to help
the other nodes in the sensing field to localize themselves. Broadly there are two types of localization methods used for calculating positions namely
the range-based and range-free methods. Initially, a range-free localization algorithm namely, Mobile Anchor Positioning - Mobile Anchor &
Neighbor (MAP-M&N) is applied. In this algorithm, the sensor nodes use the location information of beacon packets of mobile anchor nodes as well
as the location packets of neighboring nodes to improve the accuracy in localization of the sensor nodes. In this paper, the proposed optimization
approach is Artificial Bee Colony (ABC) algorithm which is incorporated with MAP-M&N to further improve the accuracy in positioning the sensor
nodes. The objective of this work is to compare the performance of MAP-ABC approach with regard to MAP-M&N algorithm. Root Mean Square
Error (RMSE) is the performance metric to compare between the two approaches namely, MAP-M&N and MAP-ABC algorithms. A study on
average localization error and comparison between the two approaches namely, MAP-M&N and MAP-ABC has been done. Simulation results reveal
that Artificial Bee Colony approach used along with MAP-M&N outperforms by minimizing error in when compared to using only MAP-M&N
approach for localization.
Keywords: Localization, Mobile Anchor, Artificial Bee Colony, Wireless sensor networks and Root Mean Square Error.

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solving complex constrained or unconstrained optimization
problems.
The rest of the paper reviews the related research work in this
area, elaborates the Mobile Anchor Positioning (MAP)
method and the proposed Artificial Bee Colony Optimization
with Mobile Anchor Positioning (MAP-ABC) and compares
the performance of the proposed approach with MAP-M&N.
2. RELATED WORK
W-H Liao et al. [15] proposed an algorithm, Mobile Anchor
Positioning in which each sensor node receives beacons
(messages containing location information) in its receiving
range from the moving anchor as the anchor moves around the
sensing field. Among the received beacons, the sensor node
selects the farthest two beacons. The node constructs two
circles with each chosen beacon as centre. The radius of the
circle is the communication range of the sensor node. It
determines the intersection points of the two circles. Out of
the two points, one is chosen to be the location of the sensor
node based on a decision strategy.
Kuo-Feng Ssu et al. [16] presented a range-free algorithm,
which uses the following conjecture. A perpendicular bisector
of a chord passes through the centre of the circle. When there
are two chords of the same circle, their perpendicular
bisectors will intersect at the centre of the circle. A mobile
anchor moves around the sensing field broadcasting beacons.
Each sensor node chooses two pairs of beacons and constructs
two chords. The sensor node assumes itself as the centre of a
circle and determines its location by finding the intersection
point of the perpendicular bisectors of the constructed chords.
Baoli Zhang et al. [17] proposed a range-free algorithm,
which works as follows. The trajectories of the mobile anchor
are in such a way that it moves in a straight line. As it moves,
it periodically broadcasts its location to the sensor nodes. A
sensor node selects four beacons among all collected beacons.
The first group (two beacons) is the location of the mobile
anchor node when it first enters the communication range of
the sensor node. The second group is the location of the
mobile anchor node when it second enters the communication
range of the sensor node. After these positions and the
communication range are obtained, four circles are
constructed with the chosen four points as centers. Four
intersection points s1, s2, s3, s4 of the circles are calculated.
Then using the centroid formula on the four intersection
points, the position of the sensor node is calculated.
Wenwen Li et al. [18] proposed the Genetic algorithm for
localization of the sensor nodes and constructed the solution
space, coded the solutions, formulated the fitness function and
used appropriate selection mechanism to choose the parents
for the next generation. The reproduction operation on the
individuals is further performed and the solution is obtained
with high accuracy. The above genetic algorithm approach
gives good localization accuracy but the solution space is very
huge. The algorithm has to search a large number of solutions
in each of the iterations or the number of iterations will be
large. When the area of the sensing field increases, the
computation involved also increases.
Gopakumar et al. [19] proposed the swarm intelligence based
approach for localization of the sensor nodes for this
non-linear optimization problem. The objective function
chosen is the mean squared range error of all neighboring
anchor nodes. The PSO algorithm provides better
convergence than simulated annealing and ensures solution
without being trapped into local minima.
Lutful Karim et al. [20] proposed a Range-free Energy
Efficient Localization Technique using Mobile Anchor
(RELMA) especially for large scale WSNs to improve both
accuracy and energy efficiency by minimizing the number of
anchor nodes used. The performance of RELMA_Method 1
and RELMA_Method 2 are compared only with the existing
Neighboring-Information-Based Localization System
(NBLS). Simulation results demonstrate the fact that
RELMA_Method 1 and RELMA_Method 2 outperform
NBLS in terms of localization accuracy as well as energy
efficiency.
Xu Lei et al. [21] proposed a Mobile Anchor Assisted
Localization Algorithm based on PSO (MAAAL_PSO)
pertaining to adverse or dangerous application environments.
The Region of Interest (ROI) is divided into grids and the
mobile anchor deploys virtual anchors on the vertex of each
grid. Based on this deployment, the node localization is
converted into non-linear constrained optimization problem
solved by PSO with the help of mobile anchor. After a few
iterations, performance evaluations demonstrate that this
algorithm improves localization accuracy. It is also robust to
the interference of environment noise.
The proposed optimization algorithm in this paper is
Artificial Bee Colony, which is applied along with Mobile
Anchor Positioning (MAP-ABC). Here the location of nodes
is initially estimated by MAP-M&N. Then ABC algorithm is
applied over the results of MAP-M&N. It is observed that
MAP-ABC approach provided relatively much better
accuracy than MAP-M&N.
3. PROPOSED LOCALIZATION APPROACH
The localization strategy used in this work can be visualized
to work in two stages. In the first stage, Mobile Anchor
Positioning - Mobile Anchor & Neighbor (MAP-M&N) is
used for determining the location of the unknown sensor
nodes. Since a range-free algorithm offers only
coarse-grained accuracy, the obtained location will be just as
an estimate. In the second stage (post optimization stage),
proposed Artificial Bee Colony (ABC) algorithm is applied
over MAP-M&N algorithm for fine-tuning the results of the
sensor nodes obtained using MAP-M&N and thereby
improving localization accuracy.
3.1. Mobile Anchor Positioning (MAP)
The simulation environment is set-up as follows: The sensor
nodes are randomly deployed in the sensing field. Mobile
anchors are location aware nodes that move in the sensing

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field fitted with Global Positioning System (GPS). As they
move around the sensing field, they periodically broadcast
beacons containing their current location at fixed time interval
to all the nodes, which are at a hearing distance from it. The
mobile anchors traverse around the field with a specific speed
and their directions are set to change for every ten seconds.
All the nodes in the communication range of the mobile
anchor will receive the beacons. A sensor node will collect all
the beacons in its range and store it as a list. The assumption
made is that the communication range of the sensor node and
the mobile anchor node are the same. Once enough beacons
are received and if a sensor node does not receive a beacon,
which is at a distance greater than the already received ones,
the localization begins at that particular node.
Assume that the sensor node has received and stored four
beacons (locations of the mobile anchor) in its list {T1, T2,
T3, and T4} as shown in Fig. 1. From the list, two beacons,
which are farthest from each other, are chosen (T1, T 4).
These points are known as Beacon points. These two points
are marked as the end of the sensor node’s communication
range since the sensor node has not received a beacon farther
from this point. Hence T1 and T4 (Beacon points) represent
either two positions of the same mobile anchor or positions of
two different mobile anchors when they were at the end of the
sensor node’s communication range.
With these two Beacon points as centers and the
communication range of a sensor node as radius, two circles
are constructed (refer Fig.1). Each circle represents the
communication range of the mobile anchor, which has sent
the beacon. The sensor node has to fall inside this
communication range, as it has received the beacon. Since the
sensor node has received packets either from both anchors or
from the two positions of the same anchor, the node has to fall
inside both the circles. Hence, it can be concluded that circles
will intersect each other.
Fig.1. Possible Locations of the Sensor Node
The intersection points of both circles are determined (S1,
S2). The intersection points are the possible locations of the
sensor node. The reason is as follows: The two farthest points
(Beacon points) are the end points of a sensor node’s
communication range. The sensor node lies on the
circumference of the other circle since it is the same with the
other mobile anchor position. Therefore, the sensor node lies
on the circumference of both the circles. The only points
satisfying the above condition are the two intersection points.
Hence, by means of Mobile Anchor Positioning, the location
of the sensor node has been approximated to two locations.
3.1.1 Identifying the Sensor Locations using MAP with
Mobile Anchor (MAP-M)
The visitor list is searched after identifying the two possible
positions i.e. the intersection points. If a node could hear
around its range, there is a possibility of a beacon point which
can be situated at a distance r from one of the two possible
locations. Thus, there is one point in the list, whose distance
from one possible location is less than r, and the distance from
other possible location is greater than r, then the first possible
location is chosen as the location of the sensor node.
It is assumed that the communication range of a mobile
anchor is R. The MAP-M maintains the visitors list after
receiving the beacon packets from the mobile anchor. The
information from the visitor list is used to approximate the
location of the sensor node. Let the visitor list of a sensor node
S consists of various location information represented as {T1,
T2… Tn}. The beacon points are the two extreme points i.e.,
T1 and Tn. Two circles with radius R and center T1 and Tn
are constructed and their intersection points of two circles are
found to be S′ and S′′. If there is any Ti (2 ≤ i ≤ n-1), such that
the distance between Ti and S′ is less than R and that between
Ti and S′′ is greater than R, then we can conclude the location
of the sensor node is S′. This is because of the fact that the
sensor node should lie inside the communication range of
mobile anchor to receive the beacon packets. Consequently,
the distance between the sensor node S and beacon packet Ti
should be less than R.
Fig. 2. Node Seeking Information from Neighbor Sensors
There is an area named as the shadow region. If all the beacon
points lie inside this region, it is not possible to determine the
location of the sensor as the shadow region comes under the
range of both the intersection points. This could be explained
by drawing two circles with S' and S" as centre and the
shadow region is the intersection of the two circles. Hence, in
order to estimate the location of the sensor node there is a
need that at least one of the beacon packets in the visitor list
must lie outside the shadow region. Therefore, it is not
possible to determine the location of the sensor node S using
the available beacon packets, thus the node is made to wait
until it gets further beacon packets. If no further beacons are

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obtained, then a single position of sensor node S cannot be
obtained. The node will have two positions S' and S" as shown
in Fig. 2. To overcome this problem, the method of Mobile
Anchor Positioning-Mobile Anchor & Neighbor
(MAP-M&N) is being adopted.
3.1.2 Forming additional Anchors and identifying the
Sensor Locations using MAP with Mobile Anchor &
Neighbor (MAP-M&N)
The location estimation done for sensors using MAP-M
method gives positions for few sensors and for the others, it
gives two positions and therefore it is the responsibility of
MAP-M&N method to produce outputs with a single position
for each sensor.
It is possible for the sensor nodes that have already
determined their location to assist other nodes in determining
their locations. As soon as the location is identified, the
localized nodes start acting like anchors. They embed their
calculated location inside the packet and then broadcast the
beacons.
Nodes, which are at its hearing range and waiting for
additional beacons to finalize their location, can make use of
these beacons. However, if the sensor node has determined its
location, it simply discards the beacon packet. By using
MAP-M&N method, the cost of movement of the mobile
anchor can be reduced.
The steps in finding the location of the sensors in the field
using MAP – M & N method are the following:
1. Deploy 100 sensor nodes randomly in the 1000 m x
1000 m area of the sensing field in the simulation environment
and deploy 3 location aware nodes (anchor nodes) i.e. sensor
nodes fit with GPS.
2. The assumption made is that Mobile Anchors move
throughout the sensing field according to the positional data
specified in the movement file which is given as input to the
NS2 simulator. The anchor nodes periodically broadcast their
location packets, which are known as beacon packets, while
on the move through the sensing field.
3. Every sensor node maintains a visitor list containing
beacon packets based on the information obtained from
anchors.
4. The sensor nodes can identify the farthest beacon
packets and chooses those beacon packets as beacon points.
5. With those two beacon points as the centers and the
communication range of a sensor node as radius, two circles
are constructed and the intersection points are found.
6. Sensor nodes try to identify its position out of the two
intersection points. Here, at least one of the beacon points in
the visitor list must lie outside the shadow region or based on
the beacon points obtained from neighbor nodes.
7. The approximate location for each of the sensor nodes
is estimated using the MAP-M&N method.
3.2 Mobile Anchor Positioning with Artificial Bee Colony
(MAP-ABC) Optimization Algorithm
The Artificial Bee Colony Optimization (ABC) [22]
algorithm can be applied for solving optimization problems.
Here ABC algorithm takes the results of MAP-M&N as the
input. The localization steps used in ABC algorithm are the
following:
1. The algorithm takes the results of MAP-M&N as its input.
The results of MAP-M&N, giving the approximate solution
of the location of each sensor at each specified time instance
is given as the input to the post optimization method.
2. Let m be the number of sensor nodes randomly placed for
each food source xi of employed bees using Eq. (1).
xij = minj + rand(0, 1)( maxj − minj) (1)
3. Now evaluate the position of the food source using
employee bees.
4. Produce new solutions υi in the neighborhood of xi for the
employed bees using Eq. (2)
vij = xij + φij(xij - xkj) (2)
Here, k is a solution in the neighborhood of i, φ is a random
number in the range [-1, 1], and j is the randomly selected
mobile sensor’s position.
5. Check υij for staying in the bounds of the area and apply the
greedy selection process between xi and υi.
6. After applying the greedy selection process for the sensor
nodes visited by the employee bees calculate the fitness value
for the sensor nodes.
7. Calculate probability values Pi for solutions xi by means of
their fitness values using Eq. (3).
Pi = [ 0.9 * (fiti / Fitbest) ] + 0.1 (3)
Here fiti is the fitness value of ith
solution and Fitbest is the
maximum fitness of the solutions.
8. Produce the new solutions, υi, for the onlooker bees from
solutions xi, selected depending on Pi, and deploy the
onlooker bees onto the respective food source.
9. Apply the greedy selection process for the onlookers
between xi and υi, until the position of the food source is
accurately found.
10. Memorize the best solution achieved thus far.

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11. Determine the abandoned solution and deploy the
scout bees to find new food sources so as to find its
position.
12. Repeat the procedure until the stopping criteria is met.
Stopping criteria = Maximum iterations or Profit
value Maximum iteration = arbitrarily chosen as 100.
4. SIMULATION RESULTS
The simulation settings used in ns-2 simulator for localization
using proposed approach when compared to MAP-M&N
algorithm is as shown in Table I.
4.1 Metric used to determine Localization Accuracy
The metric that is used to evaluate the localization accuracy is
Root Mean Square Error (RMSE). Equation (4) gives the
RMSE formula for the proposed evolutionary approaches,
Where, xact ( i), yact ( i) - represent the actual values of x and
y coordinates of the sensor nodes, xobt ( i) , yobt ( i ) -
represent the obtained values x and y coordinates of the
sensor nodes and N - represents the total number of Localized
nodes.
4.1.1 Comparison of RMSE obtained using MAP-M&N
and MAP-ABC approaches
The accuracy in localization can be evaluated based on
minimization in positional error. Root Mean Square Error
(RMSE) is calculated for MAP-ABC and MAP-M&N
approaches pertaining to every ten nodes scenario as listed in
Table II. The Table II shows the RMSE analysis of
MAP-ABC and MAP-M&N approaches corresponding to 10,
20, 30 etc. up to 100 nodes scenario which illustrates clearly
that RMSE gets drastically reduced on an average when
Artificial Bee Colony algorithm is used with MAP-M&N
algorithm (MAP-ABC) when compared to using only
MAP-M&N algorithm.
Table I: Simulation Settings
From the simulation results based on RMSE in Table II, it can
be summarized that the proposed Artificial Bee Colony with
Mobile Anchor Positioning (MAP-ABC) algorithm
minimizes the percentage of localization error significantly by
95.70 % when compared to using only MAP-M&N algorithm
on an average for 100 nodes scenario.
Table II: RMSE obtained for MAP-M&N and MAP-ABC
approaches
5. CONCLUSION
Mobile Anchor Positioning with Mobile Anchor & Neighbor
(MAP-M&N) algorithm uses range-free localization
mechanism that does not involve usage of any hardware. The
percentage of localized nodes is high which indicates that
MAP-M&N is appropriate for localization. Since this method
does not give fine-grained accuracy, Population based
optimization technique namely Artificial Bee Colony
algorithm is applied over the results of MAP-M&N. From the
simulation results based on RMSE, Mobile Anchor
Positioning with Artificial Bee Colony (MAP-ABC)
algorithm minimizes the percentage of localization error
significantly by 95.70 % when compared to MAP-M&N
algorithm on an average.
Thus, it is concluded that MAP-ABC algorithm minimizes the
localization error better than MAP-M&N. Hybridization of
optimization namely Simulated Annealing (SA) can be
combined to Mobile Anchor Positioning with Artificial Bee
Colony algorithm (MAP-ABC-SA) so as to reduce the
localization error further and the localization error of the
hybrid evolutionary algorithm can be compared with the pure
ABC algorithm (MAP-ABC) to validate its performance. The
future enhancement may be applying meta-heuristic
optimization approaches such as Glow worm swarm
optimization, fish swarm optimization etc. with mobile
anchor positioning to further minimize the localization error
significantly in wireless sensor networks.
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