Maintain load balancing in wireless sensor networks using virtual grid based routing protocol

2018 International Conference on Advanced Science and Engineering (ICOASE), Kurdistan Region, Iraq
227
978-1-5386-6696-8/18/$31.00 ©2018 IEEE
Maintain Load Balancing in Wireless Sensor
Networks Using Virtual Grid Based Routing Protocol
Husam Kareem
Directorate of Baghdad
Education, Karkh 3
Ministry of Education of Iraq
Baghdad, Iraq
husam.kar@gmail.com
Hadi Jameel
Department of Computer
Techniques Engineering
AL-Mustafa University College
Baghdad, Iraq
hjhk8891@gmail.com
Abstract— Based on the wide variety of applications of
wireless sensor networks (WSNs) in different aspects of life,
research focusing on WSNs have rapidly increased in the recent
few years. Different challenges shorten the operation of sensor
nodes over the targeted area for different reasons such as danger,
inhospitality, and limited energy resources of the surrounding
area. One major issue is the energy required to operate the
individual sensor nodes that definitely affect the operation of the
entire sensor network. Accordingly, energy consumption must be
minimized as possible which requires to compromise sensor
network activities as well as network operation. One
fundamental solution commonly used for minimizing the energy
consumption in each sensor node is using an energy-efficient
routing algorithm. In this study, a routing approach depends on
the grid topology of the sensor network is presented to maximize
the lifetime of WSNs via balancing a load of data traffic among
sensor nodes as evenly as possible. The evaluation process is done
using CFDASC routing protocol since it represents the most
comparable and related algorithm among previous work.
Simulation results prove that the presented approach
outperformance CFDASC algorithm in terms of network
stability and load balancing of the entire network.
Keywords—grid-based, wireless sensor network, cell-head, load
balancing, the stability period
I. INTRODUCTION
Generally, a number of spatially distributed sensor nodes
each of which contains four major units: a power source,
processor, transceiver, and sensor constructs a wireless sensor
network [1]. Furthermore, Wireless sensor networks could be
utilized in numerous applications such as, sensing and
collecting information from the physical environment that
usually unable to reach by humans. Thus, in such conditions,
sensor nodes are required to survive for a longest possible time
at the concerning zone. An essential issue that highly probable
to challenge the survival of sensor nodes is the limited
resources specifically power source (usually small battery).
Therefore, when the power source (battery) is depleted the
network will be losing sensor nodes continually and it is
difficult to replenish died sensors [2]. The reasons that sensor
nodes characterized by such feature is that sensor nodes are
basically static. Moreover, in regular conditions, sensor nodes
are delivered in an inhospitable area or placed in a physical
structure that makes them left unattended.
Therefore, energy consumption is a very critical factor that
should be managed wisely to extend the network lifetime
during the duration of time for the specified mission. Energy
consumption in WSNs can be divided into three different
reasons: processing, sensing and communications [3].
This research will be mainly focusing on the
communication aspect since it consumes a great portion of the
sensor nodes energy [3]. Specifically, the way that the sensor
network collects the data from the sensing field and delivers
that data to the base station. In other words, the routing
algorithm that is used to transfer data within the sensor
network (intra-network communications) and transfer the data
from the sensor network to the base station (inter-network
communications).
Thus, using a routing algorithm that able to retain sensor
network energy and provide an even energy consumption
among all sensor nodes can be a perfect solution to keep an
efficient and stable operation of WSNs as long as possible.
Considering that sensor networks performance reaches its best
when all sensor nodes are alive and functioning. If sensor
nodes start dying, empty zones will appear and there will be a
lack in the required sensing data, which leads the entire WSNs
unreliable and insufficient.
To settle the prior mentioned issue, we present a routing
algorithm that pursues to prolong the lifetime of a balanced and
stable WSNs called Virtual Grid Based (VGRP). The VGRP
algorithm splits the area that involves sensor nodes into a grid
of sub-cells with equal size then collects the sensed data using
cluster and chain techniques.
II. RELATED WORK
Incalculable studies have been carried out to develop the
approach of routing the sensed data of WSNs. Among the wide
number of approaches, some relevant will be explored for
either routing algorithms that aiming to achieve load balancing
in WSNs or routing algorithms for grid-based structure WSNs.
This is due to the proposed algorithm belongs to Grid-based
hierarchical routing algorithms, and aims to achieve load
balancing in WSNs. It is worth mentioning that hierarchical
routing algorithms are classified into three main sets:
clustering, chaining, and hybrid routing algorithms [4].

228
EEGDG [5] is a grid-based routing algorithm that combines
clustering and chain techniques which make it considered as
hybrid routing algorithm. EEGDG algorithm presented to be an
energy efficient and consequently, prolong the lifetime of
wireless sensor networks by decreasing the hops happened in
the standard chain routing algorithms like PEGASIS [6]. In
spite of the fact that EEGDG algorithm could achieve its target
by increasing the network lifetime but it did not make an
optimal solution for the delay in data delivering that occur due
to the long chain.
An energy efficient load balancing algorithm (EELBC)
presented in [7] depending on clustering to gain longer lifetime
and load balanced WSNs. In this algorithm, it is assumed that
each sensor node is equipped with a GPS unit in order to be
aware of its position. EELBC works within two stages;
bootstrapping and clustering. This algorithm successfully
achieved longer lifetime and more load balanced wireless
sensor network but its main flaw is when they assumed that
there are supernodes to act as clusters heads which are less
energy-constrained compared with other nodes in the network
which act as clusters members. Therefore, this algorithm
cannot be considered flexible or applicable for many
applications since its complex assumptions that require optimal
situations.
GBDAS [8] divides the sensing field into a grid of cells,
each cell consists of a number of sensor nodes. GBDAS
algorithm operates in three phases; grid construction, chain
formation, and data transmission. The fundamental objective of
this algorithm is to decrease the number of dead nodes during
the network lifetime. This algorithm collects the sensed data
from by using a cell head for each individual cell the forming a
very long chain that includes all cell heads in order to gather
the sensed data for the entire network. After collecting the data
within the chain, a chain head which already selected based on
its residual energy will be responsible for sending the collected
data to the base station. This algorithm can be considered as a
successful algorithm for the applications that require a small
sensing area since the chain of cell-heads will be not so long
and consequently the delay in data gathering and delivering can
be compromised with less energy consumption. In contrast,
when the application requires a large sensing area, GBDAS
will show a big delay in data gathering and delivery due to the
very long chain of cell-heads.
In the mobile sink scheme, a virtual grid based dynamic
routes adjustment (VGDRA) is proposed to reduce the routes
construction cost of sensor nodes [9]. This algorithm divides
the sensing field into a number of uniform cells that depends
on the total number of sensor nodes. To calculate the desired
number of sub-cells and thus the number of cluster heads they
utilize the same heuristics of the LEACH algorithm [10] which
consider a 5% of the total number of sensor nodes. VGDRA
algorithm uses the nodes that have the least distance to the
center of each cell to operate as a cluster head. However the
technique of selecting the cluster head at each cell can
distribute energy consumption evenly among cluster members,
it makes the nodes located near the cells centers die very
quickly. Since the cluster head node consumes much more
energy than the normal members due to receiving and
aggregating data from all cluster members and then
transmitting the aggregated data to other cluster head in the
network. In addition to this issue, implementing a mobile sink
approach is not convenient for most of the application.
Enhanced chain-cluster based mixed routing algorithm (E-
CCM) [11] has introduced to maximize the lifetime and
minimize energy consumption of wireless sensor network. E-
CCM operates based on grid topology and works within two
stages; the initialization stage and transmission stage. E-CCM
algorithm considered as hybrid hierarchical routing algorithm
since it is built depending on chain and cluster techniques. This
algorithm achieves its goals perfectly by outperforms the
previous related work like the CCM algorithm [12]. Although
the E-CCM algorithm can only function with a uniformly
predetermined distribution of sensor nodes it is not designed to
be applicable for applications that require a random
distribution of sensor nodes.
Based on the study of literature, it can be seen that majority
of related work has focused on increasing the network lifetime
without taking in consideration balance the load evenly among
sensor nodes. Moreover, some of the previous algorithms used
complex assumption like supernodes (nodes with unrestricted
energy source).
III. PROBLEM STATEMENT
The architecture of wireless sensor network can be
described as a set ‘S’ of nodes. We assume that each sensor
node is aware of its location using a GPS unit. The goal of
using hierarchical schemes; chain, cluster, and hybrid, is to
create a virtual structure that facilitates and ease managing data
routing in wireless sensor networks. One of the goals of
hierarchical routing algorithms is load balancing among sensor
nodes.
Majority of Grid-based routing algorithms is directly or
indirectly rely on clustering technique. The goal of clustering is
to select a set of sensor nodes to be cluster heads that cover the
whole network. The cluster head would be responsible for
receiving and aggregating the sensed data from all cluster
members and then forward the aggregated data either to the
base station or to the next cluster head based on the routing
mechanism. Therefore, the cluster head consumes more energy
compared to the energy consumed by a normal cluster member.
The problem of load balancing happens when a group of
sensor nodes is repeatedly selected to operate as cluster heads.
As a result, those nodes will die very fast and the death of
nodes will cause empty gaps within the entire network. Those
gaps not only can affect the validity of the sensed data but also
can affect the communication of the WSNs which are mainly
built upon multi-hop communication. Thus, the proposed
algorithm aims to keep the load evenly distributed among
sensor nodes which lead to retain all sensor nodes alive and
functioning as long as possible.
IV. VGRP SCHEME
Here, will outline the characteristics of the proposed
approach Virtual Grid Based routing protocol (VGRP) which
aims to keep the robustness and validity of wireless sensor
network as long as possible by balancing the load evenly

229
among all sensor nodes. VGRP significance appears during the
selection of the node that collects and aggregate data from
other nodes to avoid overloading specific nodes rather than
others. VGRP algorithm tried to achieve this goal by utilizing
the factor of remaining energy of individual nodes to decide
upon their selection of being a cluster or chain head. The
closest related and highest comparable work like Chain-based
fast data aggregation algorithm based on suppositional cells
has gained a substantial performance in term of transmission
delay [13].
However, the remaining energy of each sensor node that
selected to act as a cluster or chain head has not taken into
consideration. Therefore, a node with almost depleted energy
might be selected to represent the head for a group of sensors
while a node with much higher energy operates as normal
member node that requires only a few amounts of energy to
transmit its data to the group head. The proposed algorithm
VGRP distribute the duty of being a group head (chain or
cluster) by taking into consideration its remaining energy. The
operation of the VGRP algorithm can be divided into two main
stages; virtual grid setup and data transmission.
A. Virtual Grid setup
The network topology is initialized based on dividing the
sensing field to a virtual grid topology with equal size cells.
The field axes are equally divided and numbered according to
the number of cells, which are located along each field-axis.
For instance, if there are three cells on the x-axis, then the x-
axis is numbered as 0, 1, 2 and if there are five cells then the
coordinate will be numbered as 0, 1, 2, 3, 4 and so on.
Therefore, the numbers on the x-axis can be considered as the
column number while the numbers of the y-axis represent the
row numbers.
Fig. 1. Virtual grid construction
Each cell within the grid has a unique cell ID called (CID).
The group of sensor nodes that fall into a specific cell can
determine its unique cell-ID based on (1).
CID = [Cx, Cy] ()
Where Cx and Cy represent the cell coordinates on the x-
axis and y-axis respectively. Fig.1 clarifies an example of
sensing field divided into a virtual grid of 5 × 5 equal cells. For
instance, the IDs for the first row and from left to right IDs will
be [0,0], [1,0], [2,0], [3,0], [4,0] and so on so forth.
B. Head nodes selection and data transmission
In order to collect, aggregate, and send the sensed data to
the base station, nodes called cell-heads and chain heads act as
an intermediary between the non-head nodes and the base
station. At the first sensing round, all nodes have the same
energy hence cell-head nodes will be randomly selected. Then,
during the next sensing rounds, the node that has the
maximum residual energy will be functioning as the cell-head.
After the data get collected in the cell-head nodes, a chain
head is selected depending on the same basis of selecting the
cell-head, which will be responsible for gathering the data
vertically. The job of the head nodes is to collect the sensed
data from its nodes, aggregate with its own data and then
forward it either to the next head node using multi-hop
communication or send it directly to the base station. The
process of selecting cell-head nodes, forming a chain that
consists of a set of cell-head nodes, and finally select a chain
head for each individual chain is accomplished by the base
station. In VGRP algorithm, data transmission is done in steps;
within the individual cells (intra-cell data transmission) based
on clustering technique and between the cell-heads (inter-cell
data transmission) based on chain technique. Therefore, there
will be two types of head nodes which are cell-head and chain
head nodes. Fig.2 shows the flowchart of virtual-grid setup
process as well as the intra-cell communications.
Fig. 2. Flowchart of virtual-grid setup and the intra-cell communications
Intra-Cell Communications: The main structure of the
VGRP algorithm is to divide the sensing area to a virtual grid
with equal size cells. After constructing the grid and assigning
each sensor node to its corresponding cell, a cell head for each
group of nodes that belong to the same cell is selected based
on its remaining energy. The cell head takes charge of

230
gathering the sensed data packets from all non-head nodes that
belong to the same cell then aggregate it with its own data and
then forward it to the next cell-head node based on multi-hop
communications. Fig.3 illustrates network structure after
forming the grid of cells and selecting a head node for each
individual cell.
Fig. 3. Intra-cell communications
Inter-Cell Communications: When all data packets are
collected within the cell-head nodes, those head nodes will
form a chain that includes all cell-heads that fall in the same
column. Nodes with maximum remaining energy take the role
of being chain head of their respective chains. Data
transmission is done using the greedy algorithm like in pegasis
[6]. Each sensor node sends its own data packet to its neighbor
node (adjacent sensor node toward the chain head). The
neighbor node receives the data packet and aggregates it with
its own data packet then transmits it to its neighbor and so on
so forth till data are all collected in the chain head node. After
that, the chain head receives its neighbors' data, aggregates it
with its own data and then forwards it to the base station. Fig.4
clarifies the procedure of this process while Fig.5 is a
flowchart that demonstrates the vertical chains formation and
the inter-cell communications.
Fig. 4. Inter-cell communications
Fig. 5. Flowchart of vertical chains formation and inter-cell communications
V. NETWORK MODEL
The sensing field is represented by a 2-D area which is
equally partitioned into identical size cells. The base station is
fixed and placed outside, far from the sensing field. 50 and 100
sensor nodes respectively are distributed randomly over the
sensing area. In order to give comprehensive details about
network system model, it is divided into two main sections that
include the basic assumptions used in the simulation software
and energy model utilized to determine energy consumption
due to the transmitting of data packets.
A. Basic Assumptions
A number of basic assumption must be decided before
proceeding with the simulation.
• All nodes have awareness of their geographical location
based on GPS unit.
• All nodes are immobile after the deployment.
• All nodes are of homogeneous energy (all nodes
contains the same initial energy) [14].
• Each sensor node is aware of its remaining energy.
• The amount of energy consumption due to data transfer
from point X to point Y is the same as when the transfer
is from point Y to point X.
• Energy consumption due to aggregation of data packets
is equal to 5nJ/bit/packet [15].
For the first assumption, it is assumed that each sensor node
is aware of its location to help the sensor nodes to determine
the cell that they belong to and then organize themselves into
clusters within the corresponding cells. As the second

231
assumption, the vast majority of wireless sensor networks
applications require stationary sensor nodes [16]. For the
simplicity of the simulation process, sensor nodes are assumed
homogeneous. Moreover, sensor nodes are aware of their
remaining energy and the location of the base station, then the
remaining energy factor can be utilized to make the decision of
choosing the cell-head nodes and later the decision of choosing
chain-head nodes.
B. Energy Model
In order to evaluate the consumption of energy due to data
transmission and receive, the first order radio transceiver is
used in this design [6], [11], [16], [17]. Equations (2) and (3)
are used to determine the energy cost due to data receiving and
due to data transmitting respectively.
Energy consumption due to data receiving
Erx(k)= Eelec × k ()
Energy consumption due to data transmission
ETx(k, d)= Eelec × k + Eamp × k × d 
()
Where Eelec represents the energy required to run the
transmitter or the receiver, k represents the size of data packets,
Eamp stands for the amount of consumed energy to run the
amplifier, and d is the distance of transmission. Moreover,
there are some fixed parameters used through the simulation
process which is shown in Table I.
TABLE I. SIMULATION PARAMETERS
Simulation parameters Subhead
Area of the sensing field (50×50) m2 & (100×100) m2
Basic station location (25,75) & (50,150)
Number of sensor nodes 100
Sensor node's initial energy 0.5 Joule
Eele 50 nJ/bit
Eamp 100 pJ/bit/m2
Data packet size 2000 bit
VI. RESULTS AND ANALYSIS
VGRP and CFDASC performance are evaluated based on
two crucial performance metrics; load balancing, and network
stability.
A. Load Balancing
The load balancing metric represents the proportion of the
residual energy of the entire network when the first node dies.
The performance of network evaluated based upon this metric
has an inverse relationship which means that the network with
minimum residual energy when the first node dies is the
network that shows the best load balancing [17], [18]. In other
words, when the first node of a network dies and the network
only has a few portions of the remaining energy. Consequently,
it means that the rest of all alive nodes have reached to the
minimum amount of energy that makes them alive. In addition,
it also means that they are almost dead not in the same round of
the first node but might die the next round or after a few rounds
(in maximum). Based on the above explanation, we can see
that when all nodes die at the same time or even at very close
period just because they consumed the same amount of energy
during their operation. Which also means that each sensor node
in the network has a burden of almost the same amount of a
load of data packets traffic.
The total residual energy of the entire network when the
first node dies using (50 × 50) m2
& (100 × 100) m2
sensing
area is shown in Table II and Table III respectively. The
routing algorithm with the lowest parameter has the best
performance in term of load balancing [17], [18].
TABLE II. TOTAL REMAINING ENERGY WHEN FIRST NODE DIES USING
(50×50) M2 SENSING AREA
Routing algorithm
Total remaining energy
(Joule)
Percentage of
remaining energy
VGRP 10.4 20.8%
CFDASC 22 44%
TABLE III. TOTAL REMAINING ENERGY WHEN FIRST NODE DIES USING
(100×100) M2 SENSING AREA
Routing algorithm
Total remaining energy
(Joule)
Percentage of
remaining energy
VGRP 16.92 33.84%
CFDASC 29.62 59.24%
It can be clearly seen that the proposed algorithm VGRP
outperforms CFDASC algorithm in term of load balancing in
both (50 × 50) m2 & (100 × 100) m2 sensing area.
B. Stability period
Stability period of wireless sensor network represents the
period of time starting from the first sensing round till the
death of the first sensor node [17], [18]. Therefore, without
long-term stability period, more data could not be gathered
from the sensing field even if the network lifetime is high.
Because of that, extending the stability period is crucial for
many applications of wireless sensor networks. Fig.4 shows the
stability period of the sensor network using (50 × 50) m2
&
(100 × 100) m2
sensing area respectively.
Fig. 6. Stability period using different sensing area

232
As demonstrated in Fig.6, VGRP algorithm shows better
performance in term of network stability period using different
sensing area. The improvement of VGRP over CFDASC
algorithm is almost 40.3% using (50 × 50) m2
sensing area
while it is about 51.5% using (100 × 100) m2
. Equation (4) is
used to determine the percentage of improvement VGRP over
CFDASC algorithm [11].
POI = (Second Value-First Value)/First Value×100% ()
Where POI stands for the percentage of improvement,
hence, in this performance metric, it shows the improvement
percentage in network stability period. The First value refers to
the sensing round when the first node dies using CFDASC
algorithm. Second value stands for the sensing round when the
first node dies using VGRP algorithm.
VII. CONCLUSION AND FUTURE WORK
In this research, a Virtual Grid-Based routing protocol
(VGRP) has presented to maintain load balancing and increase
stability period of wireless sensor networks. VGRP algorithm
divides the sensing area to a grid of cells then treat each cell as
an independent cluster. It works within two stages; virtual grid
setup and data transmission. In addition, a sensor node, which
has maximum remaining energy, is selected to function as a
cluster head during intra-cell data transmission and inter-cell
data transmission. The evaluation process is done through a
simulation program using area (50×50) m2
& (100×100) m2
respectively to check the validity of the proposed algorithm
with different sensing area. Simulation results show that VGRP
algorithm outperforms CFDASC algorithm in both
performance metrics utilized in this study; network load
balancing and network stability period. Moreover, in term of
stability period, VGRP algorithm shows an improvement over
CFDASC algorithm about 40.3% and 51.5% using (50×50) m2
and using (100×100) m2
sensing areas respectively.
As part of our future work will try to implement the
proposed algorithm in real-life applications, specifically, in
home automation systems that involve sensors in their
operations [19]. In addition, the performance of the proposed
algorithm will be investigated using other performance metrics
such as end-to-end delay and network throughput. Moreover,
different communication approaches will be explored such as
Zigbee and LiFi technologies [20]
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