Optimization Approach for Energy Consumption in Wireless Sensor Networks using Delay Aware Dynamic Routing Protocol

Abstract

An energy-efficient smart protocol design is a key challenging problem in Wireless Sensor Networks (WSNs). Some of the few existing energy-efficient routing protocols schemes always forward the packets through the minimum energy based optimal route to the sink to minimize energy consumption. It causes an unbalanced distribution of residual energy between sensor nodes, which leads to network partition. The prime goal of this approach is to forward packets to sink through the energy denser area to protect the nodes with less residual energy, which is maximizing the Sensor Networks Lifetime. The existing technique Energy Balanced Routing Protocol (EBRP) fails to achieve Throughput, End-to-End Delay, in order to improve the Network Performance. So the efficient routing protocol is needed with the capabilities of both the Energy Efficiency and Energy Balancing. To address these issues, we have proposed Delay Aware Energy Balanced Dynamic Routing Protocol (DA-EBDRP). The proposed routing technique achieves in terms of End-to-End Delay, Throughput, Portion of Living Node (PLN) and Network Lifetime. By simulation results the proposed algorithm achieves better performance than the existing methods.

Full text

International Journal of Emerging Trends in Engineering and Development Issue 7, Vol. 2 (March 2017)
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Optimization Approach for Energy Consumption in Wireless
Sensor Networks using Delay Aware Dynamic Routing Protocol
Dr.N.Kaleeswari1 and Dr.S.Sangeetha2 A. Jerwin Prabu3
Professor, Department of ECE, V.S.B. Engineering College Karur, India.
Professor, Department of CSE, V.S.B. Engineering College Karur, India.
Head of Technology, Department of Robotics, Bharati Robotic Systems, India.
Abstract
An energy-efficient smart protocol design is a key challenging
problem in Wireless Sensor Networks (WSNs). Some of the few
existing energy-efficient routing protocols schemes always
forward the packets through the minimum energy based optimal
route to the sink to minimize energy consumption. It causes an
unbalanced distribution of residual energy between sensor nodes,
which leads to network partition. The prime goal of this approach
is to forward packets to sink through the energy denser area to
protect the nodes with less residual energy, which is maximizing
the Sensor Networks Lifetime. The existing technique Energy
Balanced Routing Protocol (EBRP) fails to achieve Throughput,
End-to-End Delay, in order to improve the Network Performance.
So the efficient routing protocol is needed with the capabilities of
both the Energy Efficiency and Energy Balancing. To address
these issues, we have proposed Delay Aware Energy Balanced
Dynamic Routing Protocol (DA-EBDRP). The proposed routing
technique achieves in terms of End-to-End Delay, Throughput,
Portion of Living Node (PLN) and Network Lifetime. By
simulation results the proposed algorithm achieves better
performance than the existing methods.
Keywords - Wireless Sensor Networks, Delay Minimum,
Balancing Energy Consumption and Potential Filed, Energy-
Efficient Routing, End-to-End Delay, Throughput, Portion of
Living Node (PLN) and Network Lifetime.
1. Introduction
A. Wireless Sensor Networks (WSNs)
Wireless Sensor Networks are a series of sensors randomly
or evenly distributed across a vast area used to monitor
disaster areas, terrorist attack areas, forest fires etc. The
sensors are located at random locations and relay their
information to a central base that is usually far from the
region of sensor nodes. Sensors usually have a few basic
properties that come along with them: one or more sensors,
a radio transceiver for communication, a microcontroller
for computation and decision making and a battery for
energy.
The network should have the self-organizing capability
because the positions of individual nodes are not known
initially. Cooperation among the nodes is the main feature
of this network. The group of nodes cooperates to
distribute the gathered information to their neighbor users
in this network. The important application areas of the
sensor networks are the military areas, natural disaster and
in health. In addition, this network is used to monitor the
light, temperature, humidity and other environmental
factors for the civil applications.
B. Energy Balancing in WSN
Wireless Sensor Networks (WSNs) are installed and
deployed to carry out different applications, such as
Environmental Monitoring, Targeting, Industrial Control,
Disaster Recovery, Nuclear, Biological & Chemical attack
Detection Reconnaissance and Battlefield Surveillance. This
Wireless Sensor Networks are expected to play more
important role in the future generation networks to sense the
physical world [1,2,3,4,7].It is very well known that the
energy is the most serious and critical resource for battery-
powered Sensor Networks. To extend the lifetime of this
network as long as possible, the energy efficiency becomes
the most important parameter during the Protocol design. In
order to achieve and use the limited energy at sensor nodes
effectively, the recently proposed routing schemes are
attempting to find the minimum energy path to the sink, which
is used to optimize the energy usage at nodes.
From the literature survey, however, it is observed that to
focus on the efficiency of energy while designing protocols
for WSNs is not sufficient. And also identified that the
uneven energy depletion which is dramatically reduces the
lifetime of networks and decreases the sensors coverage ratio.
And Furthermore, these results in [4]point out that one hop
away from the sink will exhaust their energy level, there still
up to ninety three percent of initial energy left at these nodes
beyond away. And this imbalance of energy consumption
imbalance is certainly undesirable for the long-term strength
and health of the sensor network. These sensor nodes itself
consume their energy heavier evenly, then the connectivity
between these sensors and the sink could be maintained for a

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longer time and thus the network partition might be
postponed. This beautiful degradation of the network
connectivity could be obviously provided substantial gains.
And hence, it should be rational to make a suitable trade-off
between both the energy efficiency and the balanced energy
consumption.
2. Related Work
From our literature survey, it is noted that numerous
literatures focus on energy efficient routing protocols which
aim is to find an optimal best path to minimize energy
consumption either on local nodes or in the whole WSN [6, 7,
and 8]. However, some existing routing protocols have facing
the problem of energy imbalance.
A few Routing Protocols such as LEACH [9], EAD [10] and
HEED [11] offer energy balance within clusters by arbitrarily
choosing the cluster head, but however they are limited
solutions.
M. Singh and V. Prasanna in [12], define the energy-balance
property and then designed, proposed and evaluated an
energy-balanced algorithm for single-hop Wireless Sensor
Networks.
X. Wu, G. Chen and S.K. Das in [5] developed and proposed
a non-uniform node distribution strategy which is achieving
nearly balanced energy depletion.
The Energy-Aware Routing [13] was proposed by X. Wu and
G. Chen, and S.K. Das which focuses multiple paths, which
improves the network survivability. But from our literature
survey, it is noted that it quite consume energy to exchange
the routing information very frequently. Recently, the author
Fengyuan Ren and et.al, proposed an efficient Energy
Balanced Routing Protocol [4]. This scheme employs the
steepest gradient search method to decide the route. In this
Gradient-Based Routing, the gradient is a state demonstrating
the direction toward neighboring nodes through which the
destination could be reached. It could be established with
different parameters such as Energy Consumption, Physical
Distance, Hop Count, Residual Energy and Energy Density.
The aim of this scheme is to forward the packets to the
designated sink through the dense energy area in order to
protect the nodes with comparatively less residual energy.
They have proposed a novel routing scheme called Energy
Balanced Routing Protocol which does overcome the problem
of energy consumption imbalance is a serious issue in recently
proposed energy-efficient routing techniques, and it does
demonstrate the advantage of balanced energy consumption
between the sensor networks. However, this schemes
consumes more cost which leads End-to-End delay.
To address this type of routing loop problem, EBRP [1],
efficient enhanced mechanisms have been proposed to find
and eliminate loops. From our experimental results, it is
observed that this Energy Balanced Routing Protocol (EBRP)
improves Network Lifetime. But however, it is observed that
this work fails to achieve End-to-End Delay and Throughput.
This causes the Network Performance Degradation. To
address this major issue, in this research work, we have
proposed an efficient and effective mechanism called Delay-
Aware Energy Balanced Dynamic Routing Protocol, which
improves the Network performance in terms of End-to-End
Delay and Throughput. At the same time, this proposed work
holds all the positive features of Energy Balanced Routing
Protocol.
Chang and Tassiulas [16] proposed an energy
conserving routing protocol to maximize the system
lifetime by balancing the energy consumption among the
nodes in proportion to their energy reserves. These
proposed schemes embedded the energy awareness into the
protocol and were proposed for a homogenous ad hoc
network, where all the nodes are treated identical in terms
of functioning and available resources. In addition, those
schemes are suitable for static networks because the
benefits come from the even
Distribution of traffic among different nodes. When the
nodes are moving independently, the savings provided by
these algorithms, if any, is negligible because of the
difficulty of real-time re-configuration.
Sohrabi and Pottie [17] proposed a self-organization
protocol for wireless sensor networks. Each node maintains
a TDMA like frame, called super frame, in which the node
schedules different time slots to communicate with its
known neighbors. At each time slot, it only talks to one
neighbor. To avoid interference between adjacent links, the
protocol assigns different channels, i.e., frequency
(FDMA) or spreading code (CDMA), to potentially
interfering links. Although the super frame structure is
similar to a TDMA frame, it does not prevent two
interfering nodes from accessing the medium at the same
time. The actual multiple access is accomplished by
FDMA or CDMA. A drawback of the scheme is its low
bandwidth utilization. For example, if a node only has
packets to be sent to one neighbor, it cannot reuse the time
slots scheduled to other neighbors.
Piconet [18] is an architecture designed for low-power
ad hoc wireless networks. One interesting feature of
Piconet is that it also puts nodes into periodic sleep for
energy conservation. The scheme that Piconet uses to
synchronize neighboring nodes is to let a node broadcast its
address before it starts listening. If a node wants to talk to a
neighboring node, it must wait until it receives the
neighbor’s broadcast.
The paper is organized as follows. The Section 1
describes with overview of WSN and energy balancing in

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WSN. Section 2 deals with the related works. Section 3 is
devoted for the Existing Scheme EBRP. Section 4
describes the Proposed Algorithm Delay-Aware Energy
Balanced Routing Protocol (DA-EBRP) Section 5
describes the performance analysis and the last section
concludes the work.
3. Existing Algorithm – Energy Balanced
Routing Protocol (EBRP)
While designing an efficient routing protocols in Wireless
Sensor Networks, we need to focus an important two major
parameters namely energy balance and energy efficiency.
These two parameters are different attributes of routing
techniques design goal.
Fig. 1a. Deployment of Sensors Fig. 1b. Potential Field.
The energy-efficient routing protocol is trying to extend the
network lifetime in terms of energy consumption, whereas the
energy-balanced routing protocol aims to maximize the
network lifetime through even and uniform energy
consumption.
From experimental results, the former increases the network
partition which disables the network functioning, even though
there might be sufficient residual energy and the latter
focusses energy efficiency and it maximizes both the network
connectivity and network functioning. The Figures Fig.1a and
Fig.1b demonstrates the principles of EBRP and the Sensors’
Potential Field. From the Figure Fig.1b, it is noted that this
EBRP Scheme works to balance energy consumption.
3.1 Design Models and Properties of Energy Balanced
Routing Protocol (EBRP)
In this section, we are discussing the Design models and
various properties. i.e. This section focuses how to construct
potential fields through depth, energy density and residual
energy on each and every node, and how to integrate them
into a combined virtual potential field which will drive
packets to sink and at the same time this system has to focus
balanced energy consumption.
3.1.1 Design Models of Energy Balanced Routing Protocol
(EBRP)
Here, we are discussing various design models such as Depth
Potential Field, Energy Density Potential Field, Energy
Potential Field, and Hybrid Potential Fields.
Depth Potential Field
To provide the routing function such as how to move packets
toward the sink, an inverse proportional function of depth
which is as the depth potential field Vd. i.e. Vd (d) = 1/ (d+1),
where d = D (i) represents the depth of node i. The depth
potential difference Ud (d1, d2) from depth d1 to d2could be
defined as
)1)((1/()()()(),( 12211221  dddddVdVddU ddd
Since the potential function Vd(d) is monotonically
decreasing, while the packets in depth potential field go along
with the direction of the gradient, they can reach the sink
finally. The Depth of potential field is illustrated in fig.2.The
value of the depth difference between neighboring nodes will
be 0, 1, or -1 due to the nodes two or more hops away from a
node may not become its neighbors. Thus
Fig. 2. Depth Potential Field

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












1d if ,0
1d if ,0
d if ,0
),(
21
21
21
21 d
d
d
ddUd
The Depth potential field is shown in the Figure Fig.2.
Energy Density Potential Field and Energy Potential Field
The Energy Density Potential filed can be calculated with
Ved(i,t)=ED(i,t), where Ved(i,t) is the energy density potential of a
node I at time t. This ED(i,t) will be the energy density on the
current position of node i at a time t. Hence, the potential
difference Eed(i,j,t) from node i to a node j could be defined as
Ued(i,j,t)= Ved(j,t)- Ved(i,t) =ED(j,t)-ED(i,t). Similarly the Energy
Potential Field is Ve(i,j,t)= Ve(j,t)- Ve(i,t) =E(j,t)-E(i,t)
3.1.2 Properties of Energy Balanced Routing Protocol (EBRP)
The design and implementation details of this EBRP are
discussed in this Section. This EBRP is designed with the
various routing Control Message Signals such as Flag, Depth,
Energy and Energy Density, Distance with Received Signal
Strength Indicator (RSSI), Time to Update. The structure is
shown in the Figure Fig. 3. The various control signals of this
EBRP are discussed below.
3.1.2.1 Control Message
This is the format of the routing control message, which consists
of five parts is shown in Fig. 1. The flag field has 6 bits which is
reserved for extensions. This EBRP defines two types of control
messages where the first message is the normal updated message
and its type field is 00 and the second field carries the
information which is used by EBRP, comprising energy density
depth and residual energy. And the third is used to confirm
routing loops which is called as Check Loop Packet (CLP) with
the value of 01.
3.1.2.2 Depth
In this EBRP, initially, the depth of all nodes have been
initialized to 0xff and sink default depth is 0. The sink
first will send the update message, nodes which one hop away
from the sink could get their own depth level by adding one “1”
to the depth value to the update message. Similarly all the other
nodes will get their own depth by receiving did update message
from its neighbors. The procedure for depth calculation is shown
below.
Select the Lowest Depth LD from the Routing Table
If (Lowest Depth LD > LD+1)
{
setLocalDepth (LD+1)
}
3.1.2.3 Energy and Energy Density
The EBRP calculates residual energy of the local node with
feasible software. Here a Smart System is introduced to
measure and estimate the consumed energy while forwarding
packets. The System could log the actions that the local node
is being performed to evaluate the consumed energy through
battery model [14] which is published by the authors R.
Musunuri and J.A. Cobb. In this work, it is assumed that the
value of residual energy could be easily obtained from the
above identified method. This value will be forwarded to the
update message, and thus each node does know all its
neighbors residual energy and will be maintained them in the
routing table. Energy density of the local node could be
acquired by adding all residual energy of the neighbors in
routing table. Then it is dividing this sum by the area of
coverage disk.
3.1.2.4 Distance
The distance between two neighbors could be easily obtained
by various techniques, like signal attenuation evaluation or
estimation based on Received Signal Strength Indicator
(RSSI) [15]. This is noted that the distance used by EBRP
might be approximate.
3.1.2.5 Time to Update
The Energy Balanced Routing Protocol EBRP exchanges
routing messages to its direct neighbors. To maintain the
update pace, this EBRP states both the i. Least Updating
Interval (LUI) and ii. Maximum Updating Interval (MUI)
between the two consecutive update messages. The MUI is
continuously larger than LUI. If no messages from a neighbor
in two MUIs intervals, neighbor might be considered as dead
node. Thus this EBRP will again calculating the depth and
other values. And then this EBRP will send an updated
message with the following conditions.
 If the Maximum Updating Interval (MUI) Timer Expires
 If the Elapsed Time exceeds Least Updating Interval (LUI)
 Energy consumption exceeds a certain threshold
4. Identified Problem of the Existing System
EBRP
This Energy-Balanced Routing Protocol
(EBRP) is designed which focused and monitored the
Sensors Networks in terms of residual energy and energy
density. The aim of this approach is to forward packets to
sink via the dense energy area to protect the nodes with less
residual energy, which is maximizing the Sensor Networks
Lifetime. However, from our experimental results, it is noted
that this scheme finds a best energy density based route
between source and destination. But it doesn’t focus shortest
path, which degrades network performance in terms of
Throughput, End-to-End Delay, and Network Lifetime. That
is if this Energy Balanced Routing Protocol (EBRP) calculates

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route based on Energy Density and Shortest Path as well, the
network performance could be improved in terms of
Throughput, End-to-End Delay and Network Lifetime. Thus
this research paper designed and proposed an efficient Delay-
Aware Energy Balanced Dynamic Routing Protocol (DA-
EBDRP), which will improve the network performance in
terms of Throughput, End-to-End Delay and Network
Lifetime.
5. Proposed Delay-Aware Energy Balanced
Dynamic Routing Protocol (DA-EBDRP)
In Wireless Sensor Networks, it is important to save sensors
energy. Current research on routing in Wireless Sensor Networks
generally and mostly focused on energy aware and energy balanced
protocols like EBRP to maximize the lifetime of the network which
is discussed in the previous section.
The design of a smart delay sensitive shortest path and Energy
balanced routing protocol DA-EBDRP for WSNs should allow a
flexible trade-off between packet delay, the corresponding energy
consumption and energy density. The routing methodology in DA-
EBDRP is designed to take advantage of the EBRP and it will
focus the shortest path to find the shortest energy density based
route.
This proposed DA-EBDRP consists of three Phases. They are
 Energy Balanced based Shortest Route Finding
 Alternate Next Energy Balanced based Shortest Route
Finding
 Route Update
5.1 Energy Balanced based Shortest Route Finding
This is the first phase of the DA-EBDRP. The procedure to find
the shortest path based Energy Balanced Route of the proposed
DA-EBDRP is shown below. This procedure will discover the
best energy balanced based Delay Minimum optimal route.
Shortest Path: Short_Path ( )
Updating Message: u_Msg
Neighbouring Node: neighbor_ID
Packetdropratio: PDR_size
Link Capacity: Lin_Cap
CLK: Clk
BW: Bandwidth
Select Parent according to max_d, max-Um, max-Ued, max-Ue,
min-u_Msg.DEPTH, MIN-cost, Random
Short-Path () which finds & returns the shortest path of local node
Distance () which returns the distance of the neighbor;
Calculate Energy-Density () which calculates & returns the energy
density of local node;
UpdateRoutingTable () which updates the routing table;
SetLocalDepth () which sets the depth of the local node
Local-Energy_Density= calculateLocalEnergyDensity ();
d

= (Local_Depth+I+PDR_size) / u_Msg.Depth+1
Short_Path(Ud=
d

>1 ? 1-I/
d

-1)
Lin_Cap = Short_Path(RERR+CLK size)
ed

=u_Msg.Energy_Density/Local_Energy_Density
Shor_Patht(Ued =
ed

>1 ? 1 – I /
ed

:
ed

-1)
e

=u_Msg.Energy / Local_Energy
Short_Path(Ue=
e

>1 ? 1-I/
e

-1)
Um=(1-α –β). Ud+ α Ued+ β Ue
COST = Distance(neighbor_ID)
D=Um/COST
Lin_Cap = BW+CLK Size - RERR
updateRoutingTable(neighbor_ID)
select the Lowest Depth from the routing table as LD
1. if (Local_Depth > LD +1) then
2. setLocalDepth(LD +1)
3. Endif
5.2 Alternate Next Energy Balanced based Shortest Route
Finding
This is the second phase of the proposed work. In this phase,
alternate shortest path Alternate_Sort_Path will be identified
by the sub procedure Alternate_Sort_Path()and it will be
updated in the Route Update Table. If any nodes under the
current working path are reached the lowest energy threshold
level, that route will be blocked and the Alternate_Short_Path
will be activated and subsequently Alternate_Sort_Path() will
be called and another Alternate_Sort_Path will be discovered.
This procedure will improve the reliability of the Sensor
Networks and it will improve the network lifetime also.
5.3 Route Update
This phase is used to update the new route if the working
route is getting down. This improves reliability and
Throughput without delay.
5.4 Pseudo code of Proposed Delay-Aware Energy Balanced
Dynamic Routing Protocol (DA-EBDRP)
The above process is controlled by a novel and reliable route
monitoring and selecting technique which is shown below.
CALL Short_Path (calculateLocalEnergyDensity ())
Select optimal route
FOR there are sensing data to be sent DO
BEGIN
IF Route Functioning Well
CALL data delivery
ELSE IF
CALL Alternate_Short_Path (calculateLocalEnergyDensity
())
Select Alternate optimal route
END
6. Performance Analysis and Discussions
The performance of our proposed DA-EBDRP is
implemented with QualNet4.5 Simulator and DA-EBRP is
thoroughly studied and evaluated in this section in terms of in
terms of End-to-End Delay, Throughput, Portion of Living

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Node (PLN) and Network Lifetime. It is also compared with
the recently proposed Energy Balanced Routing Protocol
(EBRP) and established that our proposed work is performing
well as compared with the existing one in terms of
Throughput and End-to-End Delay and this proposed work
retains all the positive features of Portion of Living Node
(PLN) and Network Lifetime.
Fig. 4.Route Discovery Delay DA-EBRP vs EBRP
From the Figure Fig. 4, it is noted that our proposed DA-EBRP
discovers the shortest path earlier as compared with the EBRP.
It is happening because, our work continuously pre-calculating
the alternate best shortest path which is maintained by route
update table, while communication takes place in the current
shortest path. It is also observed that as density of nodes
increases, the route discovery time approaches EBRP because
considerable time is required to calculate the Energy Density in
condensed nodes.
Our proposed work’s prime objective is to discover the best
shortest path with sufficient energy density to forward packets
to sink. That is the reason why, our method consumes less End-
to-End Delay to forward the packets to destination as compared
with EBRP, which is shown in the Figure Fig. 5.
Fig. 5.End-to-End Delay DA-EBRP vs EBRP
This approach improves the throughput level of the system, which is
shown in the Figure Fig. 6. But however, from the Figure Fig. 6,
observed that as the sending packets are more, our work chooses
route based on the energy dense nodes and hence the throughput of
our work is almost equal to EBRP for higher volume of data
communication.
As far as the Network Life Time is concerned, our proposed work
couldn’t achieve higher performance for low density nodes. But
however, from the Figure Fig. 7, it is established that our proposed
work maintains the feature of EBRP when sensor network has more
than 200 nodes.
From the Figure Fig. 8, it is observed that our proposed work utilizes
all the nodes of Sensor Network and hence our work achieves more
living nodes as compared with EBRP for long time simulation. But
however, for short time of execution, it couldn’t retain more nodes
like EBRP because our work focuses energy density and shortest
path as well for communication.
Fig. 6.Throughput DA-EBRP vs EBRP
Fig. 7.Network Life Time DA-EBRP vs EBRP

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Fig. 8.Portions of Living Node DA-EBRP vs EBRP
7. Conclusions
In Wireless Sensor Networks, recharge or
replace the batteries of the nodes may not be possible. That is
the energy is a very precious resource and hence energy-
efficient protocol design is a major challenging issue. We
have discussed various energy-efficient routing protocols and
discussed their issues. To overcome these major issues, an
efficient Energy-Balanced Routing Protocol (EBRP) is
designed. In this work, we were analysed its pros and cons.
ie from our experimental results, we established that the
EBRP fails to achieve Throughput and End-to-End Delay. To
achieve these identified issues, we have developed Delay-
Aware Energy Balanced Routing Protocol (DA-EBRP). We
have thoroughly studied and investigated this proposed
routing technique and compared with EBRP interms of End-
to-End Delay, Throughput, Portion of Living Node (PLN) and
Network Lifetime. From our experimental results it is
established that this proposed work outperforms Energy
Balanced Routing Protocol (EBRP) interms of End-to-End
Delay and Throughput, which improves the Network
Performance. Further, we revealed that this proposed work
retains all the qualities of EBRP interms of Portion of Living
Node (PLN) and Network Lifetime.
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Networks: A Survey” journal of communications and
networks, vol. 18, no. 5, october 2016.
[20].Evangelos Dimitris Toumpakaris, Adrian Munteanu,
and Nikos Deligiannis, IEEE Member”Multiterminal
Source Coding With Copula Regression for Wireless
Sensor Networks Gathering Diverse Data” IEEE
Sensors Journal, Vol. 17, No. 1, January 1, 2017
AUTHORS PROFILE
N.Kaleeswari received her B.Sc degree
in Electronics from Bharathiar
University, India in 2000, M.Sc degree
in Applied Electronics from Bharathiar
University, India in 2002, M.E. degree
in Electronics and Communication
Engineering (Applied Electronics) from
Anna University of Technology –
Coimbatore, India in 2010 and she is
currently pursuing full time Ph.D. degree in Electronics and
Communication Engineering from Anna University of
Technology –Coimbatore, India. Her research interests include
Wireless Sensor Networks, System Networking, Digital
Communication, Adhoc Networks and Pervasive Computing.