Design and implementation of new routing

International Journal of Computer Networks & Communications (IJCNC) Vol.7, No.4, July 2015
DOI : 10.5121/ijcnc.2015.7409 127
DESIGN AND IMPLEMENTATION OF NEW ROUTING
STRATEGY FOR ENHANCED ENERGY EFFICIENT IN
WSN
Mehdi Bouallegue1,2
, Hager Benfradj3
, Kosai Raoof1
and Bouallegue Ridha3
1
Laboratory of Acoustics at University of Maine, LAUM UMR CNRS n 6613, France
2
System of communication SysCom, ENIT, Tunisia
3
Innovation of communication and cooperative mobiles, InnoV’COM Lab, University of
Carthage,Tunisia
ABSTRACT
Energy consumption is a key element in the Wireless Sensor Networks (WSNs) design. Indeed, sensor nodes
are really constrained by energy supply. Hence, how to improve the network lifetime is a crucial and
challenging task. Several techniques are available at different levels of the OSI model to maximize the WSN
lifetime and especially at the network layer which uses routing strategies to maintain the routes in the
network and guarantee reliable communication. In this paper we intend to propose a new protocol called
Combined Energy and Distance Metrics Dynamic Routing Protocol (CEDM-DR). Our new approach
considers not only the distance between wireless sensors but also the energy of node acting as a router in
order to find the optimal path and achieve a dynamic and adaptive routing.
The performance metrics exploited for the evaluation of our protocol are average energy consumed,
network lifetime and packets lost. By comparing our proposed routing strategy to protocol widely used in
WSN namely Ad hoc On demand Distance Vector(AODV), simulation results show that CEDM-DR strategy
might effectively balance the sensor power consumption and permits accordingly to enhance the network
lifetime. As well, this new protocol yields a noticeable energy saving compared to its counterpart.
KEYWORDS
Energy saving, Routing protocol, lifetime, WSN, power consumption, Sensor node, NS2.
1. INTRODUCTION
In the last few years, wireless sensor networks have increasingly attracted considerable attention
among researchers in the field of Telecommunications. They are considered as one of the most
active areas of technology development due to their unique characteristics, low cost, easy
deployment and flexibility [1].
WSN help human to perform many tasks such as habitat monitoring, industry application,
collaborative and distributed computing, military, agriculture, emergency operations and health
care application [2] [4]. Although wireless sensor network is employed in various fields, it has
many constraints such as limited storage capacities, limited communication abilities and
especially limited energy resources due to the finite battery-power available [5]. In this type of
networks, each wireless sensor is able of acting as a router along with being a source node or
destination node. Hence, when sensor cannot achieve correctly its task, the performance of WSN
can be greatly impeded and eventually the basic availability of the network such as routing
approach can be affected.

128
On the other hand, reload or replace sensor nodes battery after their exhaustion is very difficult
especially in unreachable areas such as desert or battlefield [6]. So a careful energy resource
management is needed to increase the lifetime of wireless sensor networks. Substantial researches
have been carried out to analyze and overcome the constraints of WSNs.
Hence, several routing protocols have been designed and implemented to improve the
performance of these types of networks.
Indeed, routing protocols play an important role in WSNs. They permits to determine the optimal
path to a destination, ensures successful connectivity and guarantee reliable communication. But
the problem in existing energy aware communication protocols is that they try to find an optimal
path and then repeatedly use this best route for every communication, which conducts to rapid
energy diminution of the wireless sensors on the selected path. Substantial researches have been
carried out to analyze and overcome the constraints of WSNs. Hence, several routing protocols
have been designed and implemented to enhance the performance of these types of networks.
Authors in [8] proposed a new protocol called MAODV derived from AODV mechanism. Their
idea was to take into account the bit error rate as the metric to be reduced for route selection. The
simulation results showed that MAODV improves the packet delivery ratio at the cost of a delay
increase.
In addition, in [9] authors considered the transmit power control as a metric to improve the
performance of AODV routing technique. The same authors proposed in [10] a new strategy to
set a timeout for a path in order to remove the stale paths after a certain timeout period and
minimize the number of control packets. Hence, this approach permits to reduce the power
consumption of the network. A small change in the traditional AODV protocol which integrates
local routing of intermediate sensors in order to improve energy consumption of the network is
proposed in [11] and called E-AODV approach.
Among these works, most of them just integrated one cost metric (as energy or BER or transmit
power, etc.) to optimize the energy consumption of the wireless network.
In this paper, we propose a new routing strategy which considers not only the distance between
wireless sensors but also the energy of node acting as a router in order to find the best path and
achieve a dynamic and adaptive routing to increase network lifetime as long as possible.
Performance analysis of both reactive and proactive routing protocols namely DSR, AODV,
DSDV was studied in our earlier work [3] on the basis of various performance metrics and under
various traffic scenarios. Through extensive simulations, we deduced that AODV and DSR
yielded better performance than the DSDV even when the network has a large number of sensor
nodes. The results also disclose that AODV routing technique becomes more effective in
providing better performance when the studied metrics are simulated. So we concluded that
AODV technique can be considered as the most energy efficient protocol.
In this regard, we suggest a new energy efficient communication protocol for wireless sensor
networks based on the AODV platform. The main goal of our approach is to enhance the network
lifetime as well as discover the optimal path from the source sensor to the destination based on
combination of two most important metrics to evaluate the optimal path namely: distance relative
to the sink and energy available in each sensor node acting as router. Our new algorithm extends
and optimizes the routing AODV approach.

129
This work is arranged into five sections. Section II covers a brief overview of routing
strategy. Section III describes the considered performance metrics. Section IV provides a detail
description of the design and implementation of our proposed routing approach. In section V we
compare the performance of CEDM-DR protocol with its counterpart routing technique. Finally
the conclusion of the work and future directions are provided in the last section.
2. OVERVIEW OF WSN ROUTING PROTOCOL
In wireless communication we can distinguish two categories. The first needs to have direct
access to the base station (BS) for the transmission of communications. While the second has the
opportunity to access to the BS via the intermediate nodes using a communication hop by hop
[17]. The most important problem for an ad-hoc network is the delivery of data packets between
the mobile nodes. Since the node topology changes frequently this makes routing very
problematic.
Low bandwidth, limited battery capacity, and proneness to errors add to the complexity of the
design of an efficient routing protocol. A routing technique in WSN presents many challenges
compared to data routing in wired networks.
Indeed, the choice of the route is done by routing algorithms. Different routing methods are
proposed for wireless sensor networks. These protocols are classified according to many
parameters and to the strategies of discovering and maintaining routes.
Protocols can be classified [20] as reactive, proactive and hybrid, depending on their operation
and type of requests. Proactive protocols control peer connectivity to ensure the availability of
any path between the active nodes. In order to maintain a common network topology, sensor
nodes announce their routing state tables of the entire network.
On the other hand, reactive protocols establish paths only on request. Meanwhile, the sensors are
inactive in terms of routing behavior. Nodes transmit each routing request to their peers until
comes to a sink node and the last answer on the reverse communication path.
2.1. DSDV Routing protocol
Destination Sequenced Distance Vector (DSDV) [12] is a hop-to-hop distance vector routing
protocol. It is characterized by each host maintaining a table consisting of the next-hop neighbor
and the distance to the destination in terms of number of hops.
In order to obtain the optimal path, the protocol DSDV guarantees loop free routes to each
destination node, this is based on an average settling delay, which is a delay before advertising a
route. All the hosts periodically broadcast their tables to their neighboring nodes in order to
maintain an updated view of the network.
2.2. DSR Routing Protocol
The DSR protocol is a reactive protocol that aims to limit the bandwidth consumed by packet
routing in wireless ad-hoc wireless networks. Dynamic source routing protocol [13] is based on
the concept of a routing algorithm from the source node to discover routes.
This means that every node needs only forward the packet to its next hop specified in the header
and need not check its routing table as in a table-driven algorithm. Determining source routes
requires accumulating the address of each device between the source and destination during the
route discovery.

130
2.3. AODV Routing Protocol
The ad-hoc on demand distance vector is an on demand algorithm, meaning that it builds routes
between nodes only as desired by source nodes. It maintains these routes as long as they are
needed by the sources. AODV [8] [12] uses sequence numbers to ensure the freshness of routes.
This routing protocol builds routes using a route request on a route reply query cycle.
AODV uses a reactive approach for finding routes and a proactive approach for identifying the
most recent path. This protocol uses the same route discovery process to DSR protocol for finding
fresh routes.
3. PERFORMANCE METRICS
The technical performance of our proposed routing algorithm is evaluated based on various
performance metrics [3].
3.1 Average energy consumption
The energetic consumption is the average of the total energy consumption of the entire network to
transmit data packets from a source to destination. We obtain the energy consumption by
calculating the ratio of the sum of the total energy consumed by each node to the total number of
nodes [21].
[
So a protocol that uses less energy during the simulation is considered more effective [12].
3.2 Lifetime
Network lifetime is the time span from the deployment to the instant when the WSN is considered
non-functional. It can be, for example, the instant when a percentage of sensors die and
consequently the loss of coverage occurs [6] [14].
3.3 Packet Lost
It represents the total number of data packets dropped during the simulation. The loss of a packet
may be due to a collision during transmission process.
rs NPNPPL −= (1)
Where:
- PL: The number of packet lost
- NPs: The number of packet send
- NPr: The number of packet received
4. CEDM-DR PROTOCOL DESIGN AND IMPLEMENTATION
4.1 WSN Energy Consumption Model
In this work, the energy consumed by both the transmitter and the receiver blocks was evaluated
for calculating the total energy consumption in the network. We perform the transmitter and
receiver hardware model as introduced in [3] [19].

131
The total energy consumed by a wireless sensor S is the consumed energy by its communication
block (transmitter / receiver), sensing block and processing block [15].
We consider that transceiver circuit of a wireless node operates according to three modes. Indeed,
when there is information to send the sensor node operates in the communication mode so all
these circuits are active.
But, if there is no data to communicate the sensor circuits switch to standby mode. During this
mode, wireless node is in a state of listening and sensing.
This strategy contributes to reduce energy consumption that's why the power consumption in this
mode is small enough to be neglected.
In this study we assume that the energy consumed by the sensing and the processing block is
neglected because it is quite negligible with respect to the energy consumed by the
communication block [17] [18].
.
(2)
Where:
- Ec-sens is the energy consumed during sensing process.
- Ec-proc is the Energy consumed during the processing phase
- Ec-com Energy consumed during communication phase.
(3)
We considered the energy model as introduced in [19] and shown in the following Figure.
Figure 1. Energy model adopted
The total energy consumption of the communication process is expressed as follows [18]:
(4)
Where:
- ETx represents the energy consumed during the transmission process.
EEEsE comcproccsenscctot −−−− ++=)(
)()( sEsE comcpctot −− ≈
),(),()( dLEdLEsE RxTxctot +=−

132
- ERx represents the energy consumed during the reception process.
- L is the number of bit transmitted.
- d is the distance between transmitting sensor and receiving sensor
Expressing each terms:
(5)
(6)
Where:
- Ecircuit represents the energy consumed by the electronic circuits.
- Kamp : is the transmission amplification coefficient
- λ is the path loss exponent.
4.2 CEDM-DR Algorithm
In this section, we describe the algorithm of our new routing protocol which is based on
combination of two major metrics: distance and energy.
- Step 1: Each network node(s) transmits hello messages to discover neighboring nodes to
one hop.
- Step 2: Verification that the sink and the source node own neighboring nodes. ( If true go
to Step3 else Stop )
- Step 3: All network nodes discover neighboring nodes through the Step 1.
- Step 4: All network nodes evaluate the distance between each neighbor and the sink. This
distance is expressed as follows:
(7)
Where the Xs and Ys are respectively the coordinate of the node “s”.
- Step 5 : The weight is calculated using two parameters which are the remaining energy in
the node receiver and the distance between the receiver node and the sink.
(8)
- Step 6 : When one of the sensor nodes needs to transmit data it will choose the node with
the highest weight among these neighbors.
The setting parameters considered in our simulations are summarized in Table II.
To simulate different routing protocols we choose network simulator 2 (Ns2) since it is open
source free software in which different specifications in the environment can simply modified and
changed. Figure 2 presents an example of wireless sensor network under NS2.
Performance of the routing protocols AODV and the new protocol CEDM-DR are evaluated
based on different performance metrics, average energy consumption, the lifetime of the network
and total dropped data packets.
λ
dLkLEsE ampcircuitTx ***)( +=
LEsE circuitRx *)( =
( ) ( )2
sin
2
sin)( ksksSink YYXXsD −+−=
)(
1
)(Re)(
sD
smainingEnergysWeight
Sink
+=

133
Table 1. Simulation Parameters.
Parameters Values
Routing Protocols AODV, CEDM-DR
Number of Nodes deployed 25 to 200
Environment Size 400*400m2
Nodes Placement Strategy Random
Transmission Range 100m
Initial Node Energy 2.5 Joules
Tx Power 0.07mw
Idle Power 0.03mw
Sleep power 0.01mw
Energy circuit 50nJ/bit
K amplification coefficient 100pJ/bit/m2
Simulation Time 150sec
Antenna Model Omni Antenna
Propagation Model Two Ray Ground
Transport Protocol TCP/UDP
Figure 2. Simulation on NS2

134
Figure 3. Total average energy consumption
The average energy consumed by the sensor nodes as a function of the number of nodes is
illustrated in Figure 3. We note that for all variations of the number of node the new routing
protocol implemented still consumes less than the traditional AODV protocol using CEDM-DR,
we observe between 0% and 66% energy savings when compared with AODV.
Figure 4. Lifetime of the network
Figure 4 depicts the number of nodes living on a total of 200 nodes with respect to the number of
transmissions during the simulation. These curves show that the new protocol CEDM-DR
improves the entire network’s lifetime. This is mainly due to the dynamic priority-weight
adopted.

135
Figure 5. Total of dropped packet
The total number of packet lost as a function of the number of sensor nodes is drawn in Figure 5.
From this plot, we confirm that our new approach allows to obtain lower number of packet lost
than its counterpart. The CEDM-DR protocol reduces between 0% and 50% of packet lost.
5. CONCLUSIONS
In this paper, the performance of a new routing protocol using two important cost metrics has
been evaluated through extensive simulations verifying that our proposed algorithm is effective in
saving energy and leads the system to overall enhancements.
Indeed, implementation and experimentation of Combined Energy and Distance Metrics Dynamic
Routing Protocol (CEDM-DR) using network simulator reveals that our new approach is better
than AODV in energy consumption, Packet Lost and especially in Lifetime.
To sum up, the above results illustrate that the CEDM-DR strategy works well when compared
with AODV. Hence, our future plan is to evaluate security issues in this new routing approach
CEDM-DR.
ACKNOWLEDGEMENTS
This work was supported in part by Laboratory of Acoustics at University of Maine, LAUM
UMR CNRS n_6613 in France and Laboratory of System of communication Sys’Com, ENIT in
Tunisia.
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Authors
Mehdi Bouallegue received the B.S. degree in 2008 from Higher Institute of Computer
and Communication Techniques, Tunisia and M.S. degree in 2010 from National
Engineering School of Tunis. Currently he is a Ph.D. student at the School of
Engineering of Tunis. He is a researcher associate with Laboratory of Acoustics at
University of Maine, LAUM UMR CNRS n° 6613, France, and Laboratory System of
communication SysCom, ENIT, Tunisia.He is Assistant in National school of Engineering of Carthage
(ENI Carthage), Tunisia. His research interests are mainly in the field of telecommunications, wireless
sensor networks, optimization of routing protocols, Network simulation.

137
Kosai Raoof obtained his M.Sc. and Ph.D. from Grenoble University in 1990 and 1993
respectively; in 1998 he obtained the Habilitation à Diriger Des Recherches Degree (HDR).
He was invited to join Laboratoire des Images et Signaux (LIS) in 1999, to participate in the
founding of telecommunication research group. His research interest was first focalized on
advanced MIMO systems and joint CDMA synchronization; he studied and introduced polarized diversity
in MIMO systems. In 2007 he joined GIPSA-LAB to continue his research on Smart Sensor Networks and
cooperative MIMO antenna systems. He is a referee for many international journals and conferences in the
field of telecommunications and signal processing. He is currently a full professor at the ENSIM
Engineering College, University of Maine, Le Mans.
Ridha Bouallegue received the Ph.D. degrees in electronic engineering from the National
Engineering School of Tunis. In Mars 2003, he received the Hd.R degrees in multiuser
detection in wireless communications. From September 1990 He was a graduate Professor
in the higher school of communications of Tunis (SUP’COM), he has taught courses in
communications and electronics. From 2005 to 2008, he was the Director of the National
engineering school of Sousse. In 2006, he was a member of the national committee of science technology.
Since 2005, he was the laboratory research in telecommunication Director’s at SUP’COM. From 2005, he
served as a member of the scientific committee of validation of thesis and Hd.R in the higher engineering
school of Tunis.

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