Implementing Energy Efficient Strategies in the MANET on-demand routing Protocols and comparing their performances

Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
111 NITTTR, Chandigarh EDIT-2015
Implementing Energy Efficient Strategies in
the MANET on-demand routing Protocols
and comparing their performances
1
P.Sivasankar, 2
G.A.Rathy
1,2
Assistant Professor,
1
Electronics Department, 2
Electrical Department NITTTR, Chennai, India.
1 siva_sankar123p@yahoo.com
ABSTRACT:-Mobile Ad-hoc networks are self-configuring
multi-hop wireless networks where, the structure of the
network changes dynamically. Because of the nodes in the
MANET are mobile and battery operated, energy
optimization is one of the major constraints in the MANET.
Failure of some nodes operation can greatly impede the
performance of the network and even affect the basic
availability of the network, i.e., routing. To improve the
lifetime of these networks can be improving the energy levels
of the individual nodes of the network. This paper presents an
analysis of the effects of different design choices for this on-
demand routing protocols DSR and AODV in wireless ad hoc
networks. In this paper, the energy efficient strategies are
implemented in the AODV and DSR protocols to improve the
life time of the Mobile ad hoc network. The CBEER-NN is
developed using the existing DSR protocol and the AO-
EEDTR is developed using the existing AODV protocol.
GloMoSIM simulator is used to simulate the proposed
MANET environment. This paper also compares the
existing DSR and AODV protocols with proposed CBEER-
NN and AO-EEDTR protocols. From the simulated results,
this paper concludes that the proposed CBEER-NN and AO-
EEDTR protocols are improving the life time of the network
by improving the average residual energy of the nodes over
the existing DSR and AO-EEDTR protocols.
Keywords: AODV, DSR, AO-EEDTR, CBEER-NN, Cache
INTRODUCTION
Mobile ad hoc networks [1] (MANETs) are instantly
deployable without any wired base station or fixed
infrastructure. A node communicates directly with the
nodes within radio range and indirectly with all others
using a dynamically determined multi-hop route.
A critical issue for MANETs is that the activity of nodes is
energy-constrained. In the past few years, extensive
research has been carried out in developing routing
protocols for MANETs. Past research for reducing
energy consumption has focused on the hardware and
the operating system level. However, significant energy
savings can be obtained at the routing level by designing
minimum energy routing protocols that take into
consideration the energy costs of a route when choosing
the appropriate route.
This paper is worked on the network layer/routing layer &
Radio layer and focuses on design and
implementation of Cluster Based Energy Efficient
Routing using Neural Networks(CBEER-NN) in the
existing DSR protocol and AODV based Energy
Efficient Delay Time Routing(AO-EEDTR) in the
existing AODV protocol. These algorithms are designed
and implemented using Global Mobile Simulator
(GloMoSim). Also the performance of the protocol is
evaluated and compared with the existing DSR and AODV
protocols.
DSR AND AODV PROTOCOLS
In this section, the existing on-demand routing protocols
DSR and AODV protocols and their route discovery, route
maintenance procedures will be discussed.
DSR Protocol
DSR [2] is an on demand, source routing protocol, with
each packet carrying in its header the complete, ordered list
of nodes through which the packet will be routed. DSR
consists of two mechanisms: route discovery and
route maintenance. When a node s has a packet to send for
which it does not have a route, it initiates route
discovery by broadcasting a route request (RREQ). The
request is propagated in a controlled manner through the
network until it reaches either the destination node T or
some intermediate node, n, that knows of a route to node
T. Node T then sends a route reply (RREP) to node s with
the new route. In this case that multiple routes are located
(i.e., multiple route replies are received), nodes s selects
the one with the best metric (e.g., hop count).
Route Maintenance is the mechanism by which a node
detects whether or not a route kept in its cache has become
stale as result of host mobility and topology change. When
an (intermediate) node n detects that the next link in a
packets route is broken, it first sends a route error (RERR)
message to the source node s that generated the packets
route.
AODV Protocol
Ad hoc on-demand distance vector in [2] (AODV)
routing protocol uses an on-demand approach for finding
routes, that is, a route is established only when it is
required by a source node for transmitting data packets. It
employs destination sequence numbers to identify the most
recent path. The major difference between AODV and
DSR stems out from the fact that DSR uses source routing
in which a packet carries the complete path to be traversed.
However, in AODV, the source node and the intermediate
nodes store the next-hop information corresponding to
each flow for data packet transmission. In an on-demand
routing protocol, the source node floods the Route request
packet in the network when a route is not available for the
desired destination. It may obtain multiple routes to
different destinations from a single Route request. The
major difference between AODV and other on-demand
routing protocols is that it uses a destination sequence
number to determine an up-to-date path to the
destination. A node updates its path information only if the

NITTTR, Chandigarh EDIT -2015 112
destination sequence number of the current packet received
is greater than the last destination sequence number stored
at the node. When an intermediate note receives a Route
request, it either forwards it or prepares a Route reply if it
has valid route to the destination. The validity of a route at
the intermediate node is determined by comparing
the sequence number at the intermediate node with the
destination sequence number in the route request packet.
CBEER-NN AND AO-EEDTR
In this section, the proposed modified energy efficient
routing algorithms [3,5] for MANET using DSR and
AODV protocols will be discussed. The Cluster Based
Energy Efficient Routing using Neural Networks(CBEER-
NN) algorithm is implemented in existing DSR protocol
and the AODV based Energy Efficient Delay Time
Routing(AO-EEDTR) Algorithm is implemented in
existing AODV protocol.
CBEER-NN
This algorithm is implemented in DSR protocol to find
energy efficient route between the source and the
destination nodes. In this algorithm, selection of routes
should be based on the remaining battery energy level of
the node. This CBEER-NN algorithm selects the route
based on the Energy metrics of a node participating in the
mobile network to route and deliver the packet to the
destination. The CBEER-NN protocol is designed to bring
about energy aware route establishment in order to avoid
the full drain of the energy from a node, which in the
network forms a gateway to the other zones [7]. The
proposed algorithm differs from the existing DSR
protocol in the route discovery and energy aware route
maintenance with higher percentage of reliable delivery of
packets. The energy efficient route establishment in the
CBEER-NN algorithm is described in the following
section.
. Energy Efficient Route Establishment
The network is formed by the “divide and rule” policy for
the nodes to deliver the packets to the destination. The
tree structure with virtual backbone is used to deliver the
packets reliably to the destination and optimized use of
energy in the network. A node on entry to the network gets
itself associated to one of the root if its energy level [8] is
lesser than the root node else it will act as a root and the
node which was a root becomes a leaf node. A virtual
backbone is formed with the nodes having the highest
energy in the domain to establish routes from one node at
one end to other end.
The Route discovery and maintenance phases of CBEER-
NN protocol is discuss in the following sections.
3.1.2. Route Discovery Phase
The DSR protocol broadcasts a route discovery packet and
the reply is formulated by the node, which has entry of the
destination node in its cache or the destination replies
with a route reply packet. The proposed algorithm
makes sure that the route discovery packet is forwarded to
its root and if the root has the cache entry for the packet it
will reply back with the route reply or else it will in turn
forward the packet to its root. Finally if the route is non-
existent till the root node, which participates in the
backbone formation, then the route discovery packet is
sent through the backbone to the other domain nodes,
which may reply back with a route reply. Thus on
establishing a route, the route reply containing the route
back to the source is routed back. Thus if the destination
node is not existent in the domain then after a time out and
resend attempts, the source node could find that the
destination node is unreachable.
3.1.3. Route Maintenance Phase
The CBEER-NN algorithm differs considerably from DSR
in Route Maintenance. The route maintenance is easier as
the hello packet which contains the cache contains of the
node can be interchanged between the leaf node and root
nodes. This will ensure that the route is existent and the
route reply can be generated from the reference of the
cache. The energy based tree formation ensures the
participation of nodes in the network though their power
remaining is less, by reception of packets intended to them
and transmission of packets (acting as a source) but not as
a router. A field namely the ROOT_NODE or
LEAF_NODE need to be interchanged between the root
and the leaf nodes (within the range of the root node) with
the sequence number for tracking of the route and identify
to which root the node belongs to.
3.1.4. Energy Efficient Cluster Head Selection using
Neural Networks
A five layered feed forward neural network is used to
predict the final energy level of the individual nodes in the
cluster. The input patterns belong to one wireless node and
by using them as the inputs of the neural network can
predict the energy level of the mobile node at the last of
network lifetime. These patterns may be in the form of
features coded from node’s distance from sink, node’s
distance from the neighboring border, node’s number of
neighbors, the number of neighbors which initially route
their data through this node. After deploying nodes, the
base station receives nodes positions and neighbor’s
information, thus it can easily calculate these patterns for
each node and the neural network can be able to predict
their final energy level. A well-trained neural network
would be able to receive each node’s features as the inputs
and predict its final energy level. Thus, the neural network
is used to increase the life time of the network.
Selecting Group Heads amongst all the nodes is also
energy conserving scheme. Each node collects data which
are typically associated with other nodes in its
neighborhood, and then the associated data is sent to the
Base Station through Group Head for evaluating the tasks
more efficiently. Assuming the periodic sensing of same
period for all the nodes and Group Head is selected.
Inside each fixed group of nodes, a node is periodically
elected to act as Group Head through which
communication to/from Group nodes takes place. The set
of Group Head nodes can be selected on the basis of the
routing cost metric explored by the equation:
RCM = Ek/Ar{ ET
(Nk
S
,Nm
D
)+ER
(Nk
S
,Nm
D
)}------ 1
Where,
Ek be the energy associated with the delivery ratio of the
packet, delivered correctly from source node NS
to the
destination node ND
ET
(Nk
S
,Nm
D
) energy transmitted from NS
and
ER
(Nk
S
,Nm
D
) is the energy received at ND
,
Ar be the range area of the network.
AO-EEDTR

The basic idea behind this AODV based Energy efficient
delay time routing (AO-EEDTR) algorithm is to utilize a
longer and more energy efficient routing [6] path instead of
using a lesser energy efficient and shorter path. The Energy
efficient delay time routing algorithm is based on the
AODV protocol. The Route Discovery in the AODV
protocol is modified so as to enable the selection of the
most energy efficient routes [9] by the source nodes. The
Route Maintenance is essentially the same as in AODV.
3.2.1. Route Discovery Phase
Generally in on-demand routing algorithm, when a source
needs to know the route to a destination, it broadcasts a
RREQ packet. The neighboring nodes on receiving the
first-arrived RREQ packet relay this packet immediately to
their neighbors. But in our algorithm, this packet relaying
does not occur immediately. The idea of EEDTR algorithm
is as follows: Upon receiving a request packet, each
node first holds the packet for a period of time, which is
inversely proportional to its current energy level. After
this delay, the node forwards the request packet. This
simple delay mechanism is motivated by the fact that the
destination accepts only the first request packet and
discards other duplicate requests. With this delay
mechanism, request packets from nodes with lower energy
levels are transmitted after a larger delay, whereas the
request packets from nodes with higher energy levels are
transmitted after a smaller delay.
Some nodes may receive several copies of the same
RREQ packet from other neighbors. In AO-EEDTR, the
duplicate copies of the same RREQ packets would be
dropped as in the original AODV protocol. Fig.
1. Ilustrates AO-EEDTR algorithm. In the figure, it is
assumed that the initial maximum battery capacity of all
nodes is 10. The remaining energy levels after a finite
amount of time are shown in Figure alongside the nodes.
Due to transmission range limitations, nodes A and B can
transmit the packet only to nodes C and D,
respectively. The residual battery capacities of A and B
nodes are same, so they flood the RREQ packets at the
same time. We may ignore the travel time between nodes,
without loss of generality. Since node D has more residual
battery capacity than node C, other neighbors that can
communicate with both nodes C and D first receive the
RREQ packet from node D (because of the inverse delay).
The process repeats until the RREQ packet arrives at the
destination. In this figure, the destination node receives
the following routes: (S-B-C-E-T), (S-A-D-F-T)
and (S-A-D-G-T). Normally the route with the least hop is
selected. But with AO-EEDTR, the route for
communication from node S to node T is chosen as (S-A-
D-F-T), since nodes with lesser energy level delay the
packet more than others. Thus a energy efficient path is
chosen. Note that implementation of the algorithm
requires minimal modification at local nodes by adding a
delay mechanism. However, the penalty of this
protocol is introduction of delay in route discovery
procedure. Various delay functions which map energy
value into delay, will be evaluated in the simulation section
that follows.
The selected route (S-A-D-F-T) may not always
guarantee the total minimum energy, partially because it
does not consider the number of hops in the route.
Nevertheless, simulation results showed that AO-EEDTR
prolongs the network lifetime significantly.
Fig. 1. AO-EEDTR algorithm
In the algorithm the delay incorporated by each of the
nodes is inversely proportional to the remaining energy
level of each of the corresponding nodes. Following linear
function incorporates the inverse proportionality
Delay di = TM – (TM * er) /EM------------2
Where,
di  Delay to be introduced
TM  Maximum delay possible
er  Remaining energy of a node
EM  Maximal energy possible for a node
RESULT AND ANALYSIS
Simulation tool : GLOMOSIM
GloMoSim in [4] (Global Mobile Information System
Simulator) is a scalable simulation environment that
effectively utilizes parallel execution to reduce the
simulation time of detailed high-fidelity models of large
communication networks. GloMoSim is a scalable
simulation library for wireless network systems built using
the PARSEC simulation environment. Glomosim can be
modified to add new protocols and applications to the
library. Therefore Glomosim is a good choice for
implementing the different traffic sources. GloMoSim is
aimed at simulating models that may contain as many as
100,000 mobile nodes with a reasonable execution time;
this is done by using node aggregation.
Performance Metrics
The various parameters that were measured during the
simulation are as follows:
Packet Delivery Ratio: It is defined as the ratio of number
of packets received to that of the number of packets sent.
Routing overhead: It is defined as the sum of number of
route requests, route replies & route errors.
End to End Delay: It is the overall average delay
experienced by a packet from the source to that of the
destination.
Average Residual Energy: It is taken as the average of
the remaining energy levels of all the nodes in the network.
These metrics were measured by varying the
following three parameters
1. Number of Nodes
2. Speed(m/sec)
3. Number of Source Destination Pairs
Simulation Results and Comparision
Fig. 2 shows that the proposed CBEER-NN and AO-
EEDTR algorithms are performing well compared to the
existing DSR and AODV protocols for maintaining the
average residual energy in each of their nodes even if the
Number of nodes pairs increased. Similarly the average
residual energy in each of their nodes are improved in
the proposed AO-EEDTR and CBEER-NN over the

NITTTR, Chandigarh EDIT -2015 114
existing AODV and DSR protocols, though the nodes
mobility or speed increased. This is shown in Fig. 3 by
comparing the average residual energy for varying the
nodes mobility.
Fig. 2.a.
Fig. 2.b.
Fig. 2. No. of Nodes Vs Avg. Residual Energy
Fig. 3.a.
Fig. 3.b.
Fig.3. Speed Vs Avg. Residual Energy
Fig. 4 shows the improved Packet delivery ratio for the
proposed CBEER-NN and AO-EEDTR algorithms over
the existing protocols for varying the different Number of
Source Destination Pairs.
Fig. 4.a.
Fig. 4.b.
Fig. 4. No. of Source Destination Pair Vs Packet Delivery Ratio
Fig. 5 shows that the proposed algorithms produces more
End to End delay over the existing protocols because not
following the shortest hop path. Fig. 6. shows the Control
Overhead with Number of Nodes. It indicates that the
Overhead increases as the Number of Nodes increases,
due to increase in number of route requests and number of
route replies flooded in the network. Also it is concluded
that the CBEER-NN and AO-EEDTR algorithms
are working well when compared to existing DSR and
AODV protocols.
Fig. 5. No. of Nodes Vs End to End Delay
Fig. 6. No. of Nodes Vs Control Overhead
CONCLUSION
The DSR and AODV protocols have been implemented
and compared with the modified energy aware CBEER-
NN and AO-EEDTR protocol and it is observed to have
No. of Nodes Vs Avg. Residual energy
0
200
400
600
800
1000
1200
1400
40 50 60
No. of Nodes
Avg.ResidualEnergy(mWhr)
DSR
CBEER-NN
No. of Nodes Vs Avg. Residual Energy
0
200
400
600
800
1000
1200
40 50 60
No. of Nodes
AODV
AOEEDTR
Speed Vs Avg. Residual Energy
1670
1680
1690
1700
1710
1720
1730
1740
1750
1760
1770
1780
5 6 7 8
Speed(m/sec)
AODV
AOEEDTR
Speed Vs Avg. Residual Energy
3300
3400
3500
3600
3700
3800
3900
4000
5 6 7 8
Speed(m/sec)
DSR
CBEER-NN
No. of Source Destination Pairs Vs Packet Delivery Ratio
0
0.2
0.4
0.6
0.8
1
1.2
5 10 15 20
No. of Source Destination Pairs
PacketDeliveryRatio
DSR
CBEER-NN
No. of Source Destination Pairs Vs Packet Delivery Ratio
0
0.2
0.4
0.6
0.8
1
1.2
5 10 15 20
No. of Source Destination Pairs
PacketDeliveryRatio
AODV
AOEEDTR
No. of Nodes Vs End to End Delay
0
50
100
150
200
250
300
350
400
40 50 60
No. of Nodes
EndtoEndDelay(ms)
DSR
CBEER-NN
AODV
AOEEDTR
No. of Nodes Vs Control Overhead
0
200
400
600
800
1000
1200
1400
1600
40 50 60
No. of Nodes
ControlOverhead(pkts)
DSR
CBEER-NN
AODV
AOEEDTR

improved performances of the ad-hoc network. It is found
that the modified algorithms have the comparable
performance with respect to Average Residual Energy,
Packet Delivery Ratio and Overhead with the existing DSR
and AODV protocols. It has also been observed that by
implementing a proper standardized energy model in the
existing DSR and AODV protocols, our CBEER-NN and
AO-EEDTR protocols are feasible and capable of better
energy performance than the preset DSR and AODV
protocols.
REFERENCES
1. Journal Papers
[1] Marco Conti and Silvia Giordano “ Multihop Ad Hoc Networking:
The Theory”, IEEE communication Magazine, 2007.
[2] X.Hong,K.Xu and Gerla, “Scalabe Routing Protocols for MANET ”,
IEEE network vol 16, pp 11-21, 2002.
[3] Shio Kumar Singh, D K Singh and M P Singh,. “A Survey of Energy-
Efficient Hierarchical Cluster-Based Routing in Wireless Sensor
Networks, Int. J. of Advanced Networking and Applications”, Volume:
02, Issue: 02, Pages: 570-580, 2010.
[4] Rajiv Bagrodia, Xiang zeng and Mario Gerla, “Glomosim: A library
for simulation of wireless networks”, University of califrnia ,Los Angels.
2. Conference Proceedings
[5] C. Jinshong Hwang, Ashwani Kush, Sunil Taneja,. “Making MANET
Energy Efficient, IEEE”, 2011
[6] Eei Yu and jangwon Lee, “DSR based Energy aware Routing
Protocols in Ad Hoc networks”, IEEE,2003
[7] H.safa,Mirza.O, and Artail.H, “A Dynamic Energy Efficient
Clustering Algorithm for MANET.IEEE International Conference on
Wireless and Mobile Computing, 2008
[8] Young-sam Kim, Kyung-min Doo, and Kang-whan Lee,. “A Study on
the Synchronization Clustering Control for MANET, ICCIT, IEEE. 2008.
[9] Avni Khatkar, Yudhvir Singh, and Rohtak, “Performance Evaluation
of Hybrid Routing Protocols in Mobile Adhoc Networks”, 2012 Second
International Conference on Advanced Computing & Communication
Technologies. 2012
[10] Imane M. A. Fahrnv, Hesham A. Hefny and Laila Nassef,. PEEBR:
“Predictive Energy Efficient Bee Routing Algorithm for Ad-hoc Wireless
Mobile Networks, The 8th International Conference on INFOrmatics and
Systems (INFOS2012) , Computer Networks Track”, 2012

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