An Energy Efficient Routing Protocol Using NCPR and GNDA Algorithms

International Journal of Communication and Computer Technologies
Volume 02 – No.05 Issue: 01 Mar 2014
ISSN NUMBER : 2278-9723
Volume 02 – No.5, Issue: 01 Page 26
International Journal of Communication and Computer Technologies www.ijccts.org
An Energy Efficient Routing Protocol Using NCPR and GNDA
Algorithms
1
Vignesh.M, 2
Sujay.E.K, 3
Tharanya.M, 4
Saranya.R,5
D.Jayachandran
1, 2, 3, 4, 5
(Department of Computer Science and Engineering, Anna University)
ABSTRACT
This Due to high mobility of nodes in mobile ad hoc
networks (MANETs), there exist frequent link
breakages which lead to frequent path failures and
route discoveries and energy level of nodes also
frequently reduced. The overhead of a route discovery
cannot be neglected. In a route discovery,
broadcasting is a fundamental and effective data
dissemination mechanism, where a mobile node
blindly rebroadcasts the first received route request
packets unless it has a route to the destination, and
thus it causes the broadcast storm problem due to this
more rebroadcast occurs causes more energy
consumption. In this paper, we propose a neighbour
coverage-based probabilistic rebroadcast protocol
with implementation of good node detection algorithm
for reducing routing overhead and energy
consumption in MANETs. In order to effectively
exploit the neighbour coverage knowledge, we
propose a novel rebroadcast delay to determine the
rebroadcast order, and then we can obtain the more
accurate additional coverage ratio by sensing
neighbour coverage knowledge. We also define a
Good node detection Algorithm to provide the energy
efficient network by selecting energy efficient nodes.
By combining the Neighbour coverage probabilistic
routing and good node detection algorithm, we
gradually reduce rebroadcast and choosing energy
efficient nodes during routing. Our approach
combines the advantages of the neighbour coverage
knowledge and Good node detection algorithm which
can significantly decrease the number of
retransmissions so as to reduce the routing overhead,
and can also decrease the energy consumption.
Keywords— Mobile ad hoc networks, neighbour
coverage, network connectivity, routing overhead,
Energy efficient Routing
I. INTRODUCTION
This Mobile ad hoc networks (MANETs) consist of a
collection of mobile nodes which can move freely.
These nodes can be dynamically self-organized into
arbitrary topology networks without a fixed
infrastructure. One of the fundamental challenges of
MANETs is the design of dynamic routing protocols
with good performance, less overhead and energy
efficient. Many routing protocols, such as Ad hoc On-
demand Distance Vector Routing (AODV) and
Dynamic Source Routing (DSR), have been proposed
for MANETs. The above two protocols are on demand
routing protocols, and they could improve the
scalability of MANETs by limiting the routing
overhead when a new route is requested. However,
due to node mobility in MANETs, frequent link
breakages may lead to frequent path failures and route
discoveries, which could increase the overhead of
routing protocols and reduce the energy level of nodes
and increasing the end-to-end delay. Thus, reducing
the routing overhead in route discovery and improve
the energy level in network is an essential problem.
II. IMPLEMENTATION OF NCPR
NCPR is the abbreviation of Neighbour coverage
Probabilistic Routing method which is used to reduce
the routing overhead in order to improve energy level
of nodes
2.1 Neighbour coverage knowledge
When the routing starts, Source node starts to send the
Route request packets to other nodes. So we named
these packets as RREQ packets and when these
packets reach the destination node, Destination node
starts to send Route reply packets to Source to form
the path for network. So we named these packets as
RREP packets [1].

The node which has a larger rebroadcast delay may
listen to RREQ packets from the nodes which have
lower one. For example, if node ni receives a duplicate
RREQ packet from its neighbour nj, it knows that how
many its neighbors have been covered by the RREQ
packet from nj [2]. Thus, node ni could further adjust
its UCN set according to the neighbour list in the
RREQ packet from nj. Then, the U(ni) can be adjusted
as follows:
U(ni) = U(ni) – [U(ni) ∩ N(nj)] (1)
After adjusting the U(ni), the RREQ packet received
from nj is discarded.
2.2 NCPR Algorithm Description
The node which has a larger rebroadcast delay may
listen to
RREQ packets from the nodes which have lower one.
For example, if node ni receives a duplicate RREQ
packet from its neighbor nj, it knows that how many
its neighbors have been covered by the RREQ packet
from nj [1]. Thus, node ni could further adjust its UCN
set according to the neighbour list in the RREQ packet
from nj. Then, the U(ni) can be adjusted as follows [1]:
RREQv: RREQ packet received from node v.
Rv:id: the unique identifier (id) of RREQv.
N(u): Neighbor set of node u.
U(u, x): Uncovered neighbors set of node u for RREQ
whose id is x.
Timer (u, x): Timer of node u for RREQ packet whose
id is x.
{Note that, in the actual implementation of NCPR
protocol,
every different RREQ needs a UCN set and a Timer.}
1. If ni receives the new RREQ packet then
2. Compute the initial Uncovered neighbour sets
U(ni,Rs.id)
3. Compute the rebroadcast Delay Td(ni)
4. Set the timer based on rebroadcast delay.
5. End if
6.
7. While ni receives a duplicate RREQ packet
from nj before timer expires
8. Adjust uncover neighbours and discard RREQ
of nj
9. End While
10.
11. If Timer expires then
12. Compute again Rebroadcasting Probability
13. End if
III. GNDA ALGORITHM
GNDA Algorithm [4] is the Good Node Detection
Algorithm which is calculates the energy level of
nodes and chooses the good energy level nodes for
routing.
2.3 Good Node Detection
Energy efficiency is one of the main problems in a
mobile ad hoc network, especially designing a routing
protocol. The proposed work aims at discovering an
efficient energy aware routing scheme in MANETs.
Although this scheme can somewhat enhance the
latency of the data transfer but it results in a
significant power saving and long lasting routes. This
scheme is one of its types in adhoc networks which
can provide different routes for different type of data
transfer and ultimately increases the network lifetime
[2]. All information related with good neighbors is
stored in routing table which improves performance of
routing protocol in terms of good communication and
stable route. Analytical results of proposed solution
shows that it improves data throughput, improve
overall performance of the network and improve
network life with in fixed and dynamic transmission
range, as shown in Fig. 1.
Following are the parameters for deciding whether the
node is good or bad.
1. Signal Strength
2. Flow Capacity
3. Traffic less routing
2.4 GNDA Algorithm Description
1: Initialize Total number of nodes in the network
2: Initialize TTr of the network
3: Broadcast Hello message
4: Receive Hello message
5: Calculate time, of reaching Hello message
6: Compare NTr and TTr
6.1: if NTr > TTr then Decrease the NTr and
go to step: 6
6.2: else go to step 7
7: Calculate signal strength

7.1: I f signal strength > = Threshold then go
to step: 8
7.2: else it is a weak signal so go to step: 4
8: Calculate flow capacity
8.1: If flow capacity is ok then store node
address (Good node)
8.2: else Bad node
9: Send RREQ through good node
10: When RREQ reach the Destination, it stores the
T_T_L of route.
10.1: After the time out the destination
identifies the RREQ with minimum Total
Traffic Level then Select the path one with
Minimum T_T_L for RREP and acknowledge
the source with the selected path.
10.2: else if all route have high T_T_L greater
than threshold then Send negative
acknowledgement to the source that path
cannot be established now, Wait random
amount time and then send RREQ.
11: Send Data if path available
12: Stop
2.5 Simulation Results
Fig 1: Simulation Result of Throughput between
AODV vs Bandwidth
Efficient GNDA
IV.IMPLEMENTATION OF GNDA IN NCPR
In Order to make a energy efficient Network, We need
to Combine the advantages of the neighbor coverage
knowledge and Good node detection algorithm. They
Reduces the routing overhead, delay by decrease the
number of retransmissions. GNDA-Good Node
Detecting Algorithm [13] is used to overcome the
Energy problems. The node’s energy level is
calculated based on the strength of Route Reply.
GNDA select the node of high energy, packets are sent
through the high energy nodes among the neighbor
nodes and also by using GNDA we can avoid the
selfishness Problem by selecting higher energy nodes
4.1 Route Discovery
Now we perform Route Discovery through NCPR
Algorithm and also we need to use GNDA algorithm
for choosing Good Nodes [17]. SO we use GNDA for
select the energy efficient node to transmit the data in
Mobile AD-Hoc Network.
While choosing Nodes if two nodes exists Same
energy, then Calculate Distance from the searching
node and sending node and compare among sending
nodes then Choose high priority and select the node,
as shown in Fig. 2.
Fig 2: Method for Route Discovery
4.2 Data Transmission
After reaches the destination node, it Sends Route
reply packets to source node. Then transmit the data
from source node to destination node through energy
efficient intermediate nodes. If any path failure occurs
again starts route discovery, as shown in Fig. 3.

Fig 3: Method for Route Discovery
4.3 Performance Analysis
By analysing these methods and implement above idea
in AODV Protocol [2], we would obtain a Energy
efficient and less overhead Network in Manets. By
comparing the working of GNDA [13] and NCPR [3]
Algorithms we implemented the two ideas in the
efficient network.
V. RESULTS
5.1 Simulation Results
In order to evaluate the performance of the proposed
NCPR and GNDA protocols, we compare it with some
other protocols using the NS-2 simulator.
Broadcasting is a fundamental and effective data
dissemination mechanism for many applications in
MANETs. In this paper, we just study one of the
applications: route request in route discovery. In order
to compare the routing performance of the proposed
NCPR protocol, we choose the Dynamic Probabilistic
Route
Discovery protocol [6] which is an optimization
scheme for reducing the overhead of RREQ packet
incurred in route discovery in the recent literature, and
the conventional AODV protocol.
Simulation parameters are as follows: The Distributed
Coordination Function (DCF) of the IEEE 802.11
protocol is used as the MAC layer protocol. The radio
channel model follows a Lucent’s Wave LAN with a
bit rate of 2 Mbps, and the transmission range is 250
meters. We consider constant bit rate (CBR) data
traffic and randomly choose different source-
destination connections. Every source sends four CBR
packets whose size is 512 bytes per second. The
Mobility [2] model is based on the random waypoint
model in a field of 1:000 m _ 1:000 m. In this mobility
model, each node moves to a random selected
destination with a random speed from a uniform
distribution [1, max-speed]. After the node reaches its
destination, it stops for a pause time interval and
chooses a new destination and speed. In order to
reflect the network mobility, we set the max-speed to
5 m/s and set the pause time to 0. The Max Delay used
to determine the rebroadcast delay is set to 0.01 s,
which is equal to the upper limit of the random jitter
time of sending broadcast packets in the default
implementation of AODV in NS-2 [7]. Thus, it could
not induce extra delay in the route discovery. The
simulation time for each simulation scenario is set to
300 seconds. In the results, each data point represents
the average of 30 trials of experiments. The
confidence level is 95 percent, and the confidence
interval is shown as a vertical bar in the figures. The
detailed simulation parameters are shown in Table 1.
We evaluate the performance of routing protocols
using the following performance metrics:
TABLE I
SIMULATION PARAMETERS
Simulation
Parameter
Value
Simulator NS-2 (2.30)
Topology Size 1000m X 1000m
Number of Nodes 50,100,150 ......, 300
Transmission
Range
250 m
Bandwidth 2Mbps
Interface Queue
Length
50
Traffic Type CBR
Number of CBR
Connections
10,12,14,... 20
Packet Size 512 bytes
Packet Rate 4 packets/sec
Pause Time 0s
Min Speed 1 m/s
Max Speed 5 m/s

5.2 Performance Analysis Based on Time Delay
Performance based Comparison were made between
GNDA and NCPR Protocols which were make an
improvement in NCPR [5] with GNDA [14]
implemented Routing technique. The following Graph
describes that how the time delay is gradually
decreased when implanting GNDA algorithm in
NCPR, as shown in Fig. 4.
Fig 4: Comparison between AODV, NCPR and
GNDA implemented NCPR based on Time Delay
5.1 Performance Comparison based on PDR
Performance based Comparison were made
between GNDA and NCPR Protocols which were
make an improvement in NCPR with GNDA
implemented Routing technique. The following
Graph describes that how the Packet Density Ratio
(PDR) [19] is gradually decreased when implanting
GNDA algorithm in NCPR, as shown in Fig. 5.
Fig 5: Comparison between AODV, NCPR and
GNDA implemented NCPR based on Packet
Density Ratio
VI. CONCLUSIONS
With the popularization of Internet and formation of
wireless technologies provide significant impact on
Internet and Communication Technologies. These
technologies have support of one of famous technique
known as Adhoc Network. Adhoc Networks are
assortment of mobile nodes connected by wireless
links and also receiving attention in the scientific
community. In adhoc networks, routes may be
disconnected due to dynamic movement of nodes. So,
route selection and topology combination is very
difficult and challenging issue. Such networks are
more vulnerable to both congestion and path failures
due to presence of low energy neighbor nodes [17].
Observed problem affects performance of routing
protocol in adhoc networks. In this paper, impact of
bad neighbor nodes in adhoc routing is briefly
discussed and proposed a method (GNDA with NCPR)
for identifying good neighbor nodes in the network.
Besides, this approach is extended by adding extra
parameters i.e. signal strength, flow capacity and
relative position of a node in to the account. Proposed
method optimizes the routing issues by using AODV
[7]. The result of this paper is an appropriate solution
against Energy wastage.
ACKNOWLEDGMENT
This work is supported and contributed by
Mr.D.Jayachandran, M.E., [PhD] as Assistant
Professor in Computer Science and Engineering
Department in Our College.
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