Improved routing scheme with ACO in WSN in comparison to DSDV

IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 08, 2014 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 265
Improved routing scheme with ACO in WSN in comparison to DSDV
Meetu Galhotra1
Dr. Rajesh Gargi2
1
M.tech Student 2
Professor
1,2
Geeta Engineering College
Abstract— Routing is the process of selecting best paths in a
network in terms of energy and distance. In adhoc it is
critical to collect the information in an efficient manner as it
has limitations in terms of centralized congestion. In such
case to perform the effective communication there is the
requirement of some such routing approach that can provide
the routing with optimized path. In this work, ACO based
routing approach is defined to generate the optimized path in
comparison to DSDV over the network. The presented
approach is implemented in matlab environment and
obtained results shows the effective results in terms of
optimized path.
Keywords: Routing, Optimized Path ACO, Effective
Communication
I. INTRODUCTION
Wireless sensor networks are formed by small sensor nodes
communicating over wireless links without using a fixed
network infrastructure. Sensor nodes have a limited
transmission range, and their processing and storage
capabilities as well as their energy resources are also
limited. Wireless Sensor Networks are self-configured and
are without infrastructures. WSN collects data from the
environment and sends it to a destination site where the data
can be observed, memorized.
There is a bulk collection of the nodes in
Unstructured WSN. There exists some pre-defined structure
of nodes in Structured Nodes [4, 5]. Sensors can be
positioned far from the actual phenomenon. Several sensors
that perform only sensing can be deployed [6].
II. ROUTING SCHEMES IN WSN
A. Flat Routing Protocols
Flat routing protocols are divided mainly into two classes;
the first one is proactive routing (table - driven) protocols
and other is reactive (on-demand) routing protocols.
Proactive routing is mostly based on LS (link-state) while
on-demand routing is based on DV (distance-vector).
1) Pro-Active / Table - Driven routing Protocols
In proactive routing protocol, every node maintains one or
more tables representing the entire topology of the network.
These tables are updated regularly in order to maintain a up-
to-date routing information from each node to every other
node.
Examples of Proactive MANET Protocols include:
2) Reactive (On Demand) protocols
Reactive protocols start to set up routes on-demand . The
mobility of the nodes causes the topology of the network to
change constantly. Keeping track of this topology is not an
easy task, and too many resources may be consumed in
signaling. Reactive routing protocols were intended for
these types of environments. This kind of protocols is
usually based on flooding the network with Route Request
(RREQ) and Route reply (RERP) messages.
Fig. 1.1: Routing Protocols Classification
The different types of On Demand driven protocols are:
 Ad hoc On Demand Distance Vector (AODV)
 Dynamic Source routing protocol (DSR)
 Temporally ordered routing algorithm (TORA)
 Associatively Based routing (ABR)
 Signal Stability-Based Adaptive Routing (SSA)
 Location-Aided Routing Protocol
3) Hierarchical Routing Protocols
As the size of the wireless network increases, the flat routing
protocols may produce too much overhead for the MANET.
In this case a hierarchical solution may be preferable.
 Hierarchical State Routing (HSR)
 Zone Routing Protocol (ZRP)
 Cluster-head Gateway Switch Routing Protocol
(CGSR)
 Landmark Ad Hoc Routing Protocol (LANMAR)
4) Geographical Routing Protocols
An advantage of geographic routing protocols is that they
prevent network-wide searches for destinations. Examples
of geographical routing protocols are:
 GeoCast (Geographic Addressing and Routing)
 DREAM (Distance Routing Effect Algorithm for
Mobility)
 GPSR (Greedy Perimeter Stateless Routing)
III. DESTINATION SEQUENCE DISTANCE VECTOR
Destination sequenced distance vector routing (DSDV) is
adapted from the conventional Routing Information Protocol
(RIP) to ad hoc networks routing. It adds a new attribute,
sequence number, to each route table entry of the
conventional RIP. Using the newly added sequence number,
the mobile nodes can distinguish stale route information
from the new and thus prevent the formation of routing
loops.

(IJSRD/Vol. 2/Issue 08/2014/061)
A. Packet Routing and Routing Table Management
In DSDV, each mobile node of an ad hoc network maintains
a routing table, which lists all available destinations, the
metric and next hop to each destination and a sequence
number generated by the destination node. Using such
routing table stored in each mobile node, the packets are
transmitted between the nodes of an ad hoc network. Each
node of the ad hoc network updates the routing table with
advertisement periodically or when significant new
information is available to maintain the consistency of the
routing table with the dynamically changing topology of the
ad hoc network.
Periodically or immediately when network
topology changes are detected, each mobile node advertises
routing information using broadcasting or multicasting a
routing table update packet. The update packet starts out
with a metric of one to direct connected nodes. This
indicates that each receiving neighbor is one metric (hop)
away from the node. It is different from that of the
conventional routing algorithms. After receiving the update
packet, the neighbors update their routing table with
incrementing the metric by one and retransmit the update
packet to the corresponding neighbors of each of them. The
process will be repeated until all the nodes in the ad hoc
network have received a copy of the update packet with a
corresponding metric. The update data is also kept for a
while to wait for the arrival of the best route for each
particular destination node in each node before updating its
routing table and retransmitting the update packet. If a node
receives multiple update packets for a same destination
during the waiting time period, the routes with more recent
sequence numbers are always preferred as the basis for
packet forwarding decisions, but the routing information is
not necessarily advertised immediately, if only the sequence
numbers have been changed. If the update packets have the
same sequence number with the same node, the update
packet with the smallest metric will be used and the existing
route will be discarded or stored as a less preferable route.
In this case, the update packet will be propagated with the
sequence number to all mobile nodes in the ad hoc network.
The advertisement of routes that are about to change may be
delayed until the best routes have been found. Delaying the
advertisement of possibly unstable route can damp the
fluctuations of the routing table and reduce the number of
rebroadcasts of possible route entries that arrive with the
same sequence number. The elements in the routing table of
each mobile node change dynamically to keep consistency
with dynamically changing topology of an ad hoc network.
To reach this consistency, the routing information
advertisement must be frequent or quick enough to ensure
that each mobile node can almost always locate all the other
mobile nodes in the dynamic ad hoc network. Upon the
updated routing information, each node has to relay data
packet to other nodes upon request in the dynamically
created ad hoc network.
IV. EXISTING WORK
Energy efficiency is the main issue of Wireless sensor
networks operations because of the limited and energy
supply Hence, BPNDA was proposed, a data aggregation
scheme based on back-propagation network (BPN). In the
BPNDA, a three-layer BP neural network was used. The
input layer neurons are located in cluster members (CMs),
while the hidden layer neurons and the output layer neurons
are located in cluster head (CH). Only the extracted data that
represented the features of the raw data will be transmitted
to the sink, so the efficiency of data gathering is improved
and the total energy consumption is reduced[2].
In this paper they introduce our neural network
based approach which results in a more efficient routing
path discovery and sensor power management. They define
a set of attributes based on sensors’ location and
neighborhood and use them as inputs of our neural network
and the output of the neural network will be used as a factor
in the route path discovery and power management. They
designed a simulator based on our approach and observed
the effect of our method on Wireless sensor network lifetime
and sensor power consumption which will be presented in
this paper [3].
This paper describes the concept of sensor
networks which has been made viable by the convergence of
microelectro-mechanical systems technology, wireless
communications and digital electronics. First, the sensing
tasks and the potential sensor networks applications are
explored, and a review of factors influencing the design of
sensor networks is provided. Then, the communication
architecture for sensor networks is outlined, and the
algorithms and protocols developed for each layer in the
literature are explored. Open research issues for the
realization of sensor networks are also discussed.[4]
V. ANT COLONY OPTIMIZATION (ACO)
Ant Communication is accomplished primarily through
chemicals called pheromones. Ants communicate to one
another by laying down pheromones along their trails. Other
ants perceive the presence of pheromone and tend to follow
paths where pheromone concentration is higher.
Fig. 1.2: Ant Behaviour
(1) Ants in a pheromone trail between nest and food;
(2) An obstacle interrupts the trail;
(3) Ants find two paths to go around the obstacle;
(4) A new pheromone trail is formed along the shorter
path.
ACO is basically the optimization approach that is
basically used to speed up the algorithmic process. In
wireless network the ACO is basically used to optimize the
communication process. According to this approach a node
generate the ant to find the optimized path over the network.
These ant place the pheramons on this located path so that
all other nodes can follow these pheramons to communicate
on this optimized path. The formost step of ant
communication is the identification of pheramon location

and to place them at appropriate location. More time it takes
for an ant to travel down the path and back again, the more
time the pheromones have to evaporate. A short path gets
marched over faster, and thus the pheromone density
remains high as it is laid on the path as fast as it can
evaporate.
VI. PROPOSED WORK
In this present work we have improved the routing approach
by improving the existing path selection algorithm with the
inclusion of Ant Optimization approach. The first step is to
setup the network with specific parameters. These
parameters includes:
(1) Number of Packets: This property represents the
number of successful packet delivery for a specific
communication.
(2) Number of Packet loss: Due to the congestion or
any block node there are the chances of the data
loss over the network. This parameter will analyze
the packet loss over the transmission. It is the
decision parameter that will perform the analysis
the next node is a valid node or not.
(3) Packet Delivery Ratio: This parameter is basically
defines the ratio of packets transmitted and the
packet successfully arrived to the destination. The
packet delivery ratio we have analyzed on 4
intermediate nodes to identify the problem area
over the network.
(4) Time Delay: It defines the delay in the
communication. The delay will occur because of
congestion over the network.
(5) Energy: As each node in the communication is a
sensor node, because of this each node is defined
with specific energy we have defined 5 Jule to each
node. With each communication over the network
some energy is lost. If the energy is less then
minimum required energy or 0 the node will be
dead itself.
(6) Turn Around Time : It is the actual time taken to
perform the communication over the network.
i. Define N Number of Sensor Nodes in the WSN
with specific parameters in terms of energy,
transmission rate etc.
ii. Each Node Ni start Moving in Direction of Specific
Direction Di
iii. Find M Neighbor Nodes of Nodes Ni and
Maintains the respective Information
For (j=1 to M)
{
MaintainFormation (Ni,Nj)
}
iv. if DataLoss(Ni)>Threshold and TimeDelay >
Threshold1
/* If Bad Node or Congested Node Occur on Node
i*/
{
For i=1 to Mi
{
CollectInformation(Ni, Neighbor(Ni));
}
v. implement Forward ANT to find the alternate path
in each Direction of Neighbour(N(i)).
vi. Set the Pheramon on Each Hop and Identify the
Possible Path
vii. Implement Backward ANT to inform Neighbour
Nodes about Backup Path
viii. Trace the Pharamons and Commmunicate of New
Path
ix. Perform the Normal Communication
}
The description of the Ant concept is presented here
(1) At regular interval any node Source is selected to
send data to some destination node.
(2) Each forward ant selects the next hop node using
the routing table information. The next node
selected depends on some random scheme. If all
nodes already visited a uniform selection will be
performed
(3) If the selected node is some damaged node or it is
not currently available. The forward ant waits to
turn in the low priority node from the queue.
(4) It will identify any of the next non visited node and
pay some delay on it.
(5) If some cycle detected the ant is forced to turn on
the visited node.
(6) When the ant reaches the destination node a
backward ant is generated to transfer all its
memory.
(7) Backward ant uses same path generated by forward
ant.
By default route is chosen on the basis of Path
selection formula and i.e. we will choose the lowest energy
path. It means every time the selected path is using lowest
energy. In case there is problem in the selection of the path
then we apply the Ant Colony Algorithm the purpose of
which is to continue sending data using the previous
VII. RESULTS
The presented work is implemented in Matlab environment
under different scenarios.
As we can see the network of 60 nodes. These
nodes represents the mobile nodes and represent the initial
position of the nodes. We are implementing the Aggregative
path on this network
Fig. 1.3: Initial Network Design (60 Nodes)

Fig. 1.4: Aggregative Path (DSDV Protocol)
The figure shows the initial path driven from the
existing path selection routing.
Fig. 1.5 : Optimized Path
Figure 1.5 is showing the optimized path after
implementation of proposed ACO based approach. As we
can see the output is showing the node sequence in which
the nodes are being visited. In the subplot one the
optimization process is shown and in sub plot 2 the
optimized path obtained from the approach is shown.
VIII. CONCLUSION
In this work, an improved routing approach is presented that
gives the effective route generation in terms of energy,
distance. The approach will provide the safe path so that the
effective communication is expected from the network.
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Improved routing scheme with ACO in WSN in comparison to DSDV