Power Reduction Using Ant Colony Optimization Based
Routing Protocol
Umair Ahmed
Dept.of computer Sciences,
Shaheed Zulfiqar Ali Bhutto Institute of
Science and Technology, Pakistan
[email protected]
Abstract—Power consumption is always
remained a major problem in wireless sensor
networks which causes routing problem. In
wireless sensor networks battery constraints
are very limited which also limit the routing
of nodes within the network, so in order to
address this issue Ant colony optimization
(ACO) based routing algorithm is designed to
minimize the power consumption. In ACO
based algorithm a table is designed which
check the efficiency of each route and
assigned the grade based on the route
efficiency, so simulation indicates that
proposed algorithm well address the issue
and transmission between nodes reduce the
power consumption.
Keywords—ant colony optimization, power
reduction, wireless sensor networks, routing
INTRODUCTION
Wireless sensor networks industry has been
developed rapidly because sensors not only
detect the change in the environment but also
used to transfer the data among the nodes.
Wireless sensor network has quite limited
battery, sometimes sensor (node) lose the data if
battery runs out so it transfers the load to other
Zubair Ahmed
Dept.of computer Sciences
Shaheed Zulfiqar Ali Bhutto Institute of
Science and Technology, Pakistan
[email protected]
nodes so power consumption is always remain
an issue for wireless sensors.
The following will give brief introductions about
the three traditional algorithms applied in the
senor networks, including Ad-hoc On-demand
Distance Vector (AODV), Directed Diffusion
(DD) and Ant Colony Optimization (ACO).[1]
1-Ad-hoc-demand Distance vector routing
(AODV): AODV was proposed by C.E
Perkins and E.Royer and was basically
used for fixed networks where nodes are
in stationary position and for sensor
networks. The method that was adopt by
AODV for the rout request is that When
node S wants to send a packet to node D,
but does not know a route to D, node S
initiates a route discovery Source node S
floods Route Request (RREQ).Each node
appends own identifier when forwarding
RREQ, When a node re-broadcasts a
Route Request, it sets up a reverse path
pointing towards the source. AODV
assumes symmetric (bi-directional) links
when the intended destination receives a
Route Request, it replies by sending rout
reply along the path which is reverse path
of the forward path established by sending
rout request.[2]
2-Directed
Diffusion
(DD):
C.
Intanagonwiwat presented DD in 2003 In
Directed Diffusion the communication path
is established only for the known data or the
node who wants to communicate established
path between the other nodes. The Rout
request sends according to the interests for
the known data, the data that shows more
interest is forward towards the node which is
interested for that data. For that purpose
intermediate nodes can used their cache
memory in order to direct the interest based
on previous known data in cache memory
.[3]
3-Ant Colony Optimization (ACO): Dorigo
proposed ACO in 1997, which imitates the
behavior of the ants to look for the shortest
PROPOSED METHOD
transmission and send rout request to Z in
order to find the shortest path based on
AODV algorithm node Z send the rout reply
to source node A, hence each node has its
own cache memory so when node A will
move from plane x to y than each successive
node of node A will provide the shortest
Firstly the whole area of transmission is
divided in three planes X, Y and Z. This
algorithm will keep track of the movement
of node within that area surrounded by x, y
and z planes. Secondly source node A starts
path. A set of cooperating agents tends to
find the shortest path for the travelling sales
man person called ants. These cooperating
agents communicate with each other
indirectly by depositing the pheromone on
the edge of the travelling sales man person
(TSP) graph while building solution.[4] so
there exist three features of the ACO
1)-Ants tend to the path left the higher
pheromone; 2) the pheromone is
speedily accumulated in the shorter
path; 3) ants communicate indirectly in
pheromone.[5]
and efficient path to node A so when node A
will move in y plane then node A will delete
the information about the x plane nodes after
finding the shortest path in Y plane and store
it in its memory hence the same method is
applied on when node A will move to Z
plane, each successive node of the Y plane
will provide the information for the shortest
path hence when node will move to Z plane
it will delete the information from its header
or cache memory and will starts
broadcasting for shortest path in Z plane so
advantage of this protocol is that node don’t
have to keep information about the previous
node in its header the smaller the header size
the efficient will be communication and load
is balanced among the nodes of the
transmission.
2- Adhoc on demand vector routing has
some upper hand over the dynamic source
routing in three ways:
I.
II.
III.
COMPARISON
1-ACO based algorithm that is applied in
that paper is better than ant colony system
for travelling sales man person in three
different aspects:
I.
II.
III.
The state transition rule transforms
the one state of the system to other
state, exploits the new edges and
keeps the balance between the
accumulated
knowledge
and
exploiting the priori technique.
Ants tends to find the shortest path,
so the path that seems to be shortest
or seems to be best ant tour then the
global updating rule is applied on
the edge of the shortest path or best
path.
Local pheromone updating rules are
used to construct the solution.[6]
The larger the size of the header the
more
complex
will
be
communication, DSR contains the
established path in packet headers.
.In DSR each node who wants to
communicate send rout request then
after passing through intermediate
nodes rout is established so each
rout append its identifier in the
packet header but in AODV the
routing table is maintained at the
nodes so that the data packet don’t
have to contain the information in
its header.
AODV only send the data to those
nodes who wants to communicate it
means routs are not established
before communication it established
some node shows some interest for
data packet so this is the feature that
is same as in DSR.[2]
3-Directed diffusion (DD) and ant colony
optimization algorithm is used together
because directed diffusion is used in order to
save the energy because it is query driven
protocol which is used when node comes
within the range of transmission and ant
colony optimization algorithm is used to
find the shortest path so there exist
synchronization between directed diffusion
(DD)
and
ACO
algorithm.[7]
CONCLUSION
In this paper, we have presented an Ant
colony optimization based routing to reduce
the power consumption for wireless sensor
network. The proposed method first searches
for the shortest path using AODV algorithm
then rout the data among the nodes within
the x, y and z plane efficiently. The
proposed algorithm obtains more balanced
transmission among the nodes and reduces
the power consumption of the network
REFERENCE
1.
2.
3.
4.
5.
6.
7.
Shih, H.C., power Reduction of Wireless
Sensor Networks Using Ant Colony
Optimization. 2010.
Perkins, C.E., Ad Hoc on Demand
Distance Vector Routing. 1999.
C.Intanagonwiwat , R.G., Directed
diffusion for wireless sensor networking.
2003.
C.Mandal, R.M.a., Ant-aggregation: Ant
Colony Algorithm for optimal data
aggregation in Wireless Sensor
Networks. 2006.
Gamberdella, M.D.a.L.M., Ant colony
system a cooperative learning approach
to the travelling salesman problem.
1997.
M.Dorigo, V.M., The ant system
optimization by a colony of cooperating
agents. 1996.
Kumar, V.K.a.P.R., power control and
clustering in ad hoc networks. 2003.
3.
4.
5.
6.
7.
C.Intanagonwiwat , R.G., Directed
diffusion for wireless sensor networking.
2003.
C.Mandal, R.M.a., Ant-aggregation: Ant
Colony Algorithm for optimal data
aggregation in Wireless Sensor
Networks. 2006.
Gamberdella, M.D.a.L.M., Ant colony
system a cooperative learning approach
to the travelling salesman problem.
1997.
M.Dorigo, V.M., The ant system
optimization by a colony of cooperating
agents. 1996.
Kumar, V.K.a.P.R., power control and
clustering in ad hoc networks. 2003.