power reduction using ant colony optimization based routing protocol

8/6/2019 Power Reduction Using Ant Colony Optimization Based Routing Protocol

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Power Reduction Using Ant Colony Optimization Based

Routing Protocol

Umair Ahmed Zubair Ahmed

Dept.of computer Sciences, Dept.of computer Sciences

Shaheed Zulfiqar Ali Bhutto Institute of Shaheed Zulfiqar Ali Bhutto Institute of

Science and Technology, Pakistan Science and Technology, Pakistan

[email protected] [email protected]

AbstractPower consumption is alwaysremained 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.

Keywordsant colony optimization, power

reduction, wireless sensor networks, routing

INTRODUCTION

Wireless sensor networks industry has beendeveloped 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

nodes so power consumption is always remainan 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 basicallyused 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 discoverySource node Sfloods 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


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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 forthe 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

path. A set of cooperating agents tends to

find the shortest path for the travelling sales

man person called ants. These cooperatingagents 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]

PROPOSED METHOD

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

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


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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 dont

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.

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. The state transition rule transformsthe one state of the system to otherstate, exploits the new edges and

keeps the balance between the

accumulated knowledge and

exploiting the priori technique.

II. 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.

III. Local pheromone updating rules areused to construct the solution.[6]

2- Adhoc on demand vector routing has

some upper hand over the dynamic source

routing in three ways:

I. The larger the size of the header themore complex will be

communication, DSR contains the

established path in packet headers.

II. .In DSR each node who wants tocommunicate 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 dont

have to contain the information inits header.

III. AODV only send the data to thosenodes 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]


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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. Shih, H.C.,power Reduction of Wireless

Sensor Networks Using Ant Colony

Optimization. 2010.

2. Perkins, C.E.,Ad Hoc on Demand

Distance Vector Routing. 1999.

3. C.Intanagonwiwat , R.G., Directed

diffusion for wireless sensor networking.

2003.

4. C.Mandal, R.M.a.,Ant-aggregation: Ant

Colony Algorithm for optimal data

aggregation in Wireless Sensor

Networks. 2006.

5. Gamberdella, M.D.a.L.M.,Ant colony

system a cooperative learning approach

to the travelling salesman problem.

1997.

6. M.Dorigo, V.M., The ant system

optimization by a colony of cooperating

agents. 1996.

7. Kumar, V.K.a.P.R.,power control and

clustering in ad hoc networks. 2003.

3. C.Intanagonwiwat , R.G., Directed

diffusion for wireless sensor networking.

2003.

4. C.Mandal, R.M.a.,Ant-aggregation: Ant

Colony Algorithm for optimal data

aggregation in Wireless Sensor

Networks. 2006.

5. Gamberdella, M.D.a.L.M.,Ant colony

system a cooperative learning approachto the travelling salesman problem.

1997.

6. M.Dorigo, V.M., The ant system

optimization by a colony of cooperating

agents. 1996.

7. Kumar, V.K.a.P.R.,power control and

clustering in ad hoc networks. 2003.

power reduction using ant colony optimization based routing protocol

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