zone based ant colony routing in manet[report]

1Zone Based Ant Colony Routing In MANET

1. INTRODUCTION.............................................................................................................................3

2. OVERVIEW OF ANT COLONY ROUTING ALGORITHM.........................................................................4

2.1 The Shortest Path Problem In A Real Ant Colony.........................................................................4

3. POSANT ROUTING ALGORITHM.....................................................................................................6

3.1 Zones In Posant:..........................................................................................................................6

3.2 Route Establishment In Posant:...................................................................................................7

4. ZONE BASED ANT ROUTING USING CLUSTER................................................................................9

4.1 Phases Of Zone Based Clustering Algorithm................................................................................9

4.1.1) Max-Min D-Cluster Formation Algorithm...........................................................................9

4.1.2) Zone Formation algorithm:...............................................................................................11

4.1.3) Cluster Maintenance protocol using GPS technology.......................................................12

4.2 Route Establishment Procedure of Zone based ANT Colony:....................................................13

4.2.1) Route establishment Algorithm of Zone based ANT Colony:............................................14

4.2.2) Route establishment Flow Chart of Zone based ANT Colony:...........................................15

5. COMPARATIVE STUDY OF OVERHEAD OF POSANT WITH THAT OF ZONE BASED ANT COLONY ROUTING.............................................................................................................................................16

5.1) Overhead Comparison..............................................................................................................16

5.2. HOP Count Comparison............................................................................................................18

5.3 Delay Comparison.....................................................................................................................19

5.4 Throughput Comparison............................................................................................................19

5.5. Advantage of Zone based ANT colony over POSANT:...............................................................21

6.CONCLUSION....................................................................................................................................22

REFERENCES........................................................................................................................................23


1. INTRODUCTION

Wireless Sensor Network is a collection of wireless, uniquely addressable sensor devices hich

dynamically form a temporary network, without using any existing network infrastructure or

centralized administration. Each node in the network effectively becomes a router, and

orwards packets towards the packet’s destination node.Wireless sensor networks are

characterized by frequently changing network topology, multi-hop wireless connection and

the need of dynamic, efficient routing protocols. . Routing is a major challenging problem in

mobile adhoc networks because of mobile nodes, unstable links and limited resources.

Ant colony optimization (ACO) is a stochastic approach for solving combinatorial

optimization problems like routing in computer networks. The idea of this optimization is

based on the food accumulation methodology of the ant community. Zone based routing

algorithms is build on the concept of individual node’s position for routing of packets in

mobile ad-hoc networks. Here the nodes’ position can be further utilized to discover routes

by the Ants in optimized way. Position based routing algorithms (POSANT) had some

significant loopholes to find route like it never guarantees the route would be the shortest one,

in cases while it is able to find it. The routing algorithms which are based on ant colony

optimization find routing paths that are close in length to the shortest paths. The drawback of

these algorithms is the large number of control messages that needs to be sent or the long

delay before the routes are established from a source to a destination.

This paper describes Zone based Ant Colony routing algorithm using Cluster in mobile ad-

hoc network, which assures to find shortest route using the DIR principle. In this principle,

the source or intermediate node transmits message to several neighbours and the node whose

direction is closest to the direction of destination gets selected as the next hop forwarding

node,together with minimum overhead for route discovery and mobility management. Unlike

other Zone based approach, in clustering it is not required to consider zone related

information of each node while finding shortest path. This algorithm combines the concept of

Ant Colony approach and Zone based routing approach using clustering to get shortest path

with small number of control messages to minimize the overhead.It uses the concept of

clustering in mobile ad-hoc network. Those clusters will be controlled by cluster heads.


2. OVERVIEW OF ANT COLONY ROUTING ALGORITHM

Ant colony algorithm (ACO) is a stochastic approach for solving combinatorial optimization

problems like routing in computer networks.ACO is distributed and adaptive in finding

shortest paths from source to destination nodes while also constructing a low cost overlay

routing network. The idea of this optimization is based on the observation of how ants

optimize food gathering in the nature. Each node can take itself some stochastic decision

based on some specific attributes supplied to the node .

Ant colony algorithms get there inspiration from the behaviour of real ants gathering for

food. Individual ants act as simple agents that hunt for food by following pheromone trails

and depositing pheromone along the path taken. An ant is more likely to follow a trail with a

high concentration of pheromone. Pheromone on shorter paths gets increasingly reinforced as

more ants follow the higher concentration of pheromones. This is due to more ants being able

to travel a shorter path than a longer path over a given period of time .

2.1 The Shortest Path Problem In A Real Ant Colony

A real ant colony is able to find food and follow the shortest path from the nest to the food.

As a real ant moves, it deposits a substance called pheromone on the ground. When an ant

reaches a point that has more than one outgoing branch, the probability that a branch will be

selected by an ant is dependent on the amount of pheromone deposited on each branch. An

ant will select a branch and deposit more pheromone on this branch; as a result, the

probability of selecting this branch will increase. The pheromone on the branches of the

shortest path to the food will grow faster than pheromone on other branches. The pheromone

is evaporated over time, allowing the system to forget old paths and helping to avoid quick

convergence to a sub-optimal solution. To highlight how the ant colony finds the shortest

path, an example is described in Fig. 1.

Figure 1.ant colony shortest path problem


In Fig. 1-a, two ants at the nest need to reach the food. No pheromone is originally placed on

the two paths .Each ant selects one of the two paths randomly. The Ants deposit pheromone

trails while moving (Fig. 1-b). The ant that selected the shortest path will arrive first; pick up

the food and returns back following the path with highest pheromone. In this case, ant B will

reach the food first (Fig. I-c) and, in its way back, it will select the path it came from since it

has the highest pheromone and at the same time deposits more pheromone as seen in(FigI-

d).Now when ant A reaches the food, it will also follow ant B’s path since this path has more

pheromone and deposits more pheromone, thus enforcing more the selection of this path.

Gradually, the pheromone on the shortest path will increase as shown in (Fig 1-e).

A single ant is not intelligent, but the ant colony can find the shortest path. As the ants search

for the shortest path, they explore many paths. The longest paths and unexplored paths still

have a probability to be visited. If the shortest path fails, a recently explored path will be

followed by the ants. Even if , the first ants used the longer path, the ant colony is able to find

the shortest one as the pheromone evaporates with time and the shortest path still has a

probability to be visited.The real ant colony is a dynamic self-built and self configured

system, which is capable of solving its problems efficiently. These features of real ant colony

system are matching the requirements of the MANETs. By pheromone reinforcement, the

path used from the source node to the destination node attracts more ants and data packets.

As time passes, the pheromone concentration on theshorter path will be higher than that on

the longer path, because the ants using the shorter path will increase the pheromone

concentration faster. The shortest path will thus be identified and will become the only path

used by all ants eventually. This leads to a problem, called stagnation. In pheromone control,

there are several approaches, such as evaporation, aging and limiting and smoothing

pheromone. Evaporation is used to reduce the effect of past experience and aging is used for

controlling the amount of pheromone deposited for each ant according to its age. Limiting is

a scheme that sets maximum pheromone value to be deposited to make the preference of an

ant for optimal paths over nonoptimal paths is reduced.Pheromone smoothing places a

relatively greater reduction in the reinforcement of pheromone concentration on the optimal

paths. Pheromone-heuristic control uses not only the deposited pheromone value but also

heuristic function to choose the next hop.


3. POSANT ROUTING ALGORITHM

In position based routing algorithm the destination node is known and addressed by means of

its location. Routing is performed by a scheme that is based on this information, which is

generally classified as position-based scheme.POSANT is ant colony optimization based

routing algorithm which uses location information to improve its efficiency. POSANT is able

to find optimum or nearly optimum routes when a given network contains nodes.

3.1 Zones In Posant:

Consider a destination node D and a network graph G. For each node S,we partition its

neighbours into 3 zones called zone1, zone2 and zone3. Consider a line segment between S

to D. For a neighbours H of S, angle θH is defined as the angle between line segments SH

and SD. Node H belongs to zone1 if θH ≤ π/4, zone2 if π/4 < θH < 3π/4, and zone3 if 3π/4 ≤

θH ≤ π, see Fig below.

Figure 2 Different zones of network N for destination node D

3.1.a Zone Selection Algorithm of POSANT:

if (abs(θH) >= z5)

θH = abs(θH) - z5;

if(((abs(θH) < z1) && (abs(θH) >= 0.0)) || ((abs(θH) > z4) &&

(abs(θH) <= z5)))


ret = 1;

elseif (((abs(θH) < z2) && (abs(θH) >= z1)) || ((abs(θH) > z3)

&& (abs(θH) <= z4)))

ret = 2;

elseif((abs(θH) <= z3) && (abs(θH) >= z2))

ret = 3;

return ret;

Where, z5 = 2π, z4 = 7π/4, z3 = 5π/4, z2 = 3π/4, z1 = π/4.

3.2 Route Establishment In Posant:

Consider a set of data packets coming to source node S where the destination address is D. To

establish a route, S launches n forward ants with unique sequence numbers for each zone (3n

ants). Assigning very large value to n increases the overhead of the algorithm without any

significant improvement. Similar to other ACO routing algorithms, at each node a forward

ant makes a stochastic decision which is based on the values of pheromone trails to select the

next hop.The values of pheromone trails are stored in a table called pheromone trail table at

each node according to the zone value of the particular node.

Suppose that a forward ant is currently residing in node S and this node has k neighbours H1,

H2,...,Hk. Among them let H1, H2,..., Hi are in Zone 1, Hi1, Hi2,...,Hj are in Zone 2 and Hj1,

Hj2…. Hk are in Zone 3. S launches 3 ants in each zone. The ant in zone 1 will take

stochastic decision to find the next hop depending upon value of pheromone trail of its

outgoing link. As discussed above, value of pheromone trail of zone 1 will be greater than

that of other zone. So ant will follow the zone 1 go get the shortest path. Similarly, ants of

zone 2 and zone 3 will follow the path of 2nd and 3rd priority.

In the Figure below, according to the POSANT concept, ant of zone 1 chooses A as next hop

and it follows path Source -> A -> B -> L. But the path comes to a dead end and ant 1 lost

because it already traverse node B and no other path is there from node l. But the ant of zone

2 gets the path from source to destination.


Figure 3.Traversal of ants in each zone

In addition to the pheromone trail table discussed above, each node maintains another table

which we call Back Routing (BR) table. Whenever a forward ant enters a node from one of

its neighbours, an entry in the BR table will be created that stores the identifier of the

neighbour which the forward ant is coming from, the sequence number of the ant and the

identifier of the destination. Repeated forward ants will be destroyed. When a forward ant

reaches the destination, it is destroyed and a backward ant is sent back to the source. This

backward ant has the same sequence number as the corresponding forward ant and traverses

the same path to the source using the information stored in BR tables. Moving from node B to

node A, the backward ant increases the amount of pheromone stored in AB. An evaporation

process causes the amount of pheromone deposited in each link to decrease as the time passes

on .


4. ZONE BASED ANT ROUTING USING CLUSTER

Clustering means grouping of nodes in the network. This grouping depends upon

transmission range and number of hop in a group. Each node group will have a group head

called Cluster head having the responsibility of communication among its member nodes and

other cluster heads. Cluster head should contain address of its member nodes as well as that

of other cluster heads. Member nodes need to store address information of their cluster head

and neighbour nodes. When information needs to pass from one node to another, member

node sends this information to its corresponding cluster head, which decides whether the

destination is a member or not. If yes, it directly sends the information to destination. If no, it

sends the information alone with the destination node to other cluster heads which then start

to search in their own cluster.

4.1 Phases Of Zone Based Clustering Algorithm

The Zone Based Clusering Algorithm has three major phases,and they are:

Phase I :Cluster Formation

Phase II : Zone Formation

Phase III : Mobility Management of Cluster Heads through Selection of Surrogate

Heads

4.1.1) Max-Min D-Cluster Formation Algorithm

The heuristic has four logical stages:

1. Propagation of larger node ids via floodmax,

2. Propagation of smaller node ids via floodmin,

3. Determination of clusterheads,

4. The linking of clusters.

Initial Cluster formation:

At the time of initialization of this algorithm, transmission range and number of hop (d)

should be mentioned so that cluster can be formed and cluster heads can be selected for those

clusters.

Data Structures

The heuristic runs for 2d rounds of information exchange. Each node maintains two

arrays,WINNER and SENDER, each of size 2d node ids: one id per round of information

exchange.The WINNER is the winning node id of a particular round and used to determine


the clusterhead for a node.The SENDER is the node that sent the winning node id for a

particular round and is used to determine the shortest path back to the clusterhead, once the

clusterhead is selected.

Basic Idea

Initially, each node sets its WINNER to be equal to its own node id. This is followed by the

Floodmax phase.

Floodmax

Each node locally broadcasts its WINNER value to all of its 1-hop neighbours. After all

neighbouring nodes have been heard from, for a single round, the node chooses the largest

value among its own WINNER value and the values received in the round as its new

WINNER. This process continues for d rounds.

Floodmin

This follows Floodmax and also lasts d rounds. It is the same as Floodmax except a node

chooses the smallest rather than the largest value as its new WINNER

Algorithm:

Step I: At some common epoch each node initiates 2d rounds of flooding of

information exchange (node id) where d is the given heuristic. In this algorithm,

flooding occurs once, at the time of initial cluster formation. Each node maintains a

logged entry of two arrays, WINNER and SENDER to store the results of each

flooding round.

Step II: Initially each node sets its winner to be equal to its own node id.

Step III: This is the phase for FLOODMAX where a node chooses the largest value

among its own WINNER array and this process continues for d rounds.

Step IV: This FLOODMIN phase follows FLOODMAX where a node chooses the

smallest rather than the largest value as its new WINNER.

Step V: After these two d rounds of information exchange a node is able to determine

its cluster-head .

Figure below shows the clusters formed when the heuristic terminates.


Figure 4. cluster formation in a network of 25 nodes.

4.1.2) Zone Formation algorithm:

Step I: The cluster head broadcasts get_Position_forAll ( ) request message along

with its own GPS to get percolated within d hop.

Step II: All member nodes in turn unicast back the message node_GPS ( ) to the

cluster-head using geographical routing.


Step III: Cluster-head receives all the GPS values of its members and calculates

the maximum limiting coordinates for Left, Right, Up and Down values to define

its boundary.

Step IV: The cluster head then broadcasts the message get_Boundary_values ( )

within the d hop transmission range to notify all the member nodes about the

cluster boundary.

Step IV: All the member nodes become alerted about the rectangular cluster

boundary information, which can be verified while changing their positions and

these geographical formed boundaries are considered as zones.

4.1.3) Cluster Maintenance protocol using GPS technology

In ad hoc networks, because node can move arbitrarily, any node can leave its cluster

boundary. The problem of node mobility is much more alarming when the cluster head itself

becomes mobile and thereby generating the necessity of periodic re-clustering.In the cluster

management protocol any node including the cluster head automatically gets alarmed while

crossing the geographical boundary of a cluster using the cluster management protocol.There

is a program that continuously compares the current GPS value of the node with that of

boundary values. Thus it is quite easy for a departing cluster head to make a timely

arrangement for rebinding with new cluster head and unbind with old one. The cluster head

can select any of its current cluster members for delegating the cluster headship and thus can

handover the entire cluster head responsibility to facilitate the process of data

communication.This paper considers two different schemes for selecting surrogate head.

If surrogate head can be selected from the middle of the cluster then, the chance of

this new cluster head to cross the cluster boundary gets reduced and as such the

duration of a node to remain as cluster head increases, but the traffic overhead

involved for handover of headship is much higher due to multihop data transfer.

In the second scheme, when the departing head can select any of its 1-hop neighbours

as surrogate head and as such there is no need to concern about their position. Here

the initial overhead for data transfer is much less due to 1-hop data transfer.But as the

surrogate head lies within the vicinity of cluster boundary the chance of change of

cluster head in near future also gets increased.

This entire process of using surrogate head highly reduces the need for cluster head

reelection and there by decreasing the network traffic load involved. When any cluster head

crosses its boundary, surrogate head will be chosen to take the responsibility of the departing


cluster head. Thus there is no requirement of repetition of flooding for the purpose of

reclustering.So, at the time of overhead calculation we need to consider flooding overhead

once that is, only during the initial cluster formation.

4.2 Route Establishment Procedure of Zone based ANT Colony:

Consider the source node S in Figure 4, has a set of data packets which should be forwarded

to the destination node D.S will contact with its Cluster head to confirm whether the

destination node is within the same cluster. If yes, then data packet is directed towards the

destination node through the cluster head.If the destination node is not in the same cluster of

the source node, then cluster head of the source node creates n – 1 number of forward ants

with unique sequence number and send those ants to each and every reachable cluster

heads.Here n is the number of reachable clusters from the source’s cluster head including its

own cluster. Whenever a forward ant enters a node from one of its neighbours, an entry in the

Backward Routing table will be created..After getting destination id from the forward ants,

each cluster head start searching the destination id within its member list. If any of the cluster

heads finds that the destination id is its member node, then it destroys the forward ant and

creates backward ant. This backward ant has the same sequence number as the corresponding

forward ant and traverses the same path to the source using the information stored in

Backward Routing tables.

Figure 5.Zone based Ant Colony Routing using Clustering concept.


4.2.1) Route establishment Algorithm of Zone based ANT Colony:

Let us assume that, Source S has message M to send the destination node D. Then the Route

establishment Algorithm steps are:

1. S unicast data-packet DP to ClusterHead(S) [Data Packet DP consists of Destination

Node address + Message M which need to be delivered to D].

2. CH(S) searches its Member List Table to see if D is a member of CH(S)

3. If CH(S) finds that D is its own member, send feedback to S and deliver the message

M to D

4. If CH(S) finds that D is not its own member, CH(S) generates (n-1) forward ants with

unique sequence numbers to send them to (n-1) reachable Cluster heads through

multi-hop paths.

5. Each forward Ant, is forwarded towards boundary nodes of Cluster(S).

6. From these boundary nodes, forward Ants are sent to those neighbour nodes, which

are boundary nodes of other clusters.

7. From this boundary nodes of other clusters, forward ants travels to the Cluster head of

other Clusters.

8. When forward Ant comes to a Cluster head, sequence number of this forward ant is

stored in this Cluster head to keep track of duplicate entry.

9. Each Cluster head checks the sequence number of the forward ant and see if it already

has the sequence stored in it.If no, it searches its Member List Table to see if D is its

member. Otherwise it kills the forward and as it has already searches for this ant.

Thus this algorithm avoids loops.

10. When any of the Cluster heads finds that D is its member, it kills the forward Ant and

generates Backward Ant with same sequence number.

11. Cluster head which has D as its member node,delivers the message M to D.

12. Then sends back the Backward Ant to the Source node S as a feedback.

13. When a Cluster head finds that D is not its member,it sends the forward ant to its

neighbour cluster to search.


4.2.2) Route establishment Flow Chart of Zone based ANT Colony:

Figure 6.Route establishment Flow Chart of Zone based ANT Colony

Abbreviation used in the Flow Chart:

DP: Data Packet. CH(S): Cluster head of Source Node. T: Total number of cluster heads

BN: Boundary Notes. FAnt: Forward Ant

Cnt: Counter. When forward Ant comes to a CH, this counter should be increased by 1.

Seq no: Unique sequence number of ant.


5. COMPARATIVE STUDY OF OVERHEAD OF POSANT WITH THAT OF ZONE BASED ANT COLONY ROUTING

In POSANT algorithm the overhead is coming from three paths that have been considered

there between source to destination node. Total overhead in POSANT should be sum of

overhead of all the paths. In Zone based Ant Colony, if source and destination node are not in

the same zone then cluster head of the source node will send ants to all other cluster heads to

find the destination node. So, if n number of zones formed in the network, n number of

cluster heads will be there and (n – 1) number of ants will be sent by source cluster head to all

other cluster heads, so total over head will be sum of overhead of all (n-1) paths.

5.1) Overhead Comparison

a) By varying Zone size:

In Zone based ANT colony using clustering, we can increase the Zone size by increasing the

HOP value of cluster value. Thus, by increasing zone size we can reduce number of zones in

this network. By reducing number of zones we can reduce the number of forward ants which

will be sent from source cluster head to all other reachable cluster head to find out the

destination node’s cluster head. So, in other word we can reduce overhead of forwarding ants

in zone based ANT colony by increasing HOP value. Though it will not affect overhead of

POSANT as it does not depends upon HOP value.Fig. 6 shows the comparison table and

graph between POSANT and Zone based ANT colony with varying HOP value.

Figure 7. Zone size Vs Overhead


b) By varying Node Number:

In Zone based ANT colony when number of nodes will be increased, more number of nodes

will be there in each zone and will increase zone concentration and will be taken care by

corresponding cluster head. As here HOP value D is fixed,number of zones and zone size will

not be increased. So,number of ants traversing from one cluster head to another cluster head

will not be increased. Only concentration of node within each zone will be creased. It may

increase responsibility of each cluster head but at the same time increment of overhead will

be less than that of POSANT. In POSANT when number of nodes is increased ANT needs to

traverse more number of nodes to reach the destination node.Fig. below shows the graph

representing the comparison between POSANT and Zone based ANT colony with varying

node number.

Figure 8. Number of Node Vs Overhead

c) By varying Mobility:

In case of Zone based ANT colony, when the destination node will leave its zone due to

mobility and enter into a new zone, previous cluster head will reply back to the source’s

cluster head that destination node is not its member node.After getting this message, source

cluster head will forward ants towards all reachable cluster heads to know the current zone of

destination node. Where as in POSANT, source node will come to know the location change

information of the destination node or any other node in the path from source to destination

after a certain period (time out period) when the sender of the source node will not get any

acknowledgement message from the receiver of destination node. After realizing that, source

node again have to start POSANT algorithm by sending ants in three zones as mentioned

above. As POSANT algorithm needs more overhead to execute than of Zone based ANT

colony, mobility factor affect POSANT much more than Zone based ANT colony. Fig. shows


the graph representing the comparison between POSANT and Zone based ANT colony with

varying node number.

Figure 9. Mobility Vs Overhead:

5.2. HOP Count Comparison

a) By varying number of nodes

In Zone based ANT colony when we increase number of nodes ant has to traverse more of

nodes, only concentration of zone will be increased. As here HOP value is fixed, zone size

will not be increased.Number of zones will also be unchanged. So, number of ants traversing

from one cluster head to another cluster head will not be increased. As zone concentration

will be increased, ant has to traverse more number of nodes within the zone of destination

node. Whereas in POSANT, increment of number of nodes will result increment of HOP

count for the entire network. As a result ant has to traverse more number of HOP.Fig. shows

the graph representing the comparison between POSANT and Zone based ANT colony with

varying node number.

Figure 10 Number of Node Vs HOP Count:


b) By varying mobility:

In Zone based ANT colony, while location of destination node or any other member node

changes, Cluster head gets this information and guide ants to go to right direction. Where as

in case of POSANT, when position of node changes due to mobility, ants need to start

searching in the three zones again from the scratch. So, increment of mobility causes much

more increment of HOP count in case of POSANT than Zone based ANT colony.Fig. shows

the comparison table and graph between POSANT and Zone based ANT colony with varying

mobility.

Figure 11 Mobility Vs HOP Count:

5.3 Delay Comparison

In a network, if the diameter of the network increases or the density of node increases, ant or

data packet needs to traverse more number of HOP to reach the destination and that causes

increase in processing delay at each node. Thus the overall delay in delivering the data

packets gets increased. So we can ay that delay is proportional to HOP Count of a

network.Simulation results of HOP count can throw light on the variation of delay in the

network

5.4 Throughput Comparison

a) By varying number of nodes

In POSANT, when number of node increases, concentration of the whole network increases.

In case of Zone based Ant colony, when number of node increases, number of zones and zone

size will remains same. But concentration of each zone will be increased. So when Ant

traverses in a zone with higher concentration, the probability of getting lost will be decreases.


That increases throughput.Fig. shows the comparison table and graph between POSANT and

Zone based ANT colony with varying node number.

Figure 12 Number of Node Vs Throughput:

b) By varying Mobility:

In Zone based ANT colony, while location of destination node or any other member node

changes, Cluster head gets this information and guide ants to go to right direction. Where as

in case of POSANT, when position of node changes due to mobility, ants need to start

searching in the three zones again from the scratch which causes more data loss. So,

increment of mobility causes much more decrement of throughput in case of POSANT than

Zone based ANT colony.Fig. shows the comparison table and graph between POSANT and

Zone based ANT colony with varying mobility.

Figure 13 : Mobility Vs Throughput:


5.5. Advantage of Zone based ANT colony over POSANT:

This algorithm will provide advantage of both ant colony and zone based algorithm. Like ant

colony algorithm, here we need not store large routing tables in nodes, we need to store only

neighbouring node information and previous traversed node information. As nodes in mobile

ad-hoc network will have memory of small storage capacity, it would be tough to store large

routing table inside each node. In our zone based ant colony algorithm there will be cluster

head available within each zone. As clustering concept, only cluster head node need to store

path information of its member nodes as well as zone boundary information for all other

clusters in the network.In POSANT routing, like all other position based routing,source node

need to know the position of the destination node.Robustness is a problem in position based

ant colony algorithm. The use of position of destination node causes problem in terms of

reliability.

Overhead

Overhead of Zone based ANT colony is much less than that of POSANT,

Increment of overhead due to increment of node number and mobility is much

more in POSANT than that of Zone based Ant system. Decrement of overhead

due to increment of HOP value is much more in Zone based ant system than that

of POSANT.

HOP Count

HOP Count of Zone based ANT colony is less than of POSANT, as we can see

from the results of simulations on varying Number of nodes, Mobility and Zone

Size (HOP value).

Throughput

Throughput of Zone based ANT colony is better than of POSANT, because in

Zone Based Algorithm, data packets need not traverse to each individual node.

Cluster heads will decide whether data packets should traverse to a particular zone

or not. So chances of loss are less than of POSANT.


6.CONCLUSION

Zone based routing using ant colony optimization aims to support zone based routing with

minimum routing overhead for mobile ad hoc networks. The idea behind this zone based

routing is to allow the nodes within each zone to get referred with the zone boundaries and

control the node. Ant Colony technique merged with Zone based technique to optimize

memory utilization, overhead and throughput.In zone based routing, each zone member is

aware of its mobility using the zone boundary values. It allows the node to inform about their

mobility and thereby enabling the protocol to find the mobile destination quickly with

minimum overhead. A cluster head is available within each zone to perform data transmission

and routing. The performance analysis shows that, in highly mobile network, Zone based ant

colony reduces overhead, Hop value and increases throughput than POSANT in all three

cases (varying node number,mobility and Zone size). So in conclusion, we can say that Zone

based ANT colony is better than POSANT.

Future Work

Zone Based ANT Colony routing algorithm, always uses shortest path for routing the data

packets. Ants always traverse the shortest path from source to destination through the source

cluster head and destination cluster head using geographical shortest path forwarding

technique. So here always the shortest path is selected for routing and load balancing concept

has not been incorporated in this routing algorithm.Load balancing can be introduced by

selecting second shortest path sometimes in case we have large number of data packets to

send so that the same set of nodes should not remain selected in the route path always. Thus

load balancing can also be handled in Zone based Ant colony algorithm. This can be

implemented as a future work.


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zone based ant colony routing in manet[report]

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