1. ZONE BASED ANT COLONY ROUTING IN MANET
@ Ants are good citizens, they place group interests first
By :- Kumar Bhargava
Roll no :- cs-09-65
Reg.no. :-0901230399
Comp. sc. engineering
GUIDED BY: MISS B.SAHOO
SYNERGY INST OF ENGINEERING AND
TECH.(ORISSA,CUTTACK) INDIA.

2. ABSTRACT
Mobile ad-hoc networks (MANETs) are a collection of
mobile nodes communicating wirelessly without a
centralized infrastructure. The biggest challenge in
MANETs is to find a path between communicating
nodes, that is, the MANET routing problem.
The considerations of the MANET environment and the
nature of the mobile nodes create further complications
which results in the need to develop special routing
algorithms to meet these challenges. Swarm
intelligence, a bio-inspired technique, which has proven
to be very adaptable in other problem domains, has been
applied to the MANET routing problem as it forms a
good fit to the problem.

3. ZONE BASED ANT COLONY ROUTING IN MANET
ZONE BASED ANT COLONY
ROUTING
IMPLIMENTATION OF
ARA IN MANET
MANET
ARA : @ ANT COLONY ROUTING ALGORITHIM

4. ZONE
We can define zone as an area with particular
characteristic or a particular use.
or
A zone is a group of interfaces that have similar
functions or features.
ROUTERS
ROUTERS
ZONE :1 ZONE:2

5. ANT COLONY ALGORITHIM
***The ant colony optimization meta-
heuristic is a particular class of ant
algorithms. Ant algorithms are multi-
agent systems, which consist of agents
with the behavior of individual Ants.
The basic idea of the ant colony
optimization meta-heuristic is taken
from the food searching behavior of real
ants.
In computer science, metaheuristic designates a computational method that
optimizes a problem by iteratively trying to improve a candidate solution with
regard to a given measure of quality

6. NEST FOOD
The basic idea of the ant colony optimization meta
heuristic is taken from the food searching behavior of real
ants.
This behavior of the ants can be used to find the shortest
path in networks. Especially, the dynamic component of this
method allows a high adaptation to changes in mobile ad-hoc
network topology, since in these networks the existence of links
are not guaranteed and link changes occur very often.

7. ANT COLONY OPTIMIZATION META-HEURISTIC
ALGORITHM…..
Let G = (V,E) be a connected graph with n = |V| nodes. The
simple ant colony optimization meta-heuristic can be used to
find the shortest path between a source node vs and a
destination node vd on the graph G.
The path length is given by the number of nodes on the path.
Each edge e(i, j) ∈ E of the graph connecting the nodes vi and
vj has a variable ϕi,j (artificial pheromone), which is modified
by the ants when they visit the node. The pheromone
concentration, ϕi,j is an indication of the usage of this edge.

8. An ant located in node vi uses pheromone ϕi,j of node
vj ∈ Ni to compute the probability of node vj as next
hop Ni is the set of one-step neighbors of node vi.
The transition probabilities pi,j of a node vi fulfill the
constraint:
During the route finding process, ants deposit pheromone on
the edges. In the simple ant colony optimization metaheuristic
algorithm, the ants deposit a constant amount Δϕ of
pheromone.

9. An ant changes the amount of pheromone
of the edge e(vi, vj) when moving from
node vi to node vj as follows:
ϕi,j := ϕi,j +Δϕ ----------------------(1)
Like real pheromone the artificial pheromone concentration
decreases with time to inhibit a fast convergence of pheromone
on the edges. In the simple ant colony optimization meta-
heuristic, this happens exponentially:
ϕi,j := (1 − q) · ϕi,j, q∈ (0, 1]-------------------- (2)

10. ALGORITHM FOR ACO
Each initiated decision variable Xi = vji is called a solution
component and denoted by cij . The solution is constructed by
incrementally choosing the components from the Graph
G(V,E). As , the components can be associated with either the
vertices or the edges of the graph.
Each component has a pheromone value associated with it ij .
The ants move through the graph, and at each node
probabilistically choosing the next component to add to the
solution determined by the pheromone value of the
components.

11. ALGORITHM FOR ACO
Require: parameters
1 WHILE iterations not complete do
2. construct Solutions;
3. Update Pheromones;
4. Daemon Actions ; {optional}
5. end while

12. (cont.)
The components can be associated with either the vertices or the
edges of the graph. Each component has a pheromone value
associated with it ij . The ants move through the graph, and at
each node probabilistically choosing the next component to add
to the solution determined by the pheromone value of the
components.
Construct Solutions, The choice of the next feasible
component/node and of construction solution is made by the
path selection equation which depends on the ant algorithm
system being used and then solution is deposited.

13. (cont.)
Update Pheromones serves two tasks: To increase the
pheromone values of the components which are good, and to
decrease the pheromone values of the components which are
bad. The pheromone decrease is achieved through
evaporation.
Daemon Actions are usually used to perform centralized
actions that cannot be performed by a single ant and that
may be problem specific.

14. MANET
MANET is abbreviated as mobile ad-hoc
network
what is AD-HOC NETWORK ?
*****An ad-hoc network is a wireless network created for
particular purpose which is of decentralized type.

15. OVERVIEW OF MANET
GENERAL
A wireless ad-hoc network is a collection of mobile/semi mobile
nodes with no pre-established infrastructure, forming a
temporary network. Each of the nodes has a wireless interface
and communicate with each other over either radio or infrared.
Laptop computers and personal digital assistants that
communicate directly with each other are some examples of
nodes in an ad-hoc network. Nodes in the ad-hoc network are
often mobile, but can also consist of stationary nodes, such as
access points to the Internet. Semi mobile nodes can be used to
deploy relay points in areas where relay points might be
needed temporarily.

16. Figure shows a simple ad-hoc network with three nodes. The
outermost nodes are not within transmitter range of each
other. However the middle node can be used to forward
packets between the outermost nodes. The middle node is
acting as a router and the three nodes have formed an ad-hoc
network.

17. Mobile ad-hoc network: MANET
A Mobile Ad Hoc network (MANET) is a collection of wireless
mobile nodes, which dynamically form a temporary network, without
using any existing network infrastructure or centralized
administration.
Routing in mobile ad hoc networks:
• Each node is host and router,
• No infrastructures or centralized control
• Nodes might move and join and leave the network at any time
• One shared communication medium
• Short range and noisy transmissions
• Very dynamic and spatial-aware problem

18. 100 MILLION $ QUESTION
THERE ARE MANY ALGORITHM PRESENT IN THIS
COMPUTER WORLD….
Y ?
ONLY
ANT COLONY OPTIMIZATION TECHNIQUE

19. CHALLENGES IN MANET
• distributed state in unreliable environment
• dynamic topology
• limited network capacity
• wireless communication
1. variable link quality
2. interference and collisions

20. WHY ANT COLONY OPTIMIZATION
META- HEURISTIC SUITS TO AD-HOC
NETWORKS
• Dynamic topology: This property is responsible for the
bad performance of several routing algorithms in mobile
multi-hop ad-hoc networks. The ant colony optimization
meta-heuristic is based on agent systems and works with
individual ants. This allows a high adaptation to the current
topology of the network.

21. • Link quality: It is possible to integrate the connection/link
quality into the computation of the pheromone
concentration, especially into the evaporation process. This will
improve the decision process with respect to the link quality. It
is here important to notice, that the approach has to be
modified so that nodes can also manipulate the pheromone
concentration
independent of the ants, i.e. data packets, for this a node has to
monitor the link quality.

22. • Support for multi-path: Each node has a routing table with
entries for all its neighbors, which contains also the pheromone
concentration. The decision rule, to select the next node,is based
on the pheromone concentration on the current node, which is
provided for each possible link. Thus, the approach supports
multipath routing.

23. THE ROUTING ALGORITHM
In this section we discuss the adaptation of the ant colony
optimization meta-heuristic for mobile ad-hoc networks and
describe the Ant colony based Routing Algorithm (ARA).
The routing algorithm is very similar constructed as many
other routing approaches and consists of three phases.
Route Discovery Phase
In the route discovery phase new routes are created. The
creation of new routes requires the use of a forward ant
(FANT) and a backward ant (BANT). A FANT is an agent
which establishes the pheromone track to the source node. In
contrast, a BANT establishes the pheromone track to the
destination node.

24. FANT ESTABLISHING THE PHEROMONE
TRACK TO SOURCE NODE
F 5 F
2
F
s 4 D
F
F
1 F
3
F F
6

25. BANT ESTABLISHINGTHE PHEROMONE
TRACK TO THE DESTINATION NODE
B 5 B
2 4 D
S B
B
B
1 3 6
B B

26. ROUTE FAILURE HANDLING
The ARA also handles routing failures, which are caused
especially through node mobility and thus very common in
mobile ad-hoc networks. ARA recognizes a route failure
through a missing acknowledgement. If a node gets a ROUTE
ERROR message for a certain link, it first deactivates this link
by setting the pheromone value to 0. Then the node searches for
an alternative link in its routing table. If there exists a second
link it sends the packet via this path. Otherwise the node
informs its neighbors, hoping that they can relay the packet.
Either the packet can be transported to the destination node or
the backtracking continues to the source node. If the packet
does not reach the destination, the source has to initiate a new
route discovery phase.

27. PROPERTIES OF ANT COLONY ROUTING
ALGORITHM
Distributed operation: In ARA, each node owns a set of
pheromone counter ϕi,j in its routing table for a link between
node vi and vj . Each node controls the pheromone counter
independently, when ants visit the node on route searches.
**************We call the pheromone concentration here as a counter, because of its
regularly decreasing by the node.

28. Locality: The routing table and the statistic information
block of a node are local and they are not transmitted to any
other node.
Multi-path: Each node maintains several paths to a
certain destination. The choice of a certain route depends on
the environment, e.g., link quality to the relay node.
Sleep mode: In the sleep mode a node snoops, only packets
which are destined to it are processed, thus saving power.

29. OVERHEAD OF ANTCOLONY ROUTING
ALGORITHM
The expected overhead of ARA is very small, because there are
no routing tables which are interchanged between the nodes.
Unlike other routing algorithms, the FANT and BANT packets
do not transmit much routing information. Only a unique
sequence number is transmitted in the routing packets. Most
route maintenance is performed through data packets, thus
they do not have to transmit additional routing information.
ARA only needs the information in the IP header of the data
packets.

30. Reference……..
[1] S.Corson. Mobile ad hoc networking (manet): Routing protocol performance issues
and evaluation considerations. http://www.ietf.org/rfc/rfc2501.txt.
[2] Al Huda Amri and et. al. Scalability of manet routing protocols for heterogeneous
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networks. IEEE Wireless Communications, 11(6):54–61, December 2004.
[5] J. Kennedy and R. Eberhart. Particle swarm optimization. In Proceedings of IEEE
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31. ANY QUESTIONS PLZ………?
THANK YOU……………