The ant colony algorithm is an algorithm for finding optimal paths that is based on the behavior of ants searching for food. At first, the ants wander randomly. When an ant finds a source of food, it walks back to the colony leaving "markers" (pheromones) that show the path has food.

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Simulation of Ant colony Algorithm
ABSTRACT
The ant colony algorithm is an algorithm for finding optimal paths that is based on the behavior
of ants searching for food. At first, the ants wander randomly. When an ant finds a source of
food, it walks back to the colony leaving "markers" (pheromones) that show the path has food.
When other ants come across the markers, they are likely to follow the path with a certain
probability. If they do, they then populate the path with their own markers as they bring the food
back. As more ants find the path, it gets stronger until there are a couple streams of ants traveling
to various food sources near the colony.
Because the ants drop at every time they bring food, shorter paths are more likely to be stronger,
hence optimizing the "solution." In the meantime, some ants are still randomly scouting for
closer food sources. A similar approach can be used find near-optimal solution to the traveling
salesman problem. Once the food source is depleted, the route is no longer populated with
pheromones and slowly decays. Because the ant-colony works on a very dynamic system, the ant
colony algorithm works very well in graphs with changing topologies.
EXISTING SYSTEM
Any network consists of a number of nodes likely assumed as sensor network scattered in the
region of interest in order to acquire some physical data. The sensor node should have the ability
of sensing, processing and communicating. A wireless sensor network operates in an unattended
environment, with limited computational and sensing capabilities, and capable of sensing,
computing and communicating wirelessly. In order to effectively utilize wireless sensor nodes,
we need to minimize energy consumption in the design of sensor network protocols and
algorithms. A large number of sensor nodes have to be networked together. Direct transmission
from any specified node to a distant sink node is not used since sensor nodes that are farther
away from the sink node will drain their power sources much faster than those nodes that are
closer to the sink node. Up to now we are using static approaches for determine the optimal path.

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PROPOSED SYSTEM
In this project, we proposed an optimization mechanism in wireless sensor networks
using ant colony algorithm. The proposed mechanism assigned artificial ants to source
nodes to establish low-latency paths between the source nodes and the sink node. Paths
from different source nodes to a sink node form an aggregation tree rooted at the sink
node. But here, we are taken a simple example and calculated the aggregated path from
taken the various samples of path costs. Whenever similar data happen to meet at a
branching node in the tree, the copies of similar data are replaced by a single message.
Here we assumed ten nodes and establish paths between them in dynamic passion and
finding optimum path in spontaneously. If any node receives the data from a neighboring
node, the node will select next node according to the random-proportional rule of our ant
colony algorithm.

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A wireless sensor network consists of a number of sensor nodes scattered in the region of interest
in order to acquire some physical data. The sensor node should have the ability of sensing,
processing and communicating. A wireless sensor network operates in an unattended
environment, with limited computational and sensing capabilities, and capable of sensing,
computing and communicating wirelessly. In order to effectively utilize wireless sensor nodes,
we need to minimize energy consumption in the design of sensor network protocols and
algorithms. A large number of sensor nodes have to be networked together. Direct transmission
from any specified node to a distant sink node is not used since sensor nodes that are farther
away from the sink node will drain their power sources much faster than those nodes that are
closer to the sink node. On the other hand, a minimum energy multi-hop routing scheme will
rapidly drain the energy resources of the nodes, since these nodes are engaged in the forwarding
of a large number of data messages (on behalf of other nodes) to the sink node.
Software Requirements:
# OPERATING SYSTEM : Windows XP
# TECHNOLOGY : J2SDK1.4.1 And above
Hardware Requirements:
# PROCESSOR : Pentium III
# CLOCK SPEED : 550 MHz
# HARD DISK : 20GB
# RAM : 128MB
# CACHE MEMORY : 512KB

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# 412, Annpurna Block, Aditya Enclave, Ameerpet, Hyderabad – 500016
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Mobile: 91-9247249455
# OPERAING SYSEM : Windows 2000 Professional
# MONITOR : Color Monitor
# KEYBOARD : 104Keys
# MOUSE : 3Buttons
MODULE S:
We are having the following modules:
1. Antt network
To construct a Ant colony, we are taken some assumptions like taken an input of a group of ants.
From that we construct a graph internally by using connection between nodes. Here we
constructed a GUI format with ten nodes in static way. To find their states we are using different
colors.
2. Path finder
By using sensor network, we are finding the path from source to destination in different possible
ways. Here we are using this ant colony algorithm for finding a possible path from given sensor
graph.
3. Ant colony algorithm
The ant colony algorithm assigns ants to source nodes the ants search the routes and
communicate with the others through pheromones. Each ant iterates to construct the aggregation
tree where the internal nodes are aggregate points. The ants either try to find the shortest route to

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Mobile: 91-9247249455
the destination and terminate or finds the closest aggregation point of the route searched by
previous ants and terminates. The algorithm converges to the local best aggregation tree. In order
to find the global optimal aggregation nodes, the algorithm iterates on the different nodes located
within the extended routing paths.
The ant colony algorithm includes three steps.
Step 1 is how to select next hop node;
Step 2 is to extend the routing path;
Step 3 is to update the pheromone trails on the sensor nodes.
4. Cost updater
The algorithm builds the extended routing paths for different sources, and there are some nodes
called as aggregation nodes which are overlapped by the two extended routing paths. For the
aggregation nodes, they will send the cost Update packets to their own neighbors to reinforce
their relationships.