Using Ant Agents to Combine Reactive and Proactive strategies for Routing in Mobile Ad Hoc Networks Fredrick Ducatelle, Gianni di caro, and Luca Maria Gambardella International Journal of Computational Intelligence and Applications 2005

Outline 1. Introduction 2. Related work 3. Description of the algorithm 4. Experimental results 5. Conclusions

1. Introduction Mobile ad-hoc network

Mobile Ad Hoc Networks (MANETs) – no central control – no designated routers MANET routing algorithms – Reactive behavior : Gathers routing information in response to an event (ex:AODV) – Proactive behavior: Gathers routing information at other times

Present AntHocNet, MANET routing algorithm based on Ant Colony Optimization (ACO) AntHocNet in MANET : – Reactive path finding – Repairing with proactive path maintenance and improvement

2. Related work ACO routing: – Laying-following behavior of ants – Repeatedly sample paths with control packets – Provide automatic load balancing

3. Description of the algorithm 1. Reactive path setup 2. Proactive path maintenance and exploration 3. Stochastic data routing 4. Link failures

3-1 Reactive path setup A source broadcasts a reactive forward ant,when not have routing information for d The routing information of a node i is represented in a pheromone table neighbors of i over which a path to d is known parameter value

If node where no pheromone information available for d, it is broadcast If ant arrives node which was already visited at same generation, it is discarded Construct only one path during the reactive setup phase S D

Each forward ant keeps a list P = [1, 2,…., d] Arrival at the destination d, converted into a backward ant Each intermediate node, the backward ant calculates

This local estimate: The value of the estimate Node j+1 to node j time average time to send one packet number of packets in queue i j k d

Local congestion of the shared medium Backward ant updating the pheromone table entry The time took to send a packet from node i Calculated a running average of the time

travelling time estimated by the ant number of hops fixed value representing the time to take one hop

S I J K L N M D

2. Proactive path maintenance and exploration Update the information about paths, and tries to find new and better ones An important role: hello messages It allow nodes to detect which are their immediate neighbors

New node select K destinations in its pheromone table A node i receiving the hello message from n bootstrapped pheromone

For path maintenance: – Bootstrapped pheromone is used directly – If i already has a pheromone entry – used directly to replace – Pheromone on current paths is kept up-to-date A B C

For path exploration: – Bootstrapped pheromone is used indirectly – If i not have a value for – indicate a possible new path in second table virtual pheromone table A B C

Compares the virtual and regular pheromone If >,sent out a proactive forward ant, but not broadcast At the destination, become backward ant back to the source Turned into a regular path

3.3. Stochastic data routing Nodes forward data stochastically Several paths have similar quality,data will spread over them

The probabilistic routing strategy: – If estimates are kept up-to-date Leads to automatic load balancing If a path is clearly worse than others, it will avoided Try to spread the data load evenly over the network

4. Link failures Nodes can detect link failures In the first place: – Remove form neighbor list – All associated form routing table Further action: – Control packet fail transmission – Data packet fail transmission

Control packet fail transmission Node broadcasts a link failure notification message – Destinations to which the node lost its best path – New best estimated end-to-end delay – Number of hops to this destination

Data packet fail transmission Node broadcasts a route repair ant – Like reactive forward ant – Maximum number of broadcast limit proliferation – Node wait for a certain time – If no report, no alternative path – Sends a new link failure notification

4. Experimental results Qualnet 900 seconds Speed ranging with 0 and 20 m/s (random waypoint mobility model) Three scenarios

A scenario: – 100 nodes – 3000 * 1000 m2 – Source sends one 64-byte packet/second – 2Mbit/s – Radio range 300 meters – Pause time:0-480 pause time:0-480

B scenario: – More intense data traffic – 1000 * 1000 m2 – Source sends eight 64-byte packets – 11Mbit/s – Radio range 110 meters – Pause time:0-480

C scenario: – Vary the number of nodes: – 750*750 m2 to 2250*2250 m2 – Keep 1 node per 100*100 m2 – pause time: constant on 30 seconds

Average delay and delivery ratio for various pause times in 3000 * 1000

Average delay and delivery ratio for various pause times in 1000 * 1000

Average delay and delivery ratio for an increasing number of nodes

5. Conclusions AntHocNet can outperform AODV Future work: – More complex pheromone – The reliability of the bootstrapping process

Average delay jitter in the different scenarios

Overhead in the different scenarios