REDUCING THE RESOURCE INTENSITY IN ANTNET FOR MOBILE AD-HOC NETWORKS

AntNet(1) is one of the approaches to adaptive learning of routing tables in wide are best-effort datagram networks. The AmNet algorithm, as proposed by Di Caro and Dorigo is resource intensive algorithm. In this algorithm two types of ants are generated viz. Forward ant and Backward ants. While traveling from a source to a destination the forward ants store their paths and of traffic conditions they encounter. After reaching the destination the forward ant transfers its memory to the backward ant and dies. The: backward ant retraces the path traversed by the forward ant and updates the routing tables in the path. Since the mobile adhoc networks are dynamic and every effort should be made to reduce the resource intensity. We have made an effort to reduce the resource intensity in the basic AmNet algorithm. We propose three methods. The first method experiments with not generating a backward ant for every forward ant, instead we collect a set of forward ants and generate a subset of backward ants by choosing the best trip times among the received forward ants. In the second method we update the routing tables with the help of forward ants without generating backward ants. In the third method we update the routing table only at the destination node. Since every node is a destination for all other nodes, routing table at every node gets updated. In this method also no backward ants are generated. At the end we compare our results with the basic AmNet algorithm results.