Routing in Delay Tolerant Networks

Abstract

Delay-tolerant networks (DTNs) have the great potential to connecting devices and regions of the world that are presently under-served by current networks. A vital challenge for Delay Tolerant Networks is to determine the routes through the network without ever having an end to end path, or knowing which routers will be connected at any given instant of time. The problem has an added constraint of limited size of buffers at each node. This situation limits the applicability of traditional routing techniques which categorize lack of path as failure of nodes and try to seek for existing end-to-end path. Approaches have been proposed which focus either on epidemic message replication or on previously known information about the connectivity schedule. The epidemic approach, which is basically a flooding technique, of replicating messages to all nodes has a very high overhead and does not perform well with increasing load. It can, however, operate without any prior information on the network configuration. On the other hand, the alternatives, i.e., having a prior knowledge about the connectivity, seems to be infeasible for a self-configuring network.

In this project we try to maximize the message delivery rate without compromising on the amount of message discarded. The amount of message discarded has a direct relation to the bandwidth used and the battery consumed. The more the message discarded more is the bandwidth used and battery consumed by every node in transmitting the message. At the same time, with the increase in the number of messages discarded, the cost for processing every message increases and this adversely affects the nodes. Therefore, we have proposed an algorithm where the messages are disseminated faster into the network with lesser number of replication of individual messages. The history of encounter of a node with other nodes gives noisy but valuable information about the network topology. Using this history, we try to route the packets from one node to another using an algorithm that depends on each node’s present available neighbours/contact and the nodes which it has encountered in the recent past. We have also focused on passing the messages to those nodes which are on the move away from the source/forwarder node, as the nodes moving away have a greater probability of disseminating the messages throughout the network and hence increases chances of delivering the message to the destination.