An Effective Message Forwarding Algorithm for Delay Tolerant Network with Cyclic Probabilistic Influences

In this paper, we present a disruption-tolerant message forwarding algorithm for optimizing the message delivery ratio of an ad-hoc network in presence of multiple unknown probabilistic influences that are cyclic in nature. Our model assumes each node moves independently from other nodes in the network, and each node's mobility behavior, although not deterministic, can be considered as random variables associated with an unknown sequence of probability distributions that follows a cyclic pattern over time. Based on those assumptions, our algorithm enables any node in the network to estimate the cycle lengths of hidden events that cause variations in each node's mobility behavior using its known mobility history as well as the history information from all peer nodes at its current landmark. Subsequently using such estimates, each node can predict other nodes' most likely trajectories in future and make optimized routing decisions based on these predictions. In this paper, we also report results from experiments that measure the effectiveness of our proposed algorithm by comparing its message delivery ratio with those of two baselines on networks of different sizes.