Dynamic probabilistic broadcast for MANETs

Abstract

Self-pruning broadcasting algorithm exploits neighbor knowledge to reduce redundant retransmissions in mobile ad hoc wireless networks (MANETs). Although in self- pruning, only a subset of nodes forward the message based on certain forwarding rule, it belongs to one of the reliable broadcasting algorithm category where a broadcast message is guaranteed (at least algorithmically) to reach all the nodes in the network. In this thesis, we develop an analytical model to determine expected number of for- warding nodes required to complete a broadcast in self-pruning algorithm. The derived expression is a function of various network parameters (such as, network density and distance between nodes) and radio transceiver parameters (such as transmission range). Moreover, the developed mathematical expression provides us a better understanding of the highly complex packet forwarding pattern of self-pruning algorithm and valuable insight to design a new broadcasting heuristic. The proposed new heuristic is a dynamic probabilistic broadcast where rebroadcast probability of each node is dynamically de- termined from a developed mathematical expression. Extensive simulation experiments have been conducted to validate the accuracy of the analytical model, as well as, to evaluate the e ciency of the proposed heuristic. Performance analysis shows that the proposed heuristic outperforms the static probabilistic broadcasting algorithm.