A classification approach to broadcasting in a mobile ad hoc network

We present a new broadcast protocol using Bayesian probabilistic classifiers, and we demonstrate its use in a mobile ad hoe network (MANET). Broadcasting functions as a foundation of MANET communication. Unicast, multicast, and geocast protocols utilize broadcasting as a building block, providing important control and route establishment functionality. Therefore, any improvements to the process of broadcasting can be immediately realized by MANET applications. While efficient broadcast protocols have been proposed, no single broadcasting protocol works well in all possible MANET conditions. Furthermore, every protocol fails catastrophically in severe network environments. We exhibit a new approach to the design of a broadcast protocol: the decision of whether to rebroadcast a packet is cast as a classification problem. Each mobile node (MN) builds a classifier and trains it on data collected from the network environment. Given an input vector describing a broadcast packet and current network conditions, the classifier returns an output label of "Rebroadcast" or "Drop:" Because each MN adapts to changing network conditions, the result is a more robust communication protocol and more efficient use of network resources. We show that our protocol, compared to those tested, is the most efficient under a range of network conditions.