Bayesian model for mobility prediction to support routing in Mobile Ad-Hoc Networks

This paper introduces a Bayesian model to predict and classify the mobility of a node in Mobile Ad-hoc Networks (MANETs). The proposed model does not use the additional information from Global Positioning System (GPS) for its prediction as some existing models did. Instead, it relies on the “average encounter rate” and “node degree” calculated at each node. However, the outcome is still recorded at high accuracy, i.e. prediction error is fewer than 10% at high speed level (above 15m/s). The aim of this model is to help a routing protocol in MANETs avoid broadcasting request messages from a high mobility node/region relied on the outcome of the prediction. Through simulation experiments, route error rate observed reduced significantly compared to normal broadcast scheme of the Ad-hoc On-demand Distance Vector (AODV) protocol. The packet delivery ratio improved up to 46.32% at the maximum velocity of 30m/s (equal to 108km/h) in the density of 200nodes/km2.