In realistic mobile ad-hoc network scenarios, the hosts usually travel to the pre-specified
destinations, and often exhibit non-random motion behaviors. In such mobility patterns, the
future motion behavior of the mobile is correlated with its past and current mobility
characteristics. Therefore, the memoryless mobility models are not capable of realistically
emulating such a mobility behavior. In this paper, an adaptive learning automata-based
mobility prediction method is proposed in which the prediction is made based on the Gauss …