Model Based Adaptive Mobility Prediction in Mobile Ad-Hoc Networks

Mobility Prediction in mobile ad-hoc networks is used in location aided routing and mobility aware topology control protocols. These protocols assume that each node knows its current position, speed and movement direction angle. Using this information the protocols can predict the future position of each node. Also they can predict some parameters like future distance between 2 neighboring nodes. Future distance between 2 neighboring nodes is used in some applications like mobility aware topology control protocols. The major problem with these protocols is the inaccuracy of future distance predictor which uses mobility prediction to estimate the future distance of neighboring nodes. The efficiency of this estimator varies in presence of different mobility models, sampling rates and different speed ranges. In this paper, we introduce an adaptive mobility prediction method that uses learning automaton to estimate the coefficients of a simple adaptive filter in order to predict the future distance of 2 neighboring nodes. We evaluated this estimator in different mobility models and sampling rate. Simulation results show significant improvement in accuracy of the future distance prediction mechanism which causes more accurate prediction especially in low sampling rates.