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Improved Kalman Filter Based LAR in Vehicular Ad Hoc Network

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Vehicular Ad hoc Network (VANET) is a subclass of ad hoc networks and a special type of mobile ad hoc network (MANET). Due to the highly dynamic and ever-changing topology and multi-path challenges of the Global Positioning System (GPS), accurate prediction is often difficult, especially when ignoring the movement of nodes in VANETs. This research proposes a Kalman filter based Location Aided Routing (KALAR) to improve prediction accuracy in VANET. The aim of the article is to improve the accuracy of Kalman by adding an awareness component to the nature of the vehicle maneuver prediction in VANET environments.  A location prediction model for VANET, incorporating the constraint of vehicle movement was developed. Simulation results showed that the KALAR improved prediction accuracy. Packet Delivery Ratio (PDR) was at 95% with a smaller network overhead. In addition to this, KALAR was compared to other experiments: Location-aided routing (LAR) with and without a model driven tracer. Results showed that the KALAR significantly outperformed the other experiments.
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Kalman Filter; Location Aided Routing; Predication Accuracy; Vehicular Ad Hoc Network

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