Abstract
In recent years, urban population growth and the diversity of vehicles have increased. Location prediction in VANETs is extremely necessary for consumer applications such as routing, network management, knowledge dissemination protocols, and road cognition, among others. This could increase the performance of VANETs. In this paper a Kalman filter is used to predict the vehicle’s future location. We conducted experiments exploitation each vehicle quality traces and model-driven traces. We quantitatively compare the prediction performance of a Kalman filter and neural network-based methods. This paper proposes a location prediction algorithm for nonlinear vehicular movement using an Extended Kalman filter (EKF). Evaluation of the ESCL-VNET algorithm with EKF assess the given better results.