Mobility information estimation algorithm using Kalman-filter for vehicular ad hoc networks

Abstract

In vehicular networks, the exchange of mobility information is considered as the basis for many applications. The quality of this information affects applications and networks performance. False mobility information leads applications, network services and security services to make wrong decisions. Accordingly, many verification approaches were proposed as a security countermeasure. However, most of these approaches assume accurate mobility information regardless of localisation errors occurrence. Therefore, estimating the mobility information is an important security procedure to enhance the verification performance. In this paper, mobility information estimation algorithm (MIEA) was proposed based on Kalman filter to estimate the actual mobility information from noisy measurements. To evaluate the algorithm effectiveness, the study used next generation simulation dataset (NGSIM). White noises were injected into vehicle trajectories to simulate localisation errors. Results show that the root mean square error (RMSE) is reduced which implies the effectiveness of the proposed algorithm.