Predictive Mobile IP Handover for Vehicular Networks

Abstract

Vehicular networks are an emerging technology that offer potential for providing a variety of new services. However, extending vehicular networks to include IP connections is still problematic, due in part to the incompatibility of mobile IP handovers with the increased mobility of vehicles. The handover process, consisting of discovery, registration, and packet forwarding, has a large overhead and disrupts connectivity. With increased handover frequency and smaller access point dwell times in vehicular networks, the handover causes a large degradation in performance. This thesis proposes a predictive handover solution, using a combination of a Kalman filter and an online hidden Markov model, to minimize the effects of prediction errors and to capitalize on advanced handover registration. Extensive simulated experiments were carried out in NS-2 to study the performance of the proposed solution within a variety of traffic and network topology scenarios. Results show a significant improvement to both prediction accuracy and network performance when compared to recent proposed approaches.