TaM: A Terrain-Aware Markovian Mobility Model for VANets

Mobility models have significantly evolved in recent years to capture vehicular and pedestrian trajectories. However, modeling the mobility of manual and low-powered vehicles, such as e-bikes, is highly dependent on the terrains they traverse. Variations in terrain can significantly impact their acceleration and displacement along their trajectories. Accurately estimating the location of these vehicles is critical to ensuring that the underlying communication infrastructure adequately covers them. Maintaining reliable connectivity and coverage is pivotal in enabling communication during emergencies, and supporting vital tracking services. To achieve this, it is crucial to develop an accurate trajectory model that considers terrain variations, particularly how elevation changes affect a vehicle's displacement. This paper presents a terrain-aware mobility model, named Terrain-Aware Mobility (TaM), designed to enhance the accuracy of trajectory predictions based on the Gauss-Markov model. The goal of TaM is to effectively model mobility across varying terrain slopes, thereby enabling the optimal placement of wireless access points (WAPs) in under-served areas. TaM is also designed to factor in potential errors in displacement, utilizing a measure of error covariance to mitigate compounding errors. The results of adopting different mobility models are contrasted in this paper, and further insights on building probabilistic mobility models are presented.