GAMMA: A framework for moving object simulation

Simulating user mobility is crucial for mobile computing and spatial database research. However, all existing moving object generators assume a fixed and often unrealistic mobility model. In this paper, we represent the moving behavior as a trajectory in the location-temporal space and propose two generic metrics to evaluate a trajectory dataset. In this context, trajectory generation is treated as an optimization problem and a framework, GAMMA, is proposed to solve it by the genetic algorithm. We demonstrate GAMMA's practicability and flexibility by configuring it for two specific simulations, namely, cellular network trajectory and symbolic location tracking. The experimental results show that GAMMA can efficiently and robustly produce high quality moving object datasets for various simulation objectives.