Grammatical Inference for Modeling Mobility Patterns in Networks

Modeling of the mobility patterns arising in computer networks requires a compact and faithful representation of the mobility data collected from observations and measurements of the relevant network applications. This data can range from the information on the mobility of the agents that are being monitored by a wireless network to mobility information of nodes in mobile network applications. In this paper, we examine the use of probabilistic context-free grammars as the modeling framework for such data. We present a fast algorithm for deriving a concise probabilistic context-free grammar from the given training data. The algorithm uses an evaluation metric based on Bayesian formula for maximizing grammar a posteriori probability given the training data. We describe the application of this algorithm in two mobility modeling domains: 1) recognizing mobility patterns of monitored agents in different event data sets collected by sensor networks, and 2) modeling and generating node movements in mobile networks. We also discuss the model's performance in simulations utilizing both synthetic and real-world mobility traces.