An Framework of Efficient Hybrid Model and Optimal Control for Multihop Wireless Networks

The performance of multihop wireless networks (MWN) is normally studied via simulation over a fixed time horizon using a steady-state type of statistical analysis procedure. However, due to the dynamic nature of network connectivity and nonstationary traffic, such an approach may be inappropriate as the network may spend most time in a transient/nonstationary state. Moreover, the majority of the simulators suffer from scalability issues. In this work, we presents a performance modeling framework for analyzing the time varying behavior of MWN. Our framework is a hybrid model of time varying connectivity matrix and nonstationary network queues. Network connectivity is captured using stochastic modeling of adjacency matrix by considering both wireless link quality and node mobility. Nonstationary network queues behavior are modeled using fluid flow based differential equations. In terms of the computational time, the hybrid fluid-based model is a more scalable tool than the standard simulator. Furthermore, an optimal control strategy is proposed on the basis of the hybrid model.