Information-Geometric Wireless Network Inference

Abstract

We consider network tomography and monitoring in dynamic wireless systems and leverage analytical tools from optimization theory and information geometry to infer the invariant statistical network structures. We extend the classical network tomography problem beyond average link rate measurements and develop a systematic optimization mechanism to infer the end-to-end wireless network behavior. This involves estimating the distributions of global network flow rates from the arbitrary statistics collected for the wireless link (channel) rates subject to the topology and link capacity constraints. We develop first a centralized network inference framework based on minimizing the distance of network flow rates from the prior information in the probability space that is spanned by the measurement constraints. Then, distributed implementation follows from message passing among the individual probes in the network and balances the complexity and convergence trade-offs. This formulation facilitates multi-scale multi-resolution inference of flow rates along with link capacity estimation. The underlying optimization framework for information-geometric network inference adapts to wireless network dynamics and offers robust operation with respect to the measurement errors and conflicts as well as the temporal and spatial variations in wireless networks.