Towards network invariant fault diagnosis in MANETs via statistical modeling: The global strength of local weak decisions

Abstract

Due to its obvious importance, fault detection and localization is a well-studied problem in communication networks, as attested by the many techniques designed to address this problem. The inherent variability, limited component reliability, and constrained resources of MANETs (Mobile Ad hoc Networks) make the problem not just more important, but also critical. Practical development and deployment considerations imply that fault detection and localization methods must i) avoid relying on overly detailed models of network protocols and traffic assumptions and instead rely on actual cross-layer measurements/observations, and ii) be applicable across different network scales and topologies with minimum adjustments. This paper demonstrates the feasibility of such goals, and proposes an important and as yet unexplored approach to fault management in MANETs: network-invariant fault detection, localization and diagnosis with limited knowledge of the underlying network and traffic models. We show how fault management methods can be derived by observing statistical network/traffic measurements in one network, and subsequently applied to other networks with satisfactory performance. We demonstrate that a carefully designed but widely applicable set of local and weak global indicators of faults can be efficiently aggregated to produce highly sensitive and specific methods that perform well when applied to MANETs with varying sizes, topologies, and traffic matrices.