Using End-to-End Data to Infer Lossy Links in Sensor Networks

Compared to wired networks, sensor networks pose two additional challenges for monitoring functions: they support much less probing traffic, and they change their routing topologies much more frequently. We propose therefore to use only end-to-end application traffic to infer performance of internal network links. End-to-end data do not provide sufficient information to calculate link loss rates exactly, but enough to identify poorly performing (lossy) links. We introduce inference techniques based on Maximum likelihood and Bayesian principles, which handle well noisy measurements and routing changes. We evaluate the performance of both inference algorithms in simulation and on real network traces. We find that these techniques achieve high detection and low false positive rates.