End-to-end packet-channel Bayesian model applied to heterogeneous wireless networks

This paper proposes a source-traffic based model to estimate jointly packet losses and delays statistical behavior of a network path. The approach relies on a hidden Markov model built on real-traffic information. The effectiveness of the model is evaluated over different real heterogeneous network scenarios. Our experimental results show that the model captures average (long-term) and conditional (short-term) statistics that in most cases are typical of the single scenario. Preliminary results about prediction on a sample path as well as investigation on the use of the same model across different scenarios are given.