A Novel Method for Nonstationary Network Internal Loss Tomography

Most of network link parameters inference methods assume that link states are stationary during measurement period, and can not obtain time-varying characteristics of link parameters. In this paper, a novel nonstationary internal loss tomography method is proposed. Assume in a relatively short time window, the time-varying curves of link loss rates are described by k times continuous differentiable functions. The k-th order Taylor Serieses of these functions are estimated using network tomography approach. Then based on the estimates of each time window, the time-varying link loss rates of entire measurement period are obtained by integrating the estimates of all time windows using Inverse Distance Square Weighted algorithm. NS2 simulations show that the method is capable of tracking variation of link loss rates effectively, and superior existing stationary internal loss tomography methods.