Loss Cumulant Generating Function Inference in Sensor Network

The internal link performance inference has become an increasingly important issue in operating and evaluating a network. Since it is usually impractical to directly monitor each node or link in the wireless sensor network, we consider the problem of inferring the internal link loss characteristics from passive end-to-end measurement in this paper. Specifically, the link loss performance inference during the data aggregation is considered. Under the assumptions that the link losses are mutually independent, we elaborate a bias corrected link loss cumulant generating function (CGF) algorithm. Through the simulation, we show that the internal link loss CGF can be inferred accurately, comparable to the sampled internal link loss CGF. At the end of this paper, we apply the result of internal link loss CGF inference to the lossy link identification in the sensor network