Estimation on graphs from relative measurements

Large-scale sensor networks give rise to estimation problems that have a rich graphical structure. We studied one of these problems in terms of how such an estimate can be efficiently computed in a distributed manner as well as how the quality of an optimal estimate scales with the size of the network. Two distributed algorithms are presented to compute the optimal estimates that are scalable and robust to communication failures. In designing these algorithms, we found the literature on parallel computation to be a rich source of inspiration.