RANBAR is a novel outlier elimination technique designed for sensor networks. It is based on the RANSAC (RANdom SAmple Consensus)
paradigm, which is well-known in computer vision and in automated cartography. The RANSAC paradigm gives us a hint on how to
instantiate a model if there are a lot of compromised data elements. RANBAR is developed following this
paradigm and it is capable to handle a high percent of outlier measurement data by leaning on only one preassumption, namely that
the sample is i.i.d. in the unattacked case.