  In practice, network applications have to deal with failing nodes, malicious
attacks, or, somehow, nodes facing highly corrupted data --- generally
classified as outliers. This calls for robust, uncomplicated, and efficient
methods. We propose a dissimilarity model for network localization which is
robust to high-power noise, but also discriminative in the presence of regular
gaussian noise. We capitalize on the known properties of the M-estimator Huber
penalty function to obtain a robust, but nonconvex, problem, and devise a
convex underestimator, tight in the function terms, that can be minimized in
polynomial time. Simulations show the performance advantage of using this
dissimilarity model in the presence of outliers and under regular gaussian
noise.
