  We present DCOOL-NET, a scalable distributed in-network algorithm for sensor
network localization based on noisy range measurements. DCOOL-NET operates by
parallel, collaborative message passing between single-hop neighbor sensors,
and involves simple computations at each node. It stems from an application of
the majorization-minimization (MM) framework to the nonconvex optimization
problem at hand, and capitalizes on a novel convex majorizer. The proposed
majorizer is endowed with several desirable properties and represents a key
contribution of this work. It is a more accurate match to the underlying
nonconvex cost function than popular MM quadratic majorizers, and is readily
amenable to distributed minimization via the alternating direction method of
multipliers (ADMM). Moreover, it allows for low-complexity, fast Nesterov
gradient methods to tackle the ADMM subproblems induced at each node. Computer
simulations show that DCOOL-NET achieves comparable or better sensor position
accuracies than a state-of-art method which, furthermore, is not parallel.
