DCOOL-NET: Distributed cooperative localization for sensor networks

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-ofart method which, furthermore, is not parallel.