2012/11/30 by Cláudia Soares, Soares, Claudia, João Xavier +3
Engineering · #Distributed #FOS: Computer and information sciences #FOS: Mathematics #Indoor and Outdoor Localization Technologies #Optimization and Control (math.OC) #Parallel #Robotics and Sensor-Based Localization #Sparse and Compressive Sensing Techniques #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1211.7277
openalex publication_date 2012/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.