The value of clustering in distributed estimation for sensor networks

Energy efficiency, low latency, high estimation accuracy, and fast convergence are important goals in distributed estimation algorithms for sensor networks. One approach that adds flexibility in achieving these goals is clustering. In this paper, we extend the framework of distributed estimation by allowing clustering amongst the nodes. The general class of distributed incluster algorithms considered includes the distributed in-network algorithm, recently proposed by Rabbat and Nowak [1], as a special case. The distributed parameter estimation problem is posed as a convex optimization problem involving a social cost function and data from the sensor nodes. An in-cluster algorithm is then derived using the incremental subgradient method. Sensors in each cluster successively update a cluster parameter estimate based on local data, which is then passed on to a fusion center for further processing. We also prove convergence results for the distributed in-cluster algorithm, and provide simulations for least squares and robust estimation.