An Event-based Diffusion LMS Strategy

We consider a wireless sensor network consists of cooperative nodes, each of them keep adapting to streaming data to perform a least-mean-squares estimation, and also maintain information exchange among neighboring nodes in order to improve performance. For the sake of reducing communication overhead, prolonging batter life while preserving the benefits of diffusion cooperation, we propose an energy-efficient diffusion strategy that adopts an event-based communication mechanism, which allow nodes to cooperate with neighbors only when necessary. We also study the performance of the proposed algorithm, and show that its network mean error and MSD are bounded in steady state. Numerical results demonstrate that the proposed method can effectively reduce the network energy consumption without sacrificing steady-state network MSD performance significantly

Doubly compressed diffusion LMS over adaptive networks

International audienceDiffusion LMS is an efficient strategy for solving distributed optimization problems with cooperating agents. Nodes are interested in estimating the same parameter vector and exchange information with their neighbors to improve their local estimates. Successful implementation of such applications relies on a substantial amount of communication resources. In this paper, we introduce diffusion LMS strategies that offer significantly reduced communication load without compromising performance. We perform analyses in the mean and mean-square sense of these algorithms. Simulations results are provided to confirm the theoretical findings