Resource-aware event triggered distributed estimation over adaptive networks

Abstract

We propose a novel algorithm for distributed processing applications constrained by the available communication resources using diffusion strategies that achieves up to a 103 fold reduction in the communication load over the network, while delivering a comparable performance with respect to the state of the art. After computation of local estimates, the information is diffused among the processing elements (or nodes) non-uniformly in time by conditioning the information transfer on level-crossings of the diffused parameter, resulting in a greatly reduced communication requirement. We provide the mean and mean-square stability analyses of our algorithms, and illustrate the gain in communication efficiency compared to other reduced-communication distributed estimation schemes.