Time- and coefficient-selective diffusion strategies for distributed parameter estimation

We present an innovative approach to distributed estimation which features time- and coefficient-selective updates of parameter estimates, thereby offering a significant reduction in energy consumption in the sensor nodes. The proposed approach is based on the principles of set-membership adaptive filtering (SMAF), which allows for selective updates of parameter estimates. It also employs the principle of partial updates which updates only a subset of coefficients when an update of parameter estimates is called for.