Joint Collaboration and Compression Design for Distributed Sequential Estimation in a Wireless Sensor Network

In this work, we propose a joint collaboration-compression framework for sequential estimation of random vector parameter in resource constrained wireless sensor network (WSN). Specifically, where the local sensors first collaborate (via collaboration matrix) with each other. Then subset selected to communicate FC linearly compress their observations before transmission. We design near-optimal and linear compression strategies under power constraints via alternating minimization minimum mean square error. The objective function is generally non-convex. establish correspondence between sparse matrix non-sparse consisting nonzero elements matrix. Then, reformulate solve problem using quadratically quadratic program (QCQP). solved same methodology. two versions design, one centralized scheme are derived at decentralized, compute individual independently. Importantly, show that proposed methods can also be used estimating time-varying parameters. Finally, numerical results provided demonstrate effectiveness framework.