This document summarizes a two-part paper that presents a feedback-based cross-layer framework for distributed sensing and estimation of a dynamic process by a wireless sensor network. The framework considers cross-layer factors such as packet collisions and sensing-transmission costs. Each sensor node adapts its sensing-transmission action based on local observation quality and estimation quality feedback from the fusion center under cost constraints. The first part reduces optimization complexity by exploiting statistical symmetry and large network approximations. Structural properties of optimal coordinated and decentralized schemes are derived, proving that only the best sensing nodes need to activate despite fluctuations. The optimal policy dictates that the best nodes activate when estimation quality is poor, otherwise all nodes remain idle to preserve energy.