1. Cross-Layer Design of Distributed Sensing-Estimation With Quality
Feedback— Part I: Optimal Schemes
Abstract:
This two-part paper presents a feedback-based cross-layer framework for
distributed sensing and estimation of a dynamic process by
a wireless sensor network (WSN). Sensor nodes wirelessly communicate
measurements to the fusion center (FC). Cross-layer factors such as packet
collisions and the sensing-transmission costs are considered. Each SN
adapts its sensing-transmission action based on its own local observation
quality and the estimation quality feedback from the FC under cost
constraints for each SN. In this first part, the optimization complexity is
reduced by exploiting the statistical symmetry and
large network approximation of the WSN. Structural properties of the
optimal policy are derived for a coordinated and a decentralized scheme. It
is proved that a dense WSN provides sensing diversity, so that only a few
SNs with the best local observation quality need to be activated, despite the
fluctuations of the WSN. The optimal policy dictates that, when the
estimation quality is poor, only the best SNs activate, otherwise all SNs
remain idle to preserve energy. The costs of coordination and feedback are
evaluated, revealing the scalability of the decentralized scheme to large
WSNs, at the cost of performance degradation. Simulation results

2. demonstrate cost savings from 30% to 70% over a non-adaptive scheme,
and significant gains over a previously proposed estimator which does not
consider these cross-layer factors.