Cross-layer design of distributed sensing-estimation with quality feedback, Part I: Optimal schemes

TL;DR

This paper develops an optimal cross-layer framework for distributed sensing and estimation in wireless sensor networks, emphasizing adaptive sensor activation based on local and feedback information to improve efficiency and scalability.

Contribution

It introduces a novel optimization approach exploiting network symmetry and large-scale approximations, deriving structural properties of optimal policies for both coordinated and decentralized schemes.

Findings

Optimal policies activate only the best sensors when estimation quality is poor.

Decentralized schemes scale well with large networks, with some performance trade-offs.

Simulation shows 30-70% cost savings over non-adaptive methods.

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 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.