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Cross-Layer Design of Distributed Sensing-Estimation With Quality
Feedback— Part II: Myopic 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 second part, low-complexity myopic
sensing-transmission policies (MPs) are designed to optimize a trade-off
between performance and the cost incurred by each SN. The MP is
computed in closed form for a coordinated scheme, whereas an iterative
algorithm is presented for a decentralized one, which converges to a local
optimum. The MP dictates that, when the estimation quality is poor, only
the best SNs activate, otherwise all SNs remain idle to preserve energy. For
both schemes, the threshold on the estimation quality below which the SNs
remain idle is derived in closed form, and is shown to be independent of
the number of channels. It is also proved that a single channel suffices for
severely energy constrained WSNs. The proposed MPs are shown to yield
near-optimal performance with respect to the optimal policy of Part I, also
in this issue, at a fraction of the complexity, thus being more suitable for
practical WSN deployments.

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Cross layer design of distributed sensing-estimation with quality feedback— part ii myopic schemes

  • 1. Cross-Layer Design of Distributed Sensing-Estimation With Quality Feedback— Part II: Myopic 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 second part, low-complexity myopic sensing-transmission policies (MPs) are designed to optimize a trade-off between performance and the cost incurred by each SN. The MP is computed in closed form for a coordinated scheme, whereas an iterative algorithm is presented for a decentralized one, which converges to a local optimum. The MP dictates that, when the estimation quality is poor, only the best SNs activate, otherwise all SNs remain idle to preserve energy. For both schemes, the threshold on the estimation quality below which the SNs remain idle is derived in closed form, and is shown to be independent of the number of channels. It is also proved that a single channel suffices for severely energy constrained WSNs. The proposed MPs are shown to yield
  • 2. near-optimal performance with respect to the optimal policy of Part I, also in this issue, at a fraction of the complexity, thus being more suitable for practical WSN deployments.