Cross layer design of distributed sensing-estimation with quality feedback— part ii myopic schemes
This paper presents low-complexity "myopic sensing-transmission policies" (MPs) for distributed sensing and estimation in a wireless sensor network. The MPs optimize a trade-off between performance and cost for each sensor node. For a coordinated MP scheme, the policy is computed in closed form, while an iterative algorithm is used for a decentralized scheme to reach a local optimum. The MPs dictate that only the best sensor nodes activate when estimation quality is poor, otherwise all nodes remain idle to preserve energy. Thresholds for activation are derived in closed form and do not depend on the number of channels used. A single channel is sufficient for energy-constrained networks. The MPs provide near-optimal performance to the optimal policy with much lower
More Related Content
What's hot (14)
Similar to Cross layer design of distributed sensing-estimation with quality feedback— part ii myopic schemes (20)
More from ieeeprojectsbangalore (20)
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.