Cross-layer design of distributed sensing-estimation with quality feedback, Part II: Myopic schemes

This two-part paper presents a feedback-based crosslayer framework for distributed sensing and estimation of a dynamical process in a wireless sensor network (WSN) in which the sensor nodes (SNs) communicate their measurements to a fusion center (FC) via B orthogonal wireless channels. Cross-layer factors such as packet collisions and the sensingtransmission costs are accounted for. Each SN adapts its sensingtransmission action based on its own local observation quality and the estimation quality feedback from the FC. In this second part, low-complexity myopic sensing-transmission policies (MPs) are designed to optimize a trade-off between the mean squared estimation error (MSE) at the FC and the cost incurred by each SN. For the coordinated scheme, where the FC schedules the sensing-transmission action of each SN, the MP is computed in closed form; for the decentralized one, where each SN performs a local decision based on its local observation quality and the FC quality feedback, an iterative algorithm is presented, which converges to a local optimum of the MP cost function. 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 B. It is also proved that B=1 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 [13], at a fraction of the complexity, thus being more suitable for practical WSN deployments, and achieve cost savings from 30% to 70% over non-adaptive schemes.