  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, at a
fraction of the complexity, thus being more suitable for practical WSN
deployments.
