The Use of Reliability-Based Splitting Algorithms to Improve Distributed Estimation in WSNs

We consider distributed estimation algorithms in a large Wireless Sensor Network (WSN) with a star topology. The local estimates computed by each sensor must be collected in a fixed amount of time by the network's Fusion Center (FC) and processed to produce a global estimate. When the amount of time available is not sufficient to collect a local estimate from every node, a strategy that enables the most reliable estimates to be collected first must be developed. In this paper, we develop such an algorithm for a WSN whose FC uses a Best Linear Unbiased Estimator (BLUE) and collects the local estimates on a collision channel. The proposed schemes use population splitting that is based on the reliabilities of the local estimates. The time available for data collection is divided into frames and each frame is subdivided into slots. The slots in the first frame are used to collect the most reliable local estimates, the next frame of slots is used to collect the next most reliable set of local estimates, etc. As the performance of the schemes depends on the reliability thresholds, the number of bits representing the estimates, and the number of time slots in a frame, we formulate time-constrained optimization problems and derive methods to obtain the optimal values of these parameters. An interesting result shows that the optimal reliability thresholds do not maximize the channel's throughput.