Local linear suppression for wireless sensor network data

Abstract

With wireless sensor networks, preserving battery life is critical. For such sensors, data collection is relatively cheap while data transmission is relatively expensive. For such networks in ecological settings, certain processes are sufficiently predictable so that transmission of data at a particular time can be suppressed if it does not differ from what is expected at that time. That is, there will not be much loss of information with regard to inference. More precisely, there is a presumed model to explain the measurements collected at the sensors, which provides insight into what is expected at a given node, at a given time. Under the suppression, inference objectives include both estimation of the process parameters as well as reconstruction of the entire time series at each of the nodes.

In this paper, we build on the existing literature that has offered ways in which one can use suppression in wireless sensor networks to limit the number of transmissions. We introduce a new, computationally cheap, locally linear suppression scheme based upon process knowledge and compare it to the commonly used “constant” suppression scheme. Maintaining the same suppression threshold, we demonstrate decreased transmission rates under the new scheme while producing comparable posterior inference relative to constant suppression scheme. That is, the untransmitted readings are bounded to within an interval of the same length under both schemes, but the linear suppression scheme will transmit less data.

We implement this scheme for a synthetic dataset produced under the assumption of a diffusion model and show that even under high suppression rates, we are able to recover simulation parameters. We also implement linear suppression on data collected from a real wireless sensor network that measures the amount of light filtering through the forest canopy at a set of locations in the Duke Forest. We show that the in-sample predictive sum of squared errors from the suppressed data is only a bit larger than that from the full dataset.