Failure-Aware Cascaded Suppression in Wireless Sensor Networks

Wireless sensor networks are widely used to continuously collect data from the environment. Because of energy constraints on battery-powered nodes, it is critical to minimize communication. Suppression has been proposed as a way to reduce communication by using predictive models to suppress reporting of predictable data. However, in the presence of communication failures, missing data are difficult to interpret because these could have been either suppressed or lost in transmission. There is no existing solution for handling failures for general, spatiotemporal suppression that uses cascading. While cascading further reduces communication, it makes failure handling difficult, because nodes can act on incomplete or incorrect information and in turn affect other nodes. We propose a cascaded suppression framework that exploits both temporal and spatial data correlation to reduce communication, and applies coding theory and Bayesian inference to recover missing data resulted from suppression and communication failures. Experiment results show that cascaded suppression significantly reduces communication cost and improves missing data recovery compared to existing approaches.