Data Prediction + Synchronous Transmissions = Ultra-low Power Wireless Sensor Networks

Data prediction in wireless sensor networks replaces the commonly used (periodic) data reporting with a model, updated (infrequently) at the sink to accurately reproduce real data trends. This technique abates up to 99% of application messages; yet, recent work has shown it achieves "only" up to a 7x lifetime improvement when executed atop a mainstream network stack (e.g., CTP + BoX-MAC), as the idle listening and topology maintenance in the latter are ill-suited to the sparse traffic induced by data prediction. This paper presents a novel network stack designed for data prediction, CRYSTAL, that exploits synchronous transmissions to quickly and reliably transmit model updates when these occur (infrequently but often concurrently), and minimizes overhead during the (frequent) periods with no updates. Based on 90-node experiments in the Indriya testbed and with 7 public datasets, we show that CRYSTAL unleashes the full potential of data prediction, achieving per-mille duty cycle with perfect reliability and very small latency.