Chameleon: Adaptive and Scalable Stream Processing Over Sensor Sources

Internet of Things (IoT) applications make use of live data from numerous sensors that reside outside cloud datacenters. As a result, it is imperative for IoT data management systems to reduce their network footprint while simultaneously scaling to larger numbers of sensors. One way of achieving this is to adapt data generation to the rate of changes in the real world. In this systems paper, we propose Chameleon, a sensor-driven protocol for network-efficient data management that treats sensors as first-class components of a stream processing system. Chameleon combines local knowledge from the sensors with global knowledge from the cloud to improve data acquisition. Our empirical evaluation shows that systems employing Chameleon outperform baselines for aggregate queries by up to one order of magnitude in terms of network utilization while keeping query re-sults similar with negligible difference (down to 0.8%) from base-lines. Chameleon enables data management systems to handle up to 80% more sensors without needing extra network resources.