The article by Jonas Traub, Sebastian Breß, Tilmann Rabl, Asterios Katsifodimos, and Volker Markl connects data gathering with data analysis applications in order to enable demand-driven data acquisition from sensor nodes.
Abstract: Real-time sensor data enables diverse applications such as smart metering, traffic monitoring, and sport analysis. In the Internet of Things, billions of sensor nodes stream data to analysis systems. We thus cannot transfer all available data with maximal frequencies any more. Therefore, we need to tailor data streams to the demand of applications. We contribute a technique that optimizes communication costs while maintaining the desired accuracy. Our technique schedules reads across huge amounts of sensors based on the data-demands of a huge amount of concurrent queries. We introduce user-defined sampling functions that facilitate various adaptive sampling techniques, which decrease the amount of transferred data. Moreover, we share sensor reads and data transfers among queries. Our experiments with real-world data show that our approach saves up to 87% in data transmissions.
Download Paper: Optimized On-Demand Data Streaming from Sensor Nodes
Citation: Jonas Traub, Sebastian Breß, Tilmann Rabl, Asterios Katsifodimos, and Volker Markl. 2017. Optimized On-Demand Data Streaming from Sensor Nodes. In Proceedings of SoCC ’17, Santa Clara, CA, USA, September 24–27, 2017, 12 pages.