Publication

Optimized On-Demand Data Streaming from Sensor Nodes

Jonas Traub, Sebastian Breß, Tilmann Rabl, Asterios Katsifodimos, Volker Markl

In: ACM Symposium on Cloud Computing 2017. ACM Symposium on Cloud Computing (SoCC-17) September 25-27 Santa Clara CA United States ACM 2017.

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 form a sensor cloud and offer data streams to analysis systems. However, it is impossible to transfer all available data with maximal frequencies to all applications. 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 define the data-demand of queries and 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.

Projekte

on-demand-data-streaming.pdf (pdf, 1 MB)

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz