DOI

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.

Original languageEnglish
Title of host publicationSoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing
PublisherAssociation for Computing Machinery (ACM)
Pages586-597
Number of pages12
ISBN (Electronic)9781450350280
DOIs
StatePublished - 24 Sep 2017
Event2017 Symposium on Cloud Computing, SoCC 2017 - Santa Clara, United States

Conference

Conference2017 Symposium on Cloud Computing, SoCC 2017
CountryUnited States
CitySanta Clara
Period24/09/1727/09/17

    Research areas

  • Adaptive sampling, On-demand streaming, Oversampling, Real-time analysis, Sensor data, Sensor sharing, User-defined sampling

ID: 36129046