Standard

Optimized on-demand data streaming from sensor nodes. / Traub, Jonas; Breß, Sebastian; Rabl, Tilmann; Katsifodimos, Asterios; Markl, Volker.

SoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing. New York, NY : Association for Computing Machinery (ACM), 2017. p. 586-597.

Research output: Scientific - peer-reviewConference contribution

Harvard

Traub, J, Breß, S, Rabl, T, Katsifodimos, A & Markl, V 2017, Optimized on-demand data streaming from sensor nodes. in SoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing. Association for Computing Machinery (ACM), New York, NY, pp. 586-597, 2017 Symposium on Cloud Computing, SoCC 2017, Santa Clara, United States, 24/09/17. DOI: 10.1145/3127479.3131621

APA

Traub, J., Breß, S., Rabl, T., Katsifodimos, A., & Markl, V. (2017). Optimized on-demand data streaming from sensor nodes. In SoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing (pp. 586-597). New York, NY: Association for Computing Machinery (ACM). DOI: 10.1145/3127479.3131621

Vancouver

Traub J, Breß S, Rabl T, Katsifodimos A, Markl V. Optimized on-demand data streaming from sensor nodes. In SoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing. New York, NY: Association for Computing Machinery (ACM). 2017. p. 586-597. Available from, DOI: 10.1145/3127479.3131621

Author

Traub, Jonas ; Breß, Sebastian ; Rabl, Tilmann ; Katsifodimos, Asterios ; Markl, Volker. / Optimized on-demand data streaming from sensor nodes. SoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing. New York, NY : Association for Computing Machinery (ACM), 2017. pp. 586-597

BibTeX

@inbook{dd598af074c547f899da8bec3710a827,
title = "Optimized on-demand data streaming 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 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.",
keywords = "Adaptive sampling, On-demand streaming, Oversampling, Real-time analysis, Sensor data, Sensor sharing, User-defined sampling",
author = "Jonas Traub and Sebastian Breß and Tilmann Rabl and Asterios Katsifodimos and Volker Markl",
year = "2017",
month = "9",
doi = "10.1145/3127479.3131621",
pages = "586--597",
booktitle = "SoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",

}

RIS

TY - CHAP

T1 - Optimized on-demand data streaming from sensor nodes

AU - Traub,Jonas

AU - Breß,Sebastian

AU - Rabl,Tilmann

AU - Katsifodimos,Asterios

AU - Markl,Volker

PY - 2017/9/24

Y1 - 2017/9/24

N2 - 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.

AB - 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.

KW - Adaptive sampling

KW - On-demand streaming

KW - Oversampling

KW - Real-time analysis

KW - Sensor data

KW - Sensor sharing

KW - User-defined sampling

UR - http://www.scopus.com/inward/record.url?scp=85032449790&partnerID=8YFLogxK

U2 - 10.1145/3127479.3131621

DO - 10.1145/3127479.3131621

M3 - Conference contribution

SP - 586

EP - 597

BT - SoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing

PB - Association for Computing Machinery (ACM)

ER -

ID: 36129046