Enabling efficient semantic stream processing across the IoT network through adaptive distribution with DIVIDE
- Author
- Mathias De Brouwer (UGent) , Filip De Turck (UGent) and Femke Ongenae (UGent)
- Organization
- Project
- Abstract
- In the Internet of Things (IoT), semantic IoT platforms are often used to solve the challenges associated with the real-time integration of heterogeneous IoT sensor data, domain knowledge and context information. Existing platforms mostly have a static distribution and configuration of queries deployed on the platform's stream processing components. In contrast, the environmental context in which queries are deployed has a very dynamic nature: real-world set-ups involve varying tasks, device resource usage, networking conditions, etc. To solve this mismatch, this paper presents DIVIDE, an IoT platform component built on Semantic Web technologies. DIVIDE has a generic design containing multiple subcomponents that monitor the environment across a cascading architecture. By monitoring the use case context, DIVIDE adaptively derives the appropriate stream processing queries in a context-aware way. Using a Local Monitor deployed on edge devices, situational context parameters are measured and aggregated. The Meta Model allows modeling these measurements, and meta-information about devices and deployed stream processing queries. Through the definition of application-specific Global Monitor queries that are continuously evaluated centrally on the Meta Model, end users can dynamically configure how the situational context should influence the window parameter configuration and distribution of queries in the network. The paper evaluates a first implementation of DIVIDE on a homecare monitoring use case. The results show how DIVIDE can successfully adapt to varying device and networking conditions, taking into account the use case requirements. This way, DIVIDE allows better balancing use case specific trade-offs and achieves more efficient stream processing.
- Keywords
- ONTOLOGY, INTEROPERABILITY, PERFORMANCE, KNOWLEDGE, PLATFORM, INTERNET, THINGS, Network monitoring, Device monitoring, RDF stream processing, Internet of Things, Adaptive queries, Window parameters
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01HRETE7DZ7WYE304VQPRJR577
- MLA
- De Brouwer, Mathias, et al. “Enabling Efficient Semantic Stream Processing across the IoT Network through Adaptive Distribution with DIVIDE.” JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, vol. 32, no. 2, 2024, doi:10.1007/s10922-023-09797-2.
- APA
- De Brouwer, M., De Turck, F., & Ongenae, F. (2024). Enabling efficient semantic stream processing across the IoT network through adaptive distribution with DIVIDE. JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, 32(2). https://doi.org/10.1007/s10922-023-09797-2
- Chicago author-date
- De Brouwer, Mathias, Filip De Turck, and Femke Ongenae. 2024. “Enabling Efficient Semantic Stream Processing across the IoT Network through Adaptive Distribution with DIVIDE.” JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 32 (2). https://doi.org/10.1007/s10922-023-09797-2.
- Chicago author-date (all authors)
- De Brouwer, Mathias, Filip De Turck, and Femke Ongenae. 2024. “Enabling Efficient Semantic Stream Processing across the IoT Network through Adaptive Distribution with DIVIDE.” JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 32 (2). doi:10.1007/s10922-023-09797-2.
- Vancouver
- 1.De Brouwer M, De Turck F, Ongenae F. Enabling efficient semantic stream processing across the IoT network through adaptive distribution with DIVIDE. JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT. 2024;32(2).
- IEEE
- [1]M. De Brouwer, F. De Turck, and F. Ongenae, “Enabling efficient semantic stream processing across the IoT network through adaptive distribution with DIVIDE,” JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, vol. 32, no. 2, 2024.
@article{01HRETE7DZ7WYE304VQPRJR577,
abstract = {{In the Internet of Things (IoT), semantic IoT platforms are often used to solve the challenges associated with the real-time integration of heterogeneous IoT sensor data, domain knowledge and context information. Existing platforms mostly have a static distribution and configuration of queries deployed on the platform's stream processing components. In contrast, the environmental context in which queries are deployed has a very dynamic nature: real-world set-ups involve varying tasks, device resource usage, networking conditions, etc. To solve this mismatch, this paper presents DIVIDE, an IoT platform component built on Semantic Web technologies. DIVIDE has a generic design containing multiple subcomponents that monitor the environment across a cascading architecture. By monitoring the use case context, DIVIDE adaptively derives the appropriate stream processing queries in a context-aware way. Using a Local Monitor deployed on edge devices, situational context parameters are measured and aggregated. The Meta Model allows modeling these measurements, and meta-information about devices and deployed stream processing queries. Through the definition of application-specific Global Monitor queries that are continuously evaluated centrally on the Meta Model, end users can dynamically configure how the situational context should influence the window parameter configuration and distribution of queries in the network. The paper evaluates a first implementation of DIVIDE on a homecare monitoring use case. The results show how DIVIDE can successfully adapt to varying device and networking conditions, taking into account the use case requirements. This way, DIVIDE allows better balancing use case specific trade-offs and achieves more efficient stream processing.}},
articleno = {{27}},
author = {{De Brouwer, Mathias and De Turck, Filip and Ongenae, Femke}},
issn = {{1064-7570}},
journal = {{JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT}},
keywords = {{ONTOLOGY,INTEROPERABILITY,PERFORMANCE,KNOWLEDGE,PLATFORM,INTERNET,THINGS,Network monitoring,Device monitoring,RDF stream processing,Internet of Things,Adaptive queries,Window parameters}},
language = {{eng}},
number = {{2}},
pages = {{68}},
title = {{Enabling efficient semantic stream processing across the IoT network through adaptive distribution with DIVIDE}},
url = {{http://doi.org/10.1007/s10922-023-09797-2}},
volume = {{32}},
year = {{2024}},
}
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