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DeltaIoT: A Self-Adaptive Internet of Things Exemplar
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0002-1343-5834
KU Leuven, Belgium.
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). KU Leuven, Belgium.
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). KU Leuven, Belgium.ORCID iD: 0000-0002-1162-0817
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2017 (English)In: Proceedings - 2017 IEEE/ACM 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2017, IEEE, 2017, p. 76-82Conference paper, Published paper (Refereed)
Abstract [en]

Internet of Things (IoT) consists of networked tiny embedded computers (motes) that are capable of monitoring and controlling the physical world. Examples range from building security monitoring to smart factories. A central problem of IoT is minimising the energy consumption of the motes, while guaranteeing high packet delivery performance, regardless of uncertainties such as sudden changes in traffic load and communication interference. Traditionally, to deal with uncertainties the network settings are either hand-tuned or over-provisioned, resulting in continuous network maintenance or inefficiencies. Enhancing the IoT network with self-adaptation can automate these tasks. This paper presents DeltaIoT, an exemplar that enables researchers to evaluate and compare new methods, techniques and tools for self-adaptation in IoT. DeltaIoT is the first exemplar for research on self-adaptation that provides both a simulator for offline experimentation and a physical setup that can be accessed remotely for real-world experimentation. © 2017 IEEE.

Place, publisher, year, edition, pages
IEEE, 2017. p. 76-82
Keywords [en]
Exemplar, Internet of Things, Self-adaptation, Energy utilization, Software engineering, Communication interferences, Internet of Things (IOT), Monitoring and controlling, Network maintenances, Packet delivery performance, Self adaptation, Techniques and tools
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
URN: urn:nbn:se:lnu:diva-84242DOI: 10.1109/SEAMS.2017.21Scopus ID: 2-s2.0-85025610351ISBN: 9781538615508 (print)OAI: oai:DiVA.org:lnu-84242DiVA, id: diva2:1323131
Conference
12th IEEE/ACM International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2017, 22 - 23 May 2017, Buenos Aires, Argentina
Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-06-13Bibliographically approved

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Iftikhar, Muhammad UsmanWeyns, Danny

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  • apa
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