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A QoS-aware Adaptive Mobility Handling Approach for LoRa-based IoT Systems
Katholieke Univ Leuven, Belgium.
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). Katholieke Univ Leuven, Belgium.ORCID iD: 0000-0002-1162-0817
2018 (English)In: 2018 12TH IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2018), IEEE, 2018, p. 130-139Conference paper, Published paper (Refereed)
Abstract [en]

Internet-of-Things (IoT) is an emergent paradigm that is increasingly applied in smart cities. A popular technology used in IoT is LoRa that supports long-range wireless communication. In this research, we study LoRa-based IoT systems with battery-powered end nodes that collect and communicate data to a gateway for further processing. Existing approaches in such IoT systems usually only consider stationary end nodes. We focus on systems with mobile end nodes, paving the way to new applications such as target tracking. Key Quality of Service (QoS) requirements for these settings are the reliability of the communication and energy consumption. With mobile end nodes, ensuring these QoS is challenging as the system is subject to continuous changes. In this paper, we investigate how the settings of a mobile end node impact key performance indicators for reliability and energy consumption. Based on insights obtained from extensive field experiments, we devise an algorithm that automatically adapts the settings of a mobile end node to ensure its QoS requirements for a setup with a single gateway. We then extend the algorithm to a setup with multiple gateways. We demonstrate how the algorithms achieve the QoS requirements of a mobile end node in a concrete IoT deployment.

Place, publisher, year, edition, pages
IEEE, 2018. p. 130-139
Series
International Conference on Self-Adaptive and Self-Organizing Systems, ISSN 1949-3673
National Category
Computer and Information Sciences Signal Processing
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-81105DOI: 10.1109/SASO.2018.00024ISI: 000459885200014Scopus ID: 2-s2.0-85061896468ISBN: 978-1-5386-5172-8 (print)OAI: oai:DiVA.org:lnu-81105DiVA, id: diva2:1296489
Conference
12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), SEP 03-07, 2018, Trento, ITALY
Available from: 2019-03-15 Created: 2019-03-15 Last updated: 2019-08-29Bibliographically approved

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Weyns, Danny

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf