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Throughput Maximization in Low-Power IoT Networks via Tuning the Size of the TSCH Slotframe
The University of Sydney, Sydney, Australia.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0001-9194-010X
The University of Sydney, Sydney, Australia.
2021 (English)In: SenSys 2021 - Proceedings of the 2021 19th ACM Conference on Embedded Networked Sensor Systems, Association for Computing Machinery (ACM), 2021, p. 401-402Conference paper, Published paper (Refereed)
Abstract [en]

Time-Slotted Channel Hopping (TSCH) was standardized as a part of IEEE 802.15.4e to address the strict reliability and timeliness requirements of low-power Internet of Things (IoT) applications. Setting the size of the TSCH slotframe has a considerable effect on the performance of scheduling algorithms used in IoT networks. Although IETF and IEEE standards define general mechanisms for communication of TSCH nodes, finding the optimal size of the TSCH slotframe has been left open and unresolved. In this poster, we propose an algorithm called S-TSCH to find the optimal size of the TSCH slotframe for maximizing network throughput based on 1) the number of nodes placed in the topology, 2) the data generation rate of applications running on IoT nodes, 3) and the maximum rate of generating TSCH/RPL control packets. To evaluate the performance of our contribution, we implement S-TSCH on Zolerita Firefly IoT motes and the Contiki-NG operating system. Evaluation results show that our proposed method improves the performance of distributed TSCH scheduling algorithms in terms of reliability and delay.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2021. p. 401-402
Keywords [en]
Internet of Things (IoT), Time-Slotted Channel Hopping (TSCH)
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-88924DOI: 10.1145/3485730.3492894Scopus ID: 2-s2.0-85120918788ISBN: 9781450390972 (print)OAI: oai:DiVA.org:kau-88924DiVA, id: diva2:1641470
Conference
19th ACM Conference on Embedded Networked Sensor Systems, SenSys 2021, 15 November 2021 through 17 November 2021
Available from: 2022-03-02 Created: 2022-03-02 Last updated: 2022-11-07Bibliographically approved

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Taheri, Javid

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • Other style
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Language
  • de-DE
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  • nn-NO
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  • Other locale
More languages
Output format
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