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Intelligence Partitioning as a Method for Architectural Exploration of Wireless Sensor Node
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design. (STC)ORCID iD: 0000-0001-5615-7347
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design. (STC)ORCID iD: 0000-0003-1923-3843
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design. (STC)
2017 (English)In: Proceedings of the International Conference on Computational Science and Computational Intelligence (CSCI), 2016., IEEE Press, 2017, 935-940 p., 7881473Conference paper (Refereed)
Abstract [en]

Embedded systems with integrated sensing, processing and wireless communication are driving future connectivity concepts such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). Because of resource limitations, there still exists a number of challenges such as low latency and energy consumption to realize these concepts to full potential. To address and understand these challenges, we have developed and employed an intelligence partitioning method which generates different implementation alternatives by distributing processing load across multiple nodes. The task-to-node mapping has exponential complexity which is hard to compute for a large scale system. Regarding this, our method provides recommendation to handle and minimize such complexity for a large system. Experiments on a use-case concludes that the proposed method is able to identify unfavourable architecture solutions in which forward and backword communication paths exists in task-to-node mapping. These solution can be avoided for further architectural exploration, thus limiting the space for architecture exploration of a sensor node.

Place, publisher, year, edition, pages
IEEE Press, 2017. 935-940 p., 7881473
Keyword [en]
Edge computing, intelligence partitioning, embedded computing
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:miun:diva-30736DOI: 10.1109/CSCI.2016.0180ScopusID: 2-s2.0-85017325247ISBN: 978-1-5090-5510-4 (print)OAI: oai:DiVA.org:miun-30736DiVA: diva2:1095834
Conference
2016 International Conference on Computational Science and Computational Intelligence, 15-17 Dec. 2016, Las Vegas, NV, USA
Projects
ASISSMART
Funder
Knowledge Foundation
Available from: 2017-05-16 Created: 2017-05-16 Last updated: 2017-06-21Bibliographically approved

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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