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Offload shaping for wearable cognitive assistance
Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Carnegie Mellon University, Pittsburgh, Pennsylvania, USA. (ADSlab)ORCID iD: 0000-0002-9156-3364
Intel Labs, Hillsboro, Oregon, USA.
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2023 (English)In: 2023 IEEE International Conference on Edge Computing and Communications (EDGE), IEEE, 2023Conference paper, Published paper (Refereed)
Abstract [en]

Edge computing has much lower elasticity than cloud computing because cloudlets have much smaller physical and electrical footprints than a data center. This hurts the scalability of applications that involve low-latency edge offload. We show how this problem can be addressed by leveraging the growing sophistication and compute capability of recent wearable devices. We investigate four Wearable Cognitive Assistance applications on three wearable devices, and show that the technique of offload shaping can significantly reduce network utilization and cloudlet load without compromising accuracy or performance.

Place, publisher, year, edition, pages
IEEE, 2023.
Series
IEEE International Conference on Edge Computing, E-ISSN 2767-9918
Keywords [en]
Computer Vision, Machine Learning, Offloading, Wearable Computing, Mobile Computing, Edge Computing, IoT, Cloudlet, Augmented Reality, 5G, Wi-Fi
National Category
Communication Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-214470DOI: 10.1109/EDGE60047.2023.00037ISBN: 979-8-3503-0484-8 (print)ISBN: 979-8-3503-0483-1 (electronic)OAI: oai:DiVA.org:umu-214470DiVA, id: diva2:1797878
Conference
2023 IEEE International Conference on Edge Computing and Communications (EDGE), Hybrid via Chicago, IL, USA, July 2-8, 2023
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2023-09-17 Created: 2023-09-17 Last updated: 2023-09-18Bibliographically approved

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Nguyen, Chanh Le Tan

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • nn-NB
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Output format
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