Intelligent DSA-assisted clustered IoT networks: neuromorphic computing meets genetic algorithm
Authors: 
Qiang Fan, Jianan Bai, Hao-Hsuan Chang, Lianjun Li, Shiya Liu, Joe Huang, John Burgess, Allan Berlinsky, Alex Pidwerbetsky, Jonathan Ashdown, Kurt Turck, Lingjia LiuAuthors Info & Claims
Article No.: 24, Pages 1 - 6
Abstract
Dynamic spectrum access (DSA) is a promising technology to increase the spectrum efficiency of Internet of Things (IoT) networks, where the traffic demand grows up dramatically recently. In this paper, an intelligent DSA-assisted IoT network is introduced, where we investigate the spectrum sensing through neuromorphic computing (NC) and spectrum access through genetic algorithm (GA)-based power allocation. To be specific, we apply the NC's unconventional computing architectures that exploit and harness the intrinsic dynamics for computation, and thus provide increased functionality with better spectrum sensing performance requiring significantly lower size, weight, and power budgets. Furthermore, we design a GA algorithm to intelligently search the desirable transmission power for multiple IoT devices sharing the same channel to enhance the capacity of the highly dynamic DSA-assisted IoT network. Extensive simulation results have demonstrated the benefits of NC and GA compared to other baseline algorithms and methodologies.
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- Intelligent DSA-assisted clustered IoT networks: neuromorphic computing meets genetic algorithm
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Published In
September 2020
142 pages
ISBN:9781450380836
DOI:10.1145/3411295
- General Chairs:
- William E. Bentley,
- Gregory F. Payne,
- Valeria Loscri
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Published: 07 October 2020
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- National Spectrum Consortium
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NANOCOM '20
NANOCOM '20: The Seventh Annual ACM International Conference on Nanoscale Computing and Communication
September 23 - 25, 2020
Virtual Event, USA
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NanoCom '20 Paper Acceptance Rate 24 of 24 submissions, 100%;
Overall Acceptance Rate 97 of 135 submissions, 72%
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