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MOSAIC: Multiobjective Optimization Strategy for AI-Aided Internet of Things Communications

Authors
Lee, HoonLee, Sang HyunQuek, Tony Q. S.
Issue Date
1-9월-2022
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Deep learning (DL); distributed network management; multiobjective optimization; primal-dual training
Citation
IEEE INTERNET OF THINGS JOURNAL, v.9, no.17, pp.15657 - 15673
Indexed
SCIE
SCOPUS
Journal Title
IEEE INTERNET OF THINGS JOURNAL
Volume
9
Number
17
Start Page
15657
End Page
15673
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/143739
DOI
10.1109/JIOT.2022.3150747
ISSN
2327-4662
Abstract
Future Internet of Things (IoT) communication trends toward heterogeneous services and diverse quality-of-service requirements pose fundamental challenges for network management strategies. In particular, multiobjective optimization (MOO) is necessary in resolving the competition among different nodes sharing limited wireless network resources. A unified coordination mechanism is essential such that individual nodes conduct the opportunistic maximization of heterogeneous local objectives for efficient distributed resource allocation. To such a problem, this article proposes an artificial intelligence (AI)-based framework, which is termed as MOO strategy for AI-aided IoT communications (MOSAIC). This framework enables to tackle numerous MOO tasks in IoT network management with simple reconfiguration of learning rules. In this strategy, a component unit associated with an individual network node includes a pair of deep neural networks (DNNs) to learn optimal local functions responsible for calculation and distributed coordination, respectively. The resultant AI module swarm called DNN tiles realizes the node cooperation that collectively seeks distributed MOO calculation rules. The advantage of MOSAIC is characterized by Pareto tradeoffs among conflicting performance metrics in diverse wireless networking configurations subject to severe interference and distinct criteria for multiple targets.
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LEE, SANG HYUN
공과대학 (전기전자공학부)
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