Deep reinforcement learning for IoT network dynamic clustering in edge computing

Qingzhi Liu, Long Cheng, Tanir Ozcelebi, John Murphy, Johan Lukkien

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

26 Citaten (Scopus)

Samenvatting

Processing big data generated in large Internet of Things (IoT) networks is challenging current techniques. To date, a lot of network clustering approaches have been proposed to improve the performance of data collection in IoT. However, most of them focus on partitioning networks with static topologies, and thus they are not optimal in handling the case with moving objects in the networks. Moreover, to the best of our knowledge, none of them has ever considered the performance of computing in edge servers. To solve these problems, we propose a highly efficient IoT network dynamic clustering solution in edge computing using deep reinforcement learning (DRL). Our approach can both fulfill the data communication requirements from IoT networks and load-balancing requirements from edge servers, and thus provide a great opportunity for future high performance IoT data analytics. We implement our approach using a Deep Q-learning Network (DQN) model, and our preliminary experimental results show that the DQN solution can achieve higher scores in cluster partitioning compared with the current static benchmark solution.

Originele taal-2Engels
TitelProceedings - 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's600-603
Aantal pagina's4
ISBN van elektronische versie978-1-7281-0912-1
DOI's
StatusGepubliceerd - 1 mei 2019
Evenement19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019 - Larnaca, Cyprus
Duur: 14 mei 201917 mei 2019

Congres

Congres19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019
Land/RegioCyprus
StadLarnaca
Periode14/05/1917/05/19

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