Deep reinforcement learning for IoT network dynamic clustering in edge computing

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages600-603
Number of pages4
ISBN (Electronic)978-1-7281-0912-1
DOIs
Publication statusPublished - 1 May 2019
Event19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019 - Larnaca, Cyprus
Duration: 14 May 201917 May 2019

Conference

Conference19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019
CountryCyprus
CityLarnaca
Period14/05/1917/05/19

Keywords

  • Deep Reinforcement Learning
  • DQN
  • Dynamic Clustering
  • Edge Computing
  • IoT Network

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  • Cite this

    Liu, Q., Cheng, L., Ozcelebi, T., Murphy, J., & Lukkien, J. (2019). Deep reinforcement learning for IoT network dynamic clustering in edge computing. In Proceedings - 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019 (pp. 600-603). [8752691] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CCGRID.2019.00077