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Deep reinforcement learning for IoT network dynamic clustering in edge computing

Liu, Qingzhi, Cheng, Long orcid logoORCID: 0000-0003-1638-059X, Ozcelebi, Tanir, Murphy, John and Lukkien, Johan (2019) Deep reinforcement learning for IoT network dynamic clustering in edge computing. In: 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 14-17 May 2019, Larnica, Cyprus.

Abstract
How to process the big data generated in large IoT networks is still challenging current techniques. To date, a lot of network clustering approaches have been proposed to improve the performance of data aggregation in IoT. However, most of them focus on partitioning networks with static topologies, and thus they are not optimal on handling the case with moving objects in the networks. Moreover, as the best of knowledge, none of them has ever considered the performance of following computing in edge servers. To improve these problems, in this work, 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 by Deep Q-learning Network (DQN) model, and our preliminary experimental results show that the DQN solution can achieve higher score in cluster partitioning compared with the current static benchmark solution.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Big Data; data analysis; Internet of Things;learning; pattern clustering;Deep Reinforcement Learning; DQN; IoT Network; Dynamic Clustering; Edge Computing
Subjects:Computer Science > Artificial intelligence
DCU Faculties and Centres:UNSPECIFIED
Published in: 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. CCGRID . IEEE.
Publisher:IEEE
Official URL:http://dx.doi.org/10.1109/CCGRID.2019.00077
Copyright Information:2019 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:24289
Deposited On:19 Mar 2020 13:07 by Long Cheng . Last Modified 19 Mar 2020 14:45
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