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 language | English |
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Title of host publication | Proceedings - 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019 |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 600-603 |
Number of pages | 4 |
ISBN (Electronic) | 978-1-7281-0912-1 |
DOIs | |
Publication status | Published - 1 May 2019 |
Event | 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019 - Larnaca, Cyprus Duration: 14 May 2019 → 17 May 2019 |
Conference
Conference | 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019 |
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Country/Territory | Cyprus |
City | Larnaca |
Period | 14/05/19 → 17/05/19 |
Keywords
- Deep Reinforcement Learning
- DQN
- Dynamic Clustering
- Edge Computing
- IoT Network