Deep reinforcement learning for IoT network dynamic clustering in edge computing
Liu, Qingzhi, Cheng, LongORCID: 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.
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