A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks
with Transient Data
- URL: http://arxiv.org/abs/2203.12674v1
- Date: Wed, 16 Mar 2022 12:41:37 GMT
- Title: A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks
with Transient Data
- Authors: Hongda Wu, Ali Nasehzadeh, Ping Wang
- Abstract summary: Transient data generation and limited energy resources are the major bottlenecks of Internet of Things networks.
We propose a DRL-based caching scheme that improves the cache hit rate and reduces energy consumption of the IoT networks.
- Score: 4.686103742494879
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The Internet of Things (IoT) has been continuously rising in the past few
years, and its potentials are now more apparent. However, transient data
generation and limited energy resources are the major bottlenecks of these
networks. Besides, minimum delay and other conventional quality of service
measurements are still valid requirements to meet. An efficient caching policy
can help meet the standard quality of service requirements while bypassing IoT
networks' specific limitations. Adopting deep reinforcement learning (DRL)
algorithms enables us to develop an effective caching scheme without the need
for any prior knowledge or contextual information. In this work, we propose a
DRL-based caching scheme that improves the cache hit rate and reduces energy
consumption of the IoT networks, in the meanwhile, taking data freshness and
limited lifetime of IoT data into account. To better capture the
regional-different popularity distribution, we propose a hierarchical
architecture to deploy edge caching nodes in IoT networks. The results of
comprehensive experiments show that our proposed method outperforms the
well-known conventional caching policies and an existing DRL-based solution in
terms of cache hit rate and energy consumption of the IoT networks by
considerable margins.
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