# A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks with Transient Data

arXiv: https://arxiv.org/abs/2203.12674 · HTML: https://mlsanity.com/paper/2203.12674/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 16 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications](https://mlsanity.com/paper/1703.10750/) (2017)
- [Cooperative Edge Caching in User-Centric Clustered Mobile Networks](https://mlsanity.com/paper/1710.08582/) (2017)
- [The Design of Dynamic Probabilistic Caching with Time-Varying Content Popularity](https://mlsanity.com/paper/2002.06251/) (2020)
- [Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks](https://mlsanity.com/paper/1905.05256/) (2019)

## Built on by (most cited first, 3 of 3)

- [Attention-Enhanced Prioritized Proximal Policy Optimization for Adaptive Edge Caching](https://mlsanity.com/paper/2402.14576/) (2024)
- [Edge Caching Optimization with PPO and Transfer Learning for Dynamic Environments](https://mlsanity.com/paper/2411.09812/) (2024)
- [Fast-Convergent Meta-RL via Gradient-Clustered BS Sampling for Edge Caching](https://mlsanity.com/paper/2609.16370/) (2026)

## Abstract (arXiv)

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.