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

TL;DR

This paper introduces a deep reinforcement learning-based caching strategy for IoT networks that enhances cache hit rate and reduces energy consumption while considering data freshness and limited data lifetime.

Contribution

It presents a novel hierarchical DRL-based caching scheme tailored for IoT networks, addressing data freshness and regional popularity distribution.

Findings

Outperforms conventional caching policies in cache hit rate.

Reduces energy consumption significantly.

Effectively manages data freshness and limited data lifetime.

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.