Experimental Analysis of DRL-Based Edge Caching for the Internet of Things

Internet of Things (IoT) infrastructures can undoubtedly derive significant benefits from content-centric networking (CCN). Thus, a flurry of research focuses on their integration for better content retrieval. Despite the plethora of caching strategies available today, none stands out by efficiently taking into account the limitations of sensors and IoT systems such as low data-rate and energy, as well as limited memory and processing capability. In this work, we focus our attention on the effectiveness of caching on IoT edge gateways. To this end, we propose a novel Deep Reinforcement Learning (DRL) strategy that establishes an intelligent placement of contents and promotes the consideration of content popularity as well as the diversity of data in the network. Through extensive experiments and a real-world testbed on our campus, we perform an in-depth comparative analysis. We demonstrate that our approach decreases the data retrieval distance, assuredly leading to energy efficiency and reduced latency. Moreover, our DRL-based solution outperforms other strategies in terms of cache hit, content diversity, and content popularity support.