AoI and Energy-Driven Dynamic Cache Updates for Wireless Edge Networks

Wireless edge networks (WENTs) can provide edge services to support various time-critical Internet of Things (IoT) applications, like autonomous vehicles, where cache content updates are significant to maintaining information freshness quantified as Information of Age (AoI). However, frequent content updates result in high energy consumption at the edge nodes. This article investigates the cache content updates in WENTs, aiming to ensure information freshness and low energy consumption. To this end, we propose a rainbow deep reinforcement learning-based cache content update scheme (RB-DRN). In the RB-DRN scheme, we first establish a Markov decision process (MDP) to characterize the process of cache update. By fully taking advantage of R-Learning empowered Rainbow DQN, we then make optimal strategy to obtain the minimum long-term average overhead associated with energy consumption and information freshness. Extensive simulation results are presented to validate our proposed RB-DRN scheme and also to illustrate that our RB-DRN scheme outperforms the benchmark scheme in terms of information freshness and energy consumption.