Optimal Caching Strategy for Data Freshness in IoT Applications with Edge Computing

The monitoring of ambient environments relies heavily on data fetched from sensors in the Internet of Things (IoT) services. However, with the increasing number of mobile users and IoT applications, there has been a surge in traffic on IoT networks, leading to faster draining of sensor batteries. Hence, caching at the IoT Edge has emerged as a promising solution, reducing network congestion and energy consumption. This paper proposes an optimal custom caching strategy tailored to Edge computing. By considering factors such as the number of requests, battery level, and Age of Information (AoI) for each sensor, the Edge node decides whether to command a sensor to send a status update or retrieve data from the cache. We formulate the dynamic content caching challenge as a Markov Decision Process (MDP) to optimize jointly long-term caching costs. The proposed relative value iteration algorithm effectively solves the MDP problem without prior knowledge of user preference. Simulation demonstrates that the proposed policy outperforms the greedy and LRU policies with 10.61% and 44.05% lower cache miss rates, respectively, and a 69% slower energy depletion rate and significantly longer node longevity.