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Caching has been regarded as a promising technique to alleviate energyconsumption of sensors in Internet of Things (IoT) networks by responding tousers' requests with the data packets stored in the edge caching node (ECN).For real-time applications in caching enabled IoT networks, it is essential todevelop dynamic status update strategies to strike a balance between theinformation freshness experienced by users and energy consumed by the sensor,which, however, is not well addressed. In this paper, we first depict theevolution of information freshness, in terms of age of information (AoI), ateach user. Then, we formulate a dynamic status update optimization problem tominimize the expectation of a long term accumulative cost, which jointlyconsiders the users' AoI and sensor's energy consumption. To solve thisproblem, a Markov Decision Process (MDP) is formulated to cast the statusupdating procedure, and a model-free reinforcement learning algorithm isproposed, with which the challenge brought by the unknown of the formulatedMDP's dynamics can be addressed. Finally, simulations are conducted to validatethe convergence of our proposed algorithm and its effectiveness compared withthe zero-wait baseline policy.

AoI and Energy Consumption Oriented Dynamic Status Updating in Caching Enabled IoT Networks