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In the Internet of Things (IoT) networks, caching is a promising technique toalleviate energy consumption of sensors by responding to users' data requestswith the data packets cached in the edge caching node (ECN). However, withoutan efficient status update strategy, the information obtained by users may bestale, which in return would inevitably deteriorate the accuracy andreliability of derived decisions for real-time applications. In this paper, wefocus on striking the balance between the information freshness, in terms ofage of information (AoI), experienced by users and energy consumed by sensors,by appropriately activating sensors to update their current status.Particularly, we first depict the evolutions of the AoI with each sensor fromdifferent users' perspective with time steps of non-uniform duration, which aredetermined by both the users' data requests and the ECN's status updatedecision. Then, we formulate a non-uniform time step based dynamic statusupdate optimization problem to minimize the long-term average cost, jointlyconsidering the average AoI and energy consumption. To this end, a MarkovDecision Process is formulated and further, a dueling deep R-network baseddynamic status update algorithm is devised by combining dueling deep Q-networkand tabular R-learning, with which challenges from the curse of dimensionalityand unknown of the environmental dynamics can be addressed. Finally, extensivesimulations are conducted to validate the effectiveness of our proposedalgorithm by comparing it with five baseline deep reinforcement learningalgorithms and policies.

Optimal Status Update for Caching Enabled IoT Networks: A Dueling Deep R-Network Approach