Optimal status update for caching enabled IoT networks: a dueling deep R-network approach

In the Internet of Things (IoT) networks, caching is a promising technique to alleviate energy consumption of sensors by responding to users' data requests with the data packets cached in the edge caching node (ECN). However, without an efficient status update strategy, the information obtained by users may be stale, which in return would inevitably deteriorate the accuracy and reliability of derived decisions for real-time applications. In this paper, we focus on striking the balance between the information freshness, in terms of age 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 from different users' perspective with time steps of non-uniform duration, which are determined by both the users' data requests and the ECN's status update decision. Then, we formulate a non-uniform time step based dynamic status update optimization problem to minimize the long-term average cost, jointly considering the average AoI and energy consumption. To this end, a Markov Decision Process is formulated and further, a dueling deep R-network based dynamic status update algorithm is devised by combining dueling deep Q-network and tabular R-learning, with which challenges from the curse of dimensionality and unknown of the environmental dynamics can be addressed. Finally, extensive simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with five baseline deep reinforcement learning algorithms and policies.Ministry of Education (MOE)National Research Foundation (NRF)This work was supported in part by the National Natural Science Foundation of China under Grant 61701372, in part by the Chinese Universities Scientific Fund under Grant 2452017560, in part by the High-level Talents Fund of Shaanxi Province under Grant F2020221001, in part by the Technological Innovation Fund of Shaanxi Academy of Forestry under Grant SXLK2021-0215, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515012631, in part by the Zhejiang University/University of Illinois at Urbana–Champaign Institute Starting Fund, in part by the ZJU-UIUC Joint Research Center Project under Grant DREMES 202003, in part by the National Research Foundation, Singapore and Infocomm Media Development Authority under its Future Communications Research & Development Programme, and in part by the MOE ARF Tier 2 under Grant T2EP20120-0006