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|Title:||Optimal status update for caching enabled IoT networks: a dueling deep R-network approach||Authors:||Xu, Chao
Yang, Howard H.
Quek, Tony Q. S.
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2021||Source:||Xu, C., Xie, Y., Wang, X., Yang, H. H., Niyato, D. & Quek, T. Q. S. (2021). Optimal status update for caching enabled IoT networks: a dueling deep R-network approach. IEEE Transactions On Wireless Communications, 20(12), 8438-8454. https://dx.doi.org/10.1109/TWC.2021.3093352||Project:||T2EP20120-0006||Journal:||IEEE Transactions on Wireless Communications||Abstract:||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.||URI:||https://hdl.handle.net/10356/162972||ISSN:||1536-1276||DOI:||10.1109/TWC.2021.3093352||Rights:||© 2021 IEEE. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
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