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  • Final-TCE-2023-08-0953

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Edge Data Caching With Consumer-Centric Service Prediction in Resilient Industry 5.0

Research output: Contribution to Journal/MagazineJournal articlepeer-review

E-pub ahead of print
  • Guoqiang Liu
  • Guanming Bao
  • Muhammad Bilal
  • Angel Jones
  • Zhipeng Jing
  • Xiaolong Xu
<mark>Journal publication date</mark>26/10/2023
<mark>Journal</mark>IEEE Transactions on Consumer Electronics
Publication StatusE-pub ahead of print
Early online date26/10/23
<mark>Original language</mark>English


Industry 5.0, emerging as a promising industry paradigm, unleashes the potential of improving consumer experience by delivering consumer-centric services, facilitating substantial growth in consumer electronics. To improve the resilience of industry 5.0, edge data caching enables sustainable and low-latency service provision by caching data at edge servers (ESs) closer to production. However, the limited caching capacity of ESs presents a formidable challenge to efficient edge data caching. Moreover, the dynamic of consumer-centric service requests further complicates the effective implementation of caching strategies. In response to the above challenges, we propose an edge data caching scheme, named SPM-ECDP, with consumer-centric service prediction for Industry 5.0. Initially, a time-series prediction model is employed to forecast the service demands. To ensure the confidentiality of data, federated learning is introduced in the model training phase. Subsequently, reinforcement learning is adopted to enable ESs to make intelligent decisions on edge data caching, consequently enhancing caching efficiency. Through comprehensive simulation experiments, the effectiveness and superiority of the proposed scheme in increasing caching hit ratio and reducing data delivery delays are demonstrated. The experimental results demonstrate that the proposed SPM-ECDP method has enhanced the hit ratio by 7.05% -48.5% when compared to the baseline method.