Customer Centric Service Caching for Intelligent Cyber–Physical Transportation Systems With Cloud–Edge Computing Leveraging Digital Twins

Yan, Hanzhi; Xu, Xiaolong; Bilal, Muhammad; Xia, Xiaoyu; Dou, Wanchun; Wang, Huihui

doi:10.1109/tce.2023.3326969

Cited by 5 publications

(1 citation statement)

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“…Due to the presence of macro base stations and multiple micro base stations in heterogeneous networks, which results in a complex structure with numerous nodes but limited resources, researchers have employed multi-agent reinforcement-learning approaches for cache decision making, to improve resource utilization, reduce content transmission delays, and better facilitate multi-base-station collaborative operation [26]. The studies of [27,28] explored the application of multi-intelligent-body learning in network resource allocation and caching strategies. Multi-agent reinforcement-learning networks are overly complex and have a singular focus on feature analysis, which can lead to a wastage of computational resources and inadequacies in addressing user diversity and adaptability.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Heterogeneous Network Performance: Advanced Content Popularity Prediction and Efficient Caching

Sun,

Chen

2024

Electronics

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With the popularity of smart devices and the growth of high-bandwidth applications, the wireless industry is facing an increased surge in data traffic. This challenge highlights the limitations of traditional edge-caching solutions, especially in terms of content-caching effectiveness and network-communication latency. To address this problem, we investigated efficient caching strategies in heterogeneous network environments. The caching decision process becomes more complex due to the heterogeneity of the network environment, as well as due to the diversity of user behaviors and content requests. To address the problem of increased system latency due to the dynamically changing nature of content popularity and limited cache capacity, we propose a novel content placement strategy, the long-short-term-memory–content-population-prediction model, to capture the correlation of request patterns between different contents and the periodicity in the time domain, in order to improve the accuracy of the prediction of content popularity. Then, to address the heterogeneity of heterogeneous network environments, we propose an efficient content delivery strategy: the multi-intelligent critical collaborative caching policy. This strategy models the edge-caching problem in heterogeneous scenarios as a Markov decision process using multi-base-station-environment information. In order to fully utilize the multi-intelligence information, we have improved the actor–critic approach by integrating the attention mechanism into a neural network. Whereas the actor network is responsible for making decisions based on local information, the critic network evaluates and enhances the actor’s performance. We conducted extensive simulations, and the results showed that the Long Short Term Memory content population prediction model was more advantageous, in terms of content-popularity-prediction accuracy, with a 28.61% improvement in prediction error, compared to several other existing methods. The proposed multi-intelligence actor–critic collaborative caching policy algorithm improved the cache-hit-rate metric by up to 32.3% and reduced the system latency by 1.6%, demonstrating the feasibility and effectiveness of the algorithm.

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Section: Related Workmentioning

confidence: 99%