A Reactive Architectural Proposal for Fog/Edge Computing in the Internet of Things Paradigm with Application in Deep Learning

Belmonte-Fernández, Óscar; Sansano-Sansano, Emilio; Trilles, Sergio; Miedes, Antonio Caballer

doi:10.1007/978-3-030-84459-2_9

Cited by 4 publications

(2 citation statements)

References 49 publications

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“…where whether or not to cache content e k in stage st[i] is determined by W k . When deciding on cache e i in the st [1] stage, THCS needs to check whether the current cache space can accommodate content e i , the optimal solution of R i,j is as follows:…”

Section: Decision Making Based On Dynamic Planningmentioning

confidence: 99%

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Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network

Jin

Xing

2023

Sensors

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With the continuous development of intelligent vehicles, people’s demand for services has also rapidly increased, leading to a sharp increase in wireless network traffic. Edge caching, due to its location advantage, can provide more efficient transmission services and become an effective method to solve the above problems. However, the current mainstream caching solutions only consider content popularity to formulate caching strategies, which can easily lead to cache redundancy between edge nodes and lead to low caching efficiency. To solve these problems, we propose a hybrid content value collaborative caching strategy based on temporal convolutional network (called THCS), which achieves mutual collaboration between different edge nodes under limited cache resources, thereby optimizing cache content and reducing content delivery latency. Specifically, the strategy first obtains accurate content popularity through temporal convolutional network (TCN), then comprehensively considers various factors to measure the hybrid content value (HCV) of cached content, and finally uses a dynamic programming algorithm to maximize the overall HCV and make optimal cache decisions. We have obtained the following conclusion through simulation experiments: compared with the benchmark scheme, THCS has improved the cache hit rate by 12.3% and reduced the content transmission delay by 16.7%.

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Section: Decision Making Based On Dynamic Planningmentioning

confidence: 99%

“…With the rapid development of the Internet of Things (IoT) [ 1 ] and Artificial Intelligence (AI) [ 2 ] technology, intelligent connected vehicles (ICV) are on the rise. Gartner predicts that in 2023, most of the vehicles on the road will be connected to the Internet, and connected vehicles will be the largest market for 5G in 2025.…”

Section: Introductionmentioning

confidence: 99%