… request by learning content popularity. However, the content requests of edge networks
are time-varying and bursty. On the one hand, it is difficult for shallow machine learning models …

… a cognitive state for big data analytics and statistical machine learning to predict cyber risks
[19]. But the design of big data systems for edge computing environments is challenging [20]. …

… Additionally, the recognition effect of the improved CNN is significantly higher than the
Support Vector Machine (SVM) in traditional Machine Learning (ML). This work realizes the …

… (AI) have raised rethinking on the evolution of wireless networks. Mobile edge computing
(MEC) … In this paper, we present an infrastructure to perform machine learning tasks at an MEC …

… the evolution of data and security flaws and their corresponding solutions in smart edge …
using consensus approach of Blockchain and machine learning techniques that include several …

… Kato, “Machine learning meets computation and communication control in evolving edge
and cloud: Challenges and future perspective,” IEEE Commun. Surveys Tuts., 2019, doi: …

… edge computing grows, many modern consumer devices now contain edge machine learning
(… We analyze a commercial Edge TPU using 24 Google edge NN models (including CNNs, …

… This chapter presents the state-of-the-art edge computing and machine learning … the
machine learning concepts along with the IoT and cloud and thereby concentrating on the edge …

… through the blockchain, edge computing, and machine learning to develop and facilitate the
… of blockchain, edge computing, and machine learning approaches. Edge computing makes …

… work’s edge, for widespread adoption of IoT applications. In this vein, the classic machine
learning … could mark the evolution of more sophisticated models that can detect cyberattacks in …