Convergence of edge computing and deep learning: A comprehensive survey

X Wang, Y Han, VCM Leung, D Niyato… - … Surveys & Tutorials, 2020 - ieeexplore.ieee.org
Ubiquitous sensors and smart devices from factories and communities are generating
massive amounts of data, and ever-increasing computing power is driving the core of …

A survey of recent advances in edge-computing-powered artificial intelligence of things

Z Chang, S Liu, X Xiong, Z Cai… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
The Internet of Things (IoT) has created a ubiquitously connected world powered by a
multitude of wired and wireless sensors generating a variety of heterogeneous data over …

Edge intelligence: Paving the last mile of artificial intelligence with edge computing

Z Zhou, X Chen, E Li, L Zeng, K Luo… - Proceedings of the …, 2019 - ieeexplore.ieee.org
With the breakthroughs in deep learning, the recent years have witnessed a booming of
artificial intelligence (AI) applications and services, spanning from personal assistant to …

Machine learning at the network edge: A survey

MGS Murshed, C Murphy, D Hou, N Khan… - ACM Computing …, 2021 - dl.acm.org
Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous
in recent years. This has led to the generation of large quantities of data in real-time, which …

Autonomous vehicles enabled by the integration of IoT, edge intelligence, 5G, and blockchain

A Biswas, HC Wang - Sensors, 2023 - mdpi.com
The wave of modernization around us has put the automotive industry on the brink of a
paradigm shift. Leveraging the ever-evolving technologies, vehicles are steadily …

A survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic

A Sufian, A Ghosh, AS Sadiq… - Journal of Systems …, 2020 - Elsevier
Global Health sometimes faces pandemics as are currently facing COVID-19 disease. The
spreading and infection factors of this disease are very high. A huge number of people from …

Machine learning in oil and gas; a SWOT analysis approach

Y Hajizadeh - Journal of Petroleum Science and Engineering, 2019 - Elsevier
Digitalization of workflows using machine learning and advanced analytics is the new go-to
strategy to add business value in the oil and gas industry. Enterprises strive to embrace …

Machine learning on mainstream microcontrollers

F Sakr, F Bellotti, R Berta, A De Gloria - Sensors, 2020 - mdpi.com
This paper presents the Edge Learning Machine (ELM), a machine learning framework for
edge devices, which manages the training phase on a desktop computer and performs …

Flee: A hierarchical federated learning framework for distributed deep neural network over cloud, edge, and end device

Z Zhong, W Bao, J Wang, X Zhu, X Zhang - ACM Transactions on …, 2022 - dl.acm.org
With the development of smart devices, the computing capabilities of portable end devices
such as mobile phones have been greatly enhanced. Meanwhile, traditional cloud …

Efficient fine-tuning of bert models on the edge

D Vucetic, M Tayaranian, M Ziaeefard… - … on Circuits and …, 2022 - ieeexplore.ieee.org
Resource-constrained devices are increasingly the deployment targets of machine learning
applications. Static models, however, do not always suffice for dynamic environments. On …