Resource scheduling in edge computing: A survey

Q Luo, S Hu, C Li, G Li, W Shi - IEEE Communications Surveys …, 2021 - ieeexplore.ieee.org
With the proliferation of the Internet of Things (IoT) and the wide penetration of wireless
networks, the surging demand for data communications and computing calls for the …

Task offloading paradigm in mobile edge computing-current issues, adopted approaches, and future directions

MY Akhlaqi, ZBM Hanapi - Journal of Network and Computer Applications, 2023 - Elsevier
Many enterprise companies migrate their services and applications to the cloud to benefit
from cloud computing advantages. Meanwhile, the rapidly increasing number of connected …

Intelligent delay-aware partial computing task offloading for multiuser industrial Internet of Things through edge computing

X Deng, J Yin, P Guan, NN ** internet-based technologies has been leading to
propose promising methods to handle the heterogeneous massive volume of data produced …

A hybrid deep learning architecture for privacy-preserving mobile analytics

SA Osia, AS Shamsabadi… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Internet-of-Things (IoT) devices and applications are being deployed in our homes and
workplaces. These devices often rely on continuous data collection to feed machine learning …

Network attacks detection methods based on deep learning techniques: a survey

Y Wu, D Wei, J Feng - Security and Communication Networks, 2020 - Wiley Online Library
With the development of the fifth‐generation networks and artificial intelligence
technologies, new threats and challenges have emerged to wireless communication system …

Diagnosis of skin diseases in the era of deep learning and mobile technology

E Goceri - Computers in Biology and Medicine, 2021 - Elsevier
Efficient methods developed with deep learning in the last ten years have provided
objectivity and high accuracy in the diagnosis of skin diseases. They also support accurate …