A survey on the convergence of edge computing and AI for UAVs: Opportunities and challenges

P McEnroe, S Wang, M Liyanage - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
The latest 5G mobile networks have enabled many exciting Internet of Things (IoT)
applications that employ unmanned aerial vehicles (UAVs/drones). The success of most …

Security and privacy on 6g network edge: A survey

B Mao, J Liu, Y Wu, N Kato - IEEE communications surveys & …, 2023 - ieeexplore.ieee.org
To meet the stringent service requirements of 6G applications such as immersive cloud
eXtended Reality (XR), holographic communication, and digital twin, there is no doubt that …

Decentralized ai: Edge intelligence and smart blockchain, metaverse, web3, and desci

L Cao - IEEE Intelligent Systems, 2022 - ieeexplore.ieee.org
Centralization has dominated classic scientific, social, and economic developments.
Decentralization has also received increasing attention in management, decision …

AI models for green communications towards 6G

B Mao, F Tang, Y Kawamoto… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
Green communications have always been a target for the information industry to alleviate
energy overhead and reduce fossil fuel usage. In the current 5G and future 6G eras, there is …

Decentralized edge intelligence: A dynamic resource allocation framework for hierarchical federated learning

WYB Lim, JS Ng, Z Xiong, J Jin, Y Zhang… - … on Parallel and …, 2021 - ieeexplore.ieee.org
To enable the large scale and efficient deployment of Artificial Intelligence (AI), the
confluence of AI and Edge Computing has given rise to Edge Intelligence, which leverages …

At the confluence of artificial intelligence and edge computing in iot-based applications: A review and new perspectives

A Bourechak, O Zedadra, MN Kouahla, A Guerrieri… - Sensors, 2023 - mdpi.com
Given its advantages in low latency, fast response, context-aware services, mobility, and
privacy preservation, edge computing has emerged as the key support for intelligent …

Efficient acceleration of deep learning inference on resource-constrained edge devices: A review

MMH Shuvo, SK Islam, J Cheng… - Proceedings of the …, 2022 - ieeexplore.ieee.org
Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

Smartphone app usage analysis: datasets, methods, and applications

T Li, T Xia, H Wang, Z Tu, S Tarkoma… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
As smartphones have become indispensable personal devices, the number of smartphone
users has increased dramatically over the last decade. These personal devices, which are …

Resource orchestration of cloud-edge–based smart grid fault detection

J Li, Y Deng, W Sun, W Li, R Li, Q Li, Z Liu - ACM Transactions on …, 2022 - dl.acm.org
Real-time smart grid monitoring is critical to enhancing resiliency and operational efficiency
of power equipment. Cloud-based and edge-based fault detection systems integrating deep …

Fog computing: A taxonomy, systematic review, current trends and research challenges

J Singh, P Singh, SS Gill - Journal of Parallel and Distributed Computing, 2021 - Elsevier
There has been rapid development in the number of Internet of Things (IoT) connected
nodes and devices in our daily life in recent times. With this increase in the number of …