AI-Enhanced Cloud-Edge-Terminal Collaborative Network: Survey, Applications, and Future Directions
The cloud-edge-terminal collaborative network (CETCN) is considered as a novel paradigm
for emerging applications owing to its huge potential in providing low-latency and ultra …
for emerging applications owing to its huge potential in providing low-latency and ultra …
MAP based modeling method and performance study of a task offloading scheme with time-correlated traffic and VM repair in MEC systems
Y Wang, X Han, S Jin - Wireless Networks, 2023 - Springer
Abstract Mobile Edge Computing (MEC) has evolved into a key technology that can
leverage resources of computing, storage and network deployed at the proximity of the …
leverage resources of computing, storage and network deployed at the proximity of the …
New challenges in reinforcement learning: a survey of security and privacy
Reinforcement learning is one of the most important branches of AI. Due to its capacity for
self-adaption and decision-making in dynamic environments, reinforcement learning has …
self-adaption and decision-making in dynamic environments, reinforcement learning has …
A green, secure, and deep intelligent method for dynamic IoT-edge-cloud offloading scenarios
To fulfill people's expectations for smart and user-friendly Internet of Things (IoT)
applications, the quantity of processing is fast expanding, and task latency constraints are …
applications, the quantity of processing is fast expanding, and task latency constraints are …
A Caching-Based Dual K-Anonymous Location Privacy-Preserving Scheme for Edge Computing
Location-based services have become prevalent and the risk of location privacy leakage
increases. Most existing schemes use third-party-based or third-party-free system …
increases. Most existing schemes use third-party-based or third-party-free system …
Real-time virtual machine scheduling in industry IoT network: A reinforcement learning method
X Ma, H Xu, H Gao, M Bian… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The widespread adoption of Industrial Internet of Things (IIoT)-based applications has driven
the emergence and development of cloud-related computing paradigms with the ability to …
the emergence and development of cloud-related computing paradigms with the ability to …
EPtask: Deep reinforcement learning based energy-efficient and priority-aware task scheduling for dynamic vehicular edge computing
The increasing complexity of vehicles has led to a growing demand for in-vehicle services
that rely on multiple sensors. In the Vehicular Edge Computing (VEC) paradigm, energy …
that rely on multiple sensors. In the Vehicular Edge Computing (VEC) paradigm, energy …
Excavating multimodal correlation for representation learning
A majority of previous methods for multimodal representation learning ignore the rich
correlation information inherently stored in each sample, leading to a lack of robustness …
correlation information inherently stored in each sample, leading to a lack of robustness …
Game-based task offloading and resource allocation for vehicular edge computing with edge-edge cooperation
W Fan, M Hua, Y Zhang, Y Su, X Li… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Vehicular Edge Computing (VEC) enables task offloading from vehicles to the edge servers
deployed on Road Side Units (RSUs), thus enhancing the task processing performance of …
deployed on Road Side Units (RSUs), thus enhancing the task processing performance of …
Opportunistic offloading scheme for content delivery service using electro‐mobility networks
Vehicular caching (VC) in electro‐mobility networks has become promising for supporting
the needs of low end‐to‐end service delays and reducing the load of networks (ie edge …
the needs of low end‐to‐end service delays and reducing the load of networks (ie edge …