A comprehensive survey on aerial mobile edge computing: Challenges, state-of-the-art, and future directions
Driven by the visions of Internet of Things (IoT), there is an ever-increasing demand for
computation resources of IoT users to support diverse applications. Mobile edge computing …
computation resources of IoT users to support diverse applications. Mobile edge computing …
[HTML][HTML] Utilizing grid search cross-validation with adaptive boosting for augmenting performance of machine learning models
M Adnan, AAS Alarood, MI Uddin… - PeerJ Computer Science, 2022 - peerj.com
Abstract Corona Virus Disease 2019 (COVID-19) pandemic has increased the importance of
Virtual Learning Environments (VLEs) instigating students to study from their homes. Every …
Virtual Learning Environments (VLEs) instigating students to study from their homes. Every …
Toward using reinforcement learning for trigger selection in network slice mobility
Recent 5G trials have demonstrated the usefulness of the Network Slicing concept that
delivers customizable services to new and under-serviced industry sectors. However, user …
delivers customizable services to new and under-serviced industry sectors. However, user …
Knowledge-guided learning for transceiver design in over-the-air federated learning
In this paper, we consider communication-efficient over-the-air federated learning (FL),
where multiple edge devices with non-independent and identically distributed datasets …
where multiple edge devices with non-independent and identically distributed datasets …
Joint offloading scheduling and resource allocation in vehicular edge computing: A two layer solution
Vehicular Edge Computing (VEC) is a promising paradigm for autonomous driving. It can
reduce delay and energy consumption of tasks. The problem of joint task offloading …
reduce delay and energy consumption of tasks. The problem of joint task offloading …
Anticipatory allocation of communication and computational resources at the edge using spatio-temporal dynamics of mobile users
Multi-access Edge Computing represents a key enabling technology for emerging mobile
networks. It offers intensive computational resources very close to the end-users, useful for …
networks. It offers intensive computational resources very close to the end-users, useful for …
Dynamic priority-based computation scheduling and offloading for interdependent tasks: Leveraging parallel transmission and execution
R Chai, M Li, T Yang, Q Chen - IEEE Transactions on Vehicular …, 2021 - ieeexplore.ieee.org
Mobile edge computing (MEC) has recently emerged as an effective paradigm to enhance
the computing capability of capability-limited mobile devices (MDs). In this article, we …
the computing capability of capability-limited mobile devices (MDs). In this article, we …
AI based service management for 6G green communications
Green communications have always been a target for the information industry to alleviate
energy overhead and reduce fossil fuel usage. In current 5G and future 6G era, there is no …
energy overhead and reduce fossil fuel usage. In current 5G and future 6G era, there is no …
A deep rl-based algorithm for coordinated charging of electric vehicles
The development of electric vehicle (EV) industry is facing a series of issues, among which
the efficient charging of multiple EVs needs solving desperately. This paper investigates the …
the efficient charging of multiple EVs needs solving desperately. This paper investigates the …
[PDF][PDF] Multi-Agent Deep Q-Networks for Efficient Edge Federated Learning Communications in Software-Defined IoT.
Federated learning (FL) activates distributed on-device computation techniques to model a
better algorithm performance with the interaction of local model updates and global model …
better algorithm performance with the interaction of local model updates and global model …