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 …
Artificial intelligence algorithms in flood prediction: a general overview
M Pandey - Geo-information for Disaster Monitoring and …, 2024 - Springer
This paper presents a comprehensive general overview of the extensive literature available
in the field of application of artificial intelligence (AI) in flood prediction. The initial approach …
in the field of application of artificial intelligence (AI) in flood prediction. The initial approach …
Failure-aware resource provisioning for hybrid computation offloading in cloud-assisted edge computing using gravity reference approach
MI Khaleel - Swarm and Evolutionary Computation, 2024 - Elsevier
This paper tackles the challenges of computation offloading in the cloud–edge paradigm.
Although many solutions exist for enhancing the server's computational and communication …
Although many solutions exist for enhancing the server's computational and communication …
Deadline-aware heuristics for reliability optimization in ubiquitous mobile edge computing
With the advent of affordable and widely accessible broadband and mobile internet, there
has been a significant surge in user demand. These demands, especially when considering …
has been a significant surge in user demand. These demands, especially when considering …
VEC-Sim: A simulation platform for evaluating service caching and computation offloading policies in Vehicular Edge Networks
Computer simulation platforms offer an alternative solution by emulating complex systems in
a controlled manner. However, existing Edge Computing (EC) simulators, as well as general …
a controlled manner. However, existing Edge Computing (EC) simulators, as well as general …
An Overview of Deep Learning for Resource Management in mmWave-NOMA
Millimeter-wave (mmWave) frequencies ranging from 30 to 300 GHz offer vast bandwidth
and high data transmission rates, making them ideal for high-throughput applications and …
and high data transmission rates, making them ideal for high-throughput applications and …
Mean-field reinforcement learning for decentralized task offloading in vehicular edge computing
Abstract Vehicular Edge Computing (VEC) is a promising paradigm for providing low-latency
and high-reliability services in the Internet of Vehicles (IoV). The increasing number of …
and high-reliability services in the Internet of Vehicles (IoV). The increasing number of …
UAV-assisted dependency-aware computation offloading in device–edge–cloud collaborative computing based on improved actor–critic DRL
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has become a
popular research topic, addressing challenges posed by the pressure of cloud computing …
popular research topic, addressing challenges posed by the pressure of cloud computing …
Resource Allocation for Stable LLM Training in Mobile Edge Computing
As mobile devices increasingly become focal points for advanced applications, edge
computing presents a viable solution to their inherent computational limitations, particularly …
computing presents a viable solution to their inherent computational limitations, particularly …
Task Offloading and Resource Allocation in an RIS-assisted NOMA-based Vehicular Edge Computing
With the rise of intelligent transportation (ITS), autonomous cars, and on-the-road
entertainment and computation, vehicular edge computing (VEC) has become a primary …
entertainment and computation, vehicular edge computing (VEC) has become a primary …