AVE: Autonomous Vehicular Edge Computing Framework with ACO-Based Scheduling

By Jingyun Feng, Zhi Liu, Celimuge Wu, Yusheng Ji. IEEE Transactions on Vehicular Technology, 2017

299 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues (2020, methodology): “The concept of autonomous vehicular edge computing is proposed in [99] to utilize the computational resources of vehicles in vicinity.”
Computation Resource Allocation and Task Assignment Optimization in Vehicular Fog Computing: A Contract-Matching Approach (2019, background): “Feng et al. proposed a novel framework named autonomous vehicular edge (AVE) to increase the computation capabilities of vehicles in a decentralized manner [14].”
Computation Offloading Toward Edge Computing (2019, background): “LAVEA is also a typical three-tier mobile-edge-cloud architecture.”
Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, methodology): “Using EC can effectively make up for the limitations of cloud computing [148].”
Edge of Things: The Big Picture on the Integration of Edge, IoT and the Cloud in a Distributed Computing Environment (2018, background): “In [36] and [37], the researchers selected an EC-based architecture to address various network-related issues in vehicular technology.”
Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems (2019, methodology): “Therefore, ALTO is of lower complexity than the existing algorithm in [23].”