Year: 2024
Type: article
Abstract: With the continuous development of mobile communication technology, edge intelligence has received widespread attention from academia. However, when enabling edge intelligence in Unmanned Aerial Vehicle (UAV) networks where drones serve as edge devices, the problem of insufficient computing power often arises due to limited storage and computing resources. In order to solve the problem of insufficient UAV computing power, this paper proposes a distributed cloud-edge collaborative optimization algorithm (DCECOA). The core idea of the DCECOA is to make full use of the local data of edge devices (i.e., UAVs) to optimize the neural network model more efficiently and achieve model volume compression. Compared with the traditional Taylor evaluation criterion, this algorithm consumes less resources on the communication uplink. The neural network model compressed by the proposed optimization algorithm can achieve higher performance under the same compression rate.
Source: Electronics
Authors: Jian Yang, Jinyu Tao, Cheng Wang, Qinghai Yang
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
OpenAlex ID: W4402762278