Zhao, Zhongliang; Pacheco, Lucas; Santos, Hugo; Liu, Minghui; Di Maio, Antonio; Rosário, Denis; Cerqueira, Eduardo; Braun, Torsten; Cao, Xianbin (2021). Predictive UAV Base Station Deployment and Service Offloading with Distributed Edge Learning. IEEE Transactions on Network and Service Management, 18(4), pp. 3955-3972. IEEE 10.1109/TNSM.2021.3123216
|
Text
Predictive_UAV_Base_Station_Deployment_and_Service_Offloading_with_Distributed_Edge_Learning.pdf - Accepted Version Available under License Publisher holds Copyright. Download (1MB) | Preview |
In modern networks, edge computing will be responsible for processing and learning from the critical network-and user-generated data, such as wireless link usage, mobility information, application requests, and many others. The presence of Artificial Intelligence-based (AI) applications at the edge of the network will enable the network to predict necessary user behavior and its impact on network infrastructure, such as base station overloading. One of the main strategies for offloading users and base stations is to deploy UAV base stations, or flying base stations, which can dynamically provide service and connectivity. In this article, we introduce a framework for distributed learning over (), which manages data applications in a fully distributed setting across edge servers, thus reducing the cost of collecting user information in a centralized server. We couple the proposed distributed learning with a novel similarity metric for user trajectories, which can aggregate neural network models with similar costs as other model aggregation techniques. However, the aggregation technique can achieve much higher accuracy. Furthermore, we apply the proposed distributed learning scheme to manage and deploy flying base stations to areas that experience high demand or poor user connectivity, thus optimizing connectivity in terms of user satisfaction, delay, and network throughput.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
08 Faculty of Science > Institute of Computer Science (INF) 08 Faculty of Science > Institute of Computer Science (INF) > Communication and Distributed Systems (CDS) |
UniBE Contributor: |
De Sousa Pacheco, Lucas, Melo dos Santos, Hugo Leonardo, Di Maio, Antonio, Braun, Torsten |
Subjects: |
000 Computer science, knowledge & systems |
ISSN: |
1932-4537 |
Publisher: |
IEEE |
Funders: |
[UNSPECIFIED] Beihang Zhuobai Program ; [UNSPECIFIED] 10.13039/501100001809-National Natural Science Foundation of China ; [UNSPECIFIED] CNPqCAPES |
Language: |
English |
Submitter: |
Dimitrios Xenakis |
Date Deposited: |
04 Nov 2021 08:31 |
Last Modified: |
02 Mar 2023 23:35 |
Publisher DOI: |
10.1109/TNSM.2021.3123216 |
Uncontrolled Keywords: |
Distributed machine learning; trajectory prediction; unmanned aerial vehicle; flying base station deployment; mobility management |
BORIS DOI: |
10.48350/160420 |
URI: |
https://boris.unibe.ch/id/eprint/160420 |
Actions (login required)
 |
Edit item |