SoftDrone: Softwarized 5G assisted drone networks for dynamic resource sharing using machine learning techniques. (July 2022)
- Record Type:
- Journal Article
- Title:
- SoftDrone: Softwarized 5G assisted drone networks for dynamic resource sharing using machine learning techniques. (July 2022)
- Main Title:
- SoftDrone: Softwarized 5G assisted drone networks for dynamic resource sharing using machine learning techniques
- Authors:
- Basu, Deborsi
Kal, Soumyadeep
Ghosh, Uttam
Datta, Raja - Abstract:
- Abstract: The concept of a programmable network instantiates dedicated network slices for Unmanned Aerial Vehicle (UAV)-based on-demand communication layers which improve the efficiency of overall network performance. With the increasing random service demands, network resource allocation, retention, and release have become serious networking challenges. Often existing methods consider dedicated resource allocations which result in poor resource utilization as well as service quality. Though Machine Learning (ML) techniques are being used for better performance, limited energy constraints and complexity in resource cycle management become a critical matter of fact again. To resolve these issues, we propose service-specific learning models on VNF (Virtual Network Function) data that are running on shared network slices. The results show an average reduction of 35% error from state-of-the-art techniques. This improved performance can further reduce chances of over or under allocation of resources which could lead to severe service denials to time-critical applications in the areas of disaster management, e-healthcare applications, etc. Graphical abstract: Highlights: Network Slicing enables dynamic resource sharing within 5G networks. Limited on-air support provokes UAV-networks to get an intelligent resource sharing system. Dynamic allocation of virtual network functions smartly satisfies the demand requirements. High accuracy in service-specific function selections improvesAbstract: The concept of a programmable network instantiates dedicated network slices for Unmanned Aerial Vehicle (UAV)-based on-demand communication layers which improve the efficiency of overall network performance. With the increasing random service demands, network resource allocation, retention, and release have become serious networking challenges. Often existing methods consider dedicated resource allocations which result in poor resource utilization as well as service quality. Though Machine Learning (ML) techniques are being used for better performance, limited energy constraints and complexity in resource cycle management become a critical matter of fact again. To resolve these issues, we propose service-specific learning models on VNF (Virtual Network Function) data that are running on shared network slices. The results show an average reduction of 35% error from state-of-the-art techniques. This improved performance can further reduce chances of over or under allocation of resources which could lead to severe service denials to time-critical applications in the areas of disaster management, e-healthcare applications, etc. Graphical abstract: Highlights: Network Slicing enables dynamic resource sharing within 5G networks. Limited on-air support provokes UAV-networks to get an intelligent resource sharing system. Dynamic allocation of virtual network functions smartly satisfies the demand requirements. High accuracy in service-specific function selections improves efficiency. The slice coordination technique for UAV networks has many critical applications. … (more)
- Is Part Of:
- Computers & electrical engineering. Volume 101(2022)
- Journal:
- Computers & electrical engineering
- Issue:
- Volume 101(2022)
- Issue Display:
- Volume 101, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 101
- Issue:
- 2022
- Issue Sort Value:
- 2022-0101-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-07
- Subjects:
- Network slicing -- VNF -- SDN -- 5G -- NFV -- Slice coordination -- UAV
Computer engineering -- Periodicals
Electrical engineering -- Periodicals
Electrical engineering -- Data processing -- Periodicals
Ordinateurs -- Conception et construction -- Périodiques
Électrotechnique -- Périodiques
Électrotechnique -- Informatique -- Périodiques
Computer engineering
Electrical engineering
Electrical engineering -- Data processing
Periodicals
Electronic journals
621.302854 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00457906/ ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.compeleceng.2022.107962 ↗
- Languages:
- English
- ISSNs:
- 0045-7906
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.680000
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British Library HMNTS - ELD Digital store - Ingest File:
- 22350.xml