SoftDrone: Softwarized 5G assisted drone networks for dynamic resource sharing using machine learning techniques

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.

Automated summary

This research proposes a programmable network solution that improves the efficiency of overall network performance by using dedicated network slices for communication. By using service-specific learning models on shared network slices, the error in resource allocation is reduced, leading to fewer service denials for critical applications.