GDTN: Genome-Based Delay Tolerant Network Formation in Heterogeneous 5G Using Inter-UA Collaboration.

Area of Science:

• Computer Science
• Network Engineering
• Telecommunications

Background:

• Existing 3G/4G networks struggle with high data rate and coverage demands from sophisticated users.
• Heterogeneous 5G networks offer solutions but face challenges like node failures and transmission halts.
• Previous attempts to integrate 5G with ad hoc networks still require continuous connectivity.

Purpose of the Study:

• To propose a novel network formation model for robust and delay-tolerant transmissions.
• To address limitations of existing heterogeneous 5G networks, ensuring continuous connectivity.
• To leverage Unmanned Aircraft (UA) and genomic principles for enhanced network performance.

Main Methods:

• Developed a novel network model using nodes from different networks maneuvered by Unmanned Aircraft (UA).
• Integrated genomic features to create a delay-tolerant network compatible with existing models.
• Evaluated the model's performance using real-time testbed and simulation environments.

Main Results:

• The proposed network demonstrates efficient data delivery.
• Achieved lower overheads and reduced transmission delays compared to existing approaches.
• Exhibited a high convergence rate, indicating robust network formation.

Conclusions:

• The novel genome-inspired, UA-maneuvered network model provides continuous and robust connectivity.
• This approach effectively resolves data rate and coverage challenges in 5G environments.
• The model offers significant improvements in efficiency, delay, and convergence for delay-tolerant networks.