A Novel Weighted Clustering Algorithm Supported by a Distributed Architecture for D2D Enabled Content-Centric...

Area of Science:

• Computer Science
• Telecommunications Engineering
• Network Architecture

Background:

• Next-generation cellular systems require efficient content distribution strategies.
• Device-to-Device (D2D) clustered networks offer a promising approach to reduce cellular network load.
• Existing content distribution methods face challenges in efficiency and scalability.

Purpose of the Study:

• To propose a distributed architecture for efficient content delivery using Content-Centric Networking and Network Virtualization.
• To develop a weighted multifactor clustering algorithm for grouping Device-to-Device User Equipment (DUEs) with shared interests.
• To evaluate the performance of the proposed clustering algorithm and analyze its flexibility.

Main Methods:

• Utilized Content-Centric Networking and Network Virtualization to design a distributed architecture.
• Developed a weighted multifactor clustering algorithm for user-level content distribution in D2D networks.
• Conducted a comprehensive simulation study to assess performance metrics and compare with existing algorithms.

Main Results:

• The proposed weighted multifactor clustering algorithm demonstrates superior performance in energy efficiency, area spectral efficiency, and throughput.
• The algorithm's flexibility allows for a tradeoff between fairness and other performance parameters.
• The study analyzed the impact of the number of clusters on overall system performance.

Conclusions:

• The proposed distributed architecture and clustering algorithm provide an efficient solution for content delivery in D2D networks.
• The weighted multifactor clustering algorithm offers enhanced flexibility and performance compared to classical and state-of-the-art methods.
• This approach effectively alleviates the burden on cellular networks while improving user experience.