부산대학교 도서관

The Gossip algorithm is an important communication protocol that can be used in various fields, including computer networks and distributed systems. This letter proposes an integrated approach that combines Constraint-Driven Type-2 Fuzzy C-Means (T2FCM) clustering and the step-wise gossip algorithm to overcome the challenges of communication congestion in peer-to-peer distributed networks. Traditional clustering algorithms often lack accuracy and relevance in organizing and analyzing large volumes of data in nodes. To address this, the integration of T2FCM clustering, which incorporates domain-specific constraints, is introduced to improve clustering accuracy. Additionally, the transition state theory based step-wise gossip algorithm is utilized to adjust information transfer rates, enhancing scalability, fault tolerance, and resource utilization while reducing communication congestion. Simulation results demonstrate the effectiveness of the proposed approach in achieving low latency access. By leveraging accurate clustering results and adjusting information transfer rates, the overall performance of fusion transmission in peer-to-peer networks is significantly improved.