Using Trust and “Utility” for Group Formation in the Cloud of Things

Fortino, Giancarlo; Fotia, Lidia; Messina, Fabrizio; Rosaci, Domenico; Sarné, Giuseppe M. L.

doi:10.1007/978-3-030-34914-1_11

Cited by 1 publication

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors in [46] propose Trust-based Optimum Neighbor Selection (TONS), an optimized algorithm for blockchain networks in IoT environments, addressing the challenge of unreliable or malicious nodes. TONS employs a trust and reputation model to evaluate node reliability, ensuring miners communicate with the most trustworthy neighbors.…”

Section: Vu and Tewarimentioning

confidence: 99%

An Efficient Blockchain Neighbor Selection Framework Based on Agglomerative Clustering

Mohamed,

Elkhouly,

El-Aziz

et al. 2024

IJACSA

View full text Add to dashboard Cite

Blockchain-based decentralized applications have garnered significant attention and have been widely deployed in recent years. However, blockchain technology faces several challenges, such as limited transaction throughput, large blockchain sizes, scalability, and consensus protocol limitations. This paper introduces an efficient framework to accelerate broadcast efficiency and enhance the blockchain system's throughput by reducing block propagation time. It addresses these concerns by proposing a dynamic and optimized Blockchain Neighbor Selection Framework (BNSF) based on agglomerative clustering. The main idea behind the BNSF is to divide the network into clusters and select a leader node for each cluster. Each leader node resolves the Minimum Spanning Tree (MST) problem for its cluster in parallel. Once these individual MSTs are connected, they form a comprehensive MST for the entire network, where nodes obtain optimal neighbors to facilitate the process of block propagation. The evaluation of BNSF showed superior performance compared to neighbor selection solutions such as Dynamic Optimized Neighbor Selection Algorithm (DONS), Random Neighbor Selection (RNS), and Neighbor Selection based on Round Trip Time (RTT-NS). Furthermore, BNSF significantly reduced the block propagation time, surpassing DONS, RTT-NS, and RNS by 51.14%, 99.16%, and 99.95%, respectively. The BNSF framework also achieved an average MST calculation time of 27.92% lower than the DONS algorithm.

show abstract

Section: Vu and Tewarimentioning

confidence: 99%