Advanced DAG-Based Ranking (ADR) Protocol for Blockchain Scalability
By: Tayyaba Noreen, Qiufen Xia, Muhammad Zeeshan Haider
Potential Business Impact:
Makes online money systems faster and safer.
In the past decade, blockchain has emerged as a promising solution for building secure distributed ledgers and has attracted significant attention. However, current blockchain systems suffer from limited throughput, poor scalability, and high latency. Due to limitations in consensus mechanisms, especially in managing node identities, blockchain is often considered unsuitable for applications such as the Internet of Things (IoT). This paper proposes the Advanced DAG-based Ranking (ADR) protocol to enhance blockchain scalability and throughput. ADR employs a directed acyclic graph (DAG) structure where nodes are positioned based on their rankings. Unlike traditional chains, ADR allows honest nodes to write blocks and verify transactions using a DAG-based topology. The protocol follows a three-step approach to secure the network against double-spending and enhance performance. First, it verifies nodes using their public and private keys before granting entry. Second, it builds an advanced DAG ledger enabling block production and transaction validation. Third, a ranking algorithm filters out malicious nodes, ranks the remaining nodes based on performance, and arranges them topologically. This process increases throughput and ensures robust scalability. We evaluated ADR on Amazon EC2 clusters with over 100 nodes, including scenarios with injected malicious nodes. Simulation results demonstrate that ADR significantly improves transaction throughput and network liveness compared to existing DAG-based blockchains such as IOTA and ByteBall, making it well-suited for IoT applications.
Similar Papers
Utilizing Transaction Prioritization to Enhance Confirmation Speed in the IOTA Network
Cryptography and Security
Lets important messages go through faster.
DAG-AFL:Directed Acyclic Graph-based Asynchronous Federated Learning
Machine Learning (CS)
Makes learning faster and better for many computers.
DAGs for the Masses
Distributed, Parallel, and Cluster Computing
Makes computer networks work faster and bigger.