SIM-P-A Simplified Consensus Protocol Simulator: Applications to Proof of Reputation-X and Proof of Contribution

被引:0
|
作者
Oyinloye, Damilare Peter [1 ,2 ]
Teh, Je Sen [1 ]
Jamil, Norziana [3 ]
Teh, Jiashen [1 ]
机构
[1] Univ Sains Malaysia, Sch Comp Sci, Minden 11800, Malaysia
[2] Kwara State Univ, Dept Comp Sci, Ilorin 23401, Nigeria
[3] Univ Tenaga Nas, Inst Informat & Comp Energy, Kajang 43000, Malaysia
关键词
Consensus protocol; Protocols; Bitcoin; Peer-to-peer computing; Adaptation models; Stochastic processes; Internet of Things; Blockchain; consensus protocol; proof of contribution; proof of reputation; Proof of Work (PoW); simulator; BLOCKCHAIN CONSENSUS;
D O I
10.1109/JIOT.2022.3221916
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Blockchain is a distributed ledger in which participating users with varying levels of trust agree on the ledger's content using a consensus mechanism called consensus protocols. There has been a rising interest in the design of consensus protocols since they play a central role in blockchain architecture. However, many recently proposed consensus protocols lack experimental verification which hampers the possible deployment of these protocols in real-world blockchain networks. In this article, we propose a simple tool called simplified consensus protocol simulator (SIM-P) that can accurately simulate the behavior of these consensus protocols with ease. It is an agent-based stochastic simulator that relies on the sequential Monte Carlo method to model how block publishers are selected. The likelihood of each node (represented as agents) being selected as a block publisher is represented by independent trials in a binomial experiment. We provide a base SIM-P model that simulates Proof of Work (PoW) for benchmarking purposes. The PoW model also serves as the basic structure of the simulator that can be adapted to other protocols. We showcase the flexibility of SIM-P by proposing two additional simulation models for Proof of Reputation-X and Proof of Contribution, both of which lack experimental verification in their original design specifications. We show how the simulator can be used to produce vital metrics, such as throughput, resistance against the 51% attack, and energy consumption. We verify the accuracy of SIM-P by comparing PoW's simulated results with theoretical estimates and historical Bitcoin data.
引用
收藏
页码:5083 / 5094
页数:12
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