Performance analysis and comparison of PoW, PoS and DAG based blockchains

Cao, Bin; Zhang, Zhenghui; Feng, Daquan; Zhang, Shengli; Zhang, Lei; Peng, Mugen; Li, Yun

doi:10.1016/j.dcan.2019.12.001

Cited by 188 publications

(87 citation statements)

References 6 publications

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“…Additionally, the average block confirmation time, in PoW-based and PoA-based BCs, seems to be directly proportional to the BC network size, which complies with the results recently presented in [86]. Comparatively, an average block confirmation time in a PoS-based BC seems unaffected by the network size, which complies with the expectations recently presented in [87].…”

Section: Case Studiessupporting

confidence: 90%

FoBSim: An extensible open-source simulation tool for integrated Fog-Blockchain systems

Baniata¹,

Kertész²

2020

Preprint

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A lot of hard work and years of research are still needed for developing successful Blockchain (BC) applications. Although it is not yet standardized, BC technology was proven as to be an enhancement factor for security, decentralization, and reliability, leading to be successfully implemented in cryptocurrency industries. Fog computing (FC) is one of the recently emerged paradigms that needs to be improved to serve Internet of Things (IoT) environments of the future. As hundreds of projects, ideas, and systems were proposed, one can find a great R\&D potential for integrating BC and FC technologies. Examples of organizations contributing to the R\&D of these two technologies, and their integration, include Linux, IBM, Google, Microsoft, and others. To validate an integrated Fog-Blockchain protocol or method implementation, before the deployment phase, a suitable and accurate simulation environment is needed. Such validation should save a great deal of costs and efforts on researchers and companies adopting this integration. Current available simulation environments facilitate Fog simulation, or BC simulation, but not both. In this paper, we introduce a Fog-Blockchain simulator, namely FoBSim, with the main goal is to ease the experimentation and validation of integrated Fog-Blockchain approaches. According to our proposed workflow of simulation, we implement different Consensus Algorithms (CA), different deployment options of the BC in the FC architecture, and different functionalities of the BC in the simulation. Furthermore, technical details and algorithms on the simulated integration are provided. We validate FoBSim by describing the technologies used within FoBSim, highlighting FoBSim novelty compared to the state-of-the-art, discussing the event validity in FoBSim, and providing a clear walk-through validation. Finally, we simulate two case studies, then present and analyze the obtained results.

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Section: Case Studiessupporting

confidence: 90%

FoBSim: An extensible open-source simulation tool for integrated Fog-Blockchain systems

Baniata¹,

Kertész²

2020

Preprint

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“…On the other hand, PoS is an alternative to the PoW consensus protocol [45][46][47][48]. It does not rely on the mining process, but performs a validating one.…”

Section: Blockchain Technologymentioning

confidence: 99%

“…Two of the largest challenges facing BCT are its inability to handle a large volume of transactions simultaneously and its high transaction fees. In order to resolve these drawbacks, a more recent solution is to use a directed acyclic graph (DAG) to implement a distributed ledger [45,46] instead of the BCT linear structure as shown in Figure 4. In mathematics, DAG is a graph that travels in one direction without cycles connecting to other edges.…”

Section: Blockchain Technologymentioning

confidence: 99%

A Survey on the Usage of Blockchain Technology for Cyber-Threats in the Context of Industry 4.0

et al. 2020

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A systematic review of the literature is presented related to the usage of blockchain technology (BCT) for cyber-threats in the context of Industry 4.0. BCT plays a crucial role in creating smart factories and it is recognized as a core technology that triggers a disruptive revolution in Industry 4.0. Beyond security, authentication, asset tracking and the exchange of smart contracts, BCTs allow terminals to exchange information according to mutually agreed rules within a secured manner. Consequently, BCT can play a crucial role in industrial sustainability by preserving the assets and the environment and by enhancing the quality of life of citizens. In this work, a classification of the most important cyber-attacks that occurred in the last decade in Industry 4.0 is proposed based on four classes. The latter classes cover scanning, local to remote, power of root and denial of service (DoS). BCT is also defined and various types belong to BCT are introduced and highlighted. Likewise, BCT protocols and implementations are discussed as well. BCT implementation includes linear structure and directed acyclic graph (DAG) technology. Then, a comparative study of the most relevant works based on BCT in Industry 4.0 is conducted in terms of confidentiality, integrity, availability, privacy and multifactor authentication features. Our review shows that the integration of BCT in industry can ensure data confidentiality and integrity and should be enforced to preserve data availability and privacy. Future research directions towards enforcing BCT in the industrial field by considering machine learning, 5G/6G mobile systems and new emergent technologies are presented.

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“…However, DAG consensuses use some effective algorithms (e.g., Markov Chain Monte Carlo algorithm [13] and virtual voting algorithm [15]) to address double-spending problem and allow new arrival transactions access the ledger in a forking topology. As a result, the transaction throughput in DAG consensus process will not be limited anymore [16]. Moreover, unlike the single chain design in PoW or PoS, the data stored in DAG-based ledgers is protected by massive forking blocks, thus, the average resource consumption on each node can be very low.…”

Section: Introductionmentioning

confidence: 99%

Direct Acyclic Graph-Based Ledger for Internet of Things: Performance and Security Analysis

Li¹,

Cao²,

Peng³

et al. 2020

IEEE/ACM Trans. Networking

Self Cite

131

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Direct Acyclic Graph (DAG)-based ledger and the corresponding consensus algorithm has been identified as a promising technology for Internet of Things (IoT). Compared with Proof-of-Work (PoW) and Proof-of-Stake (PoS) that have been widely used in blockchain, the consensus mechanism designed on DAG structure (simply called as DAG consensus) can overcome some shortcomings such as high resource consumption, high transaction fee, low transaction throughput and long confirmation delay. However, the theoretic analysis on the DAG consensus is an untapped venue to be explored. To this end, based on one of the most typical DAG consensuses, Tangle, we investigate the impact of network load on the performance and security of the DAG-based ledger. Considering unsteady network load, we first propose a Markov chain model to capture the behavior of DAG consensus process under dynamic load conditions. The key performance metrics, i.e., cumulative weight and confirmation delay are analysed based on the proposed model. Then, we leverage a stochastic model to analyse the probability of a successful double-spending attack in different network load regimes. The results can provide an insightful understanding of DAG consensus process, e.g., how the network load affects the confirmation delay and the probability of a successful attack. Meanwhile, we also demonstrate the trade-off between security level and confirmation delay, which can act as a guidance for practical deployment of DAG-based ledgers.

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Performance analysis and comparison of PoW, PoS and DAG based blockchains

Cited by 188 publications

References 6 publications

FoBSim: An extensible open-source simulation tool for integrated Fog-Blockchain systems

FoBSim: An extensible open-source simulation tool for integrated Fog-Blockchain systems

A Survey on the Usage of Blockchain Technology for Cyber-Threats in the Context of Industry 4.0

Direct Acyclic Graph-Based Ledger for Internet of Things: Performance and Security Analysis

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