Pith. sign in

REVIEW 3 major objections 6 minor 36 references

Efficient Byzantine Consensus MechanismBased on Reputation in IoT Blockchain

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read EBRC, a reputation-based Byzantine consensus protocol, claims to reduce IoT blockchain communication overhead from O(n^2) to O(n) while improving latency and throughput.

desk verdict A Byzantine master can break the two-phase commit, and Eq. (2) rewards misbehavior; neither is a minor fix. read the letter →

arxiv 2508.01856 v1 pith:LNLLHCTE submitted 2025-08-03 cs.DC cs.CRcs.DBcs.SE

Xu Yuan , Fang Luo , Muhammad Zeeshan Haider , Zhikui Chen , Yucheng Li This is my paper · ORCID
classification cs.DCcs.CRcs.DBcs.SE MSC 68M14
keywords blockchainByzantinefaulttolerancePBFTreputationverifiablerandomfunctionIoTconsensusscalability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes EBRC, a Byzantine fault-tolerant consensus protocol for IoT blockchains that combines reputation-based node scoring with verifiable random-function (VRF) election and a dynamic join/exit protocol. The central claim is that by pre-selecting trustworthy nodes and by having slave nodes send Commit messages only to the master, the protocol lowers consensus delay, raises transaction throughput, reduces verification costs, and scales better than classic PBFT. The authors report experiments on a Sawtooth-based prototype with up to 40 nodes showing lower latency and higher throughput than PBFT, including under Byzantine faults. If correct, EBRC offers a path toward practical Byzantine consensus on resource-constrained IoT devices and toward applications such as Internet courts that combine IoT and blockchain.

What carries the argument

The load-bearing mechanism is the modified two-phase commit: in EBRC's commit phase, each slave node sends its signed Commit message only to the master node, which is responsible for collecting $2f+1$ identical Commit messages before finalizing. The VRF-based election and reputation scoring (with weights $[0.1, 0.3, 0.3, 0.2, 0.1]$ on margin ratio, incomplete rate, evil rate, activity rate, and transaction magnitude factor) determine which nodes are even eligible to be part of the consensus committee, and the dynamic join/exit protocol keeps the committee size above $3f+1$.

What would settle it

Run EBRC with a Byzantine master that receives Commit messages from honest slaves but only forwards a subset to the client, or that reorders them, and show that a block is committed although fewer than $2f+1$ distinct honest slaves actually committed; alternatively, run a formal model checker on the two-phase protocol and find a reachable safety violation when the master equivocates.

Watch

Extended reading notes

Core claim

The paper claims that a reputation-aware, VRF-elected committee can run a two-phase PBFT-like consensus with substantially lower communication complexity. The key change is that in the commit phase, slave nodes send their Commit messages only to the master node rather than broadcasting to all peers; the master aggregates $2f+1$ identical Commit messages, then replies to the client and writes the block. Combined with reputation-based election (nodes with low completion rates, high error rates, or low activity are unlikely to be chosen) and a dynamic join/exit protocol that replaces failed nodes with candidates from the top-85% reputation bracket, EBRC is claimed to maintain fault tolerance while reducing consensus delay, increasing throughput, and lowering communication cost from $O(n^2)$ to $O(n)$ in stable networks.

Load-bearing premise

The central assumption is that having slave nodes send Commit messages only to the master, and letting the master collect $2f+1$ of them, preserves the same Byzantine fault tolerance as PBFT's all-to-all broadcast commit phase.

Editorial extensions

If this is right

  • If EBRC's claims hold, IoT blockchain deployments can support more nodes with fixed bandwidth, since per-round messages grow linearly rather than quadratically in committee size.
  • Reputation-based election implies that nodes with a history of failures or faults are systematically excluded, reducing the chance of Byzantine nodes entering the consensus set.
  • The dynamic join/exit protocol makes it practical to replace failed or leaving nodes during an epoch, keeping the network operational without restarting consensus.
  • The reduction in verification and communication cost could make Byzantine fault tolerance feasible on devices with constrained bandwidth and battery.
  • The comparison table suggests EBRC is the only algorithm among PBFT, DBFT, EPBFT, Trust-PBFT, T-PBFT, and Tu that combines reliability evaluation, election fairness, punishment, $O(m^2)$ complexity with $m < n$, and dynamic network suitability.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the master in EBRC can equivocate or selectively forward Commit messages, honest replicas may not be able to verify that $2f+1$ distinct nodes actually committed, so the paper's safety claim rests on an unproven assumption about the aggregated-commit protocol.
  • The reputation inputs themselves depend on honest reporting by other nodes; a sybil or colluding group could inflate or deflate reputation values, so the security guarantee ultimately rests on reporting honesty rather than on cryptographic enforcement.
  • A testable extension would be to compare EBRC's committee-election fairness against a VRF-only baseline without a reputation gate, in order to isolate the contribution of reputation scoring.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper proposes EBRC, a PBFT-style consensus mechanism for IoT blockchains that combines reputation-based node evaluation, VRF-based random election, and a dynamic join/exit protocol (DJEP). The central claim, stated in the abstract and Section 3.7, is that EBRC achieves lower consensus delay, higher throughput, improved security, and lower verification costs than PBFT, with communication complexity reduced from O(n^2) to O(n). The consensus protocol is described in Section 2.4 as a two-phase prepare/commit process in which slave nodes send Commit messages only to the master; the reputation model in Section 2.2.1 assigns each node a weighted score; and Section 3 reports experiments comparing EBRC with PBFT on latency, throughput, communication times, and election fairness.

Significance. If the design were sound, the combination of reputation-based screening, VRF election, and reduced communication would be a useful contribution for resource-constrained IoT blockchain deployments. The paper includes a prototype and a reasonably extensive experimental comparison, and the VRF election fairness experiment is a good idea in principle. However, two load-bearing flaws undermine the central claims: the reputation formula in Eq. (2) rewards misbehavior instead of penalizing it, and the two-phase commit protocol with master-only aggregation does not preserve PBFT safety under Byzantine faults. Because these flaws invalidate the security and complexity claims, the significance of the proposed mechanism is not established by the manuscript as written.

major comments (3)
  1. [Section 2.2.1, Eq. (2)] The reputation formula R_i = 0.1δ_i + 0.3τ_i + 0.3ψ_i + 0.2φ_i + 0.1ρ_i adds the incomplete rate τ_i and the evil rate ψ_i with positive weights. Since higher τ_i and higher ψ_i denote worse behavior (more failed consensuses and more reported error messages), a node that misbehaves more receives a higher reputation score. This directly contradicts the stated design intent in the same section, where the weights are described as giving the most weight to non-completion and evil rates as a penalty, and it undermines the election mechanism in Table 3, which uses high reputation as a prerequisite for consensus participation. As written, the protocol preferentially elects misbehaving nodes, so the claimed security benefit from reputation screening is not supported.
  2. [Section 2.4, steps (v)-(vi); Figure 3] The protocol specifies that slave nodes send Commit messages only to the master node, and step (vi) states that 'all consensus nodes receive 2f+1 identical acknowledgment messages.' No relay or certificate-broadcast step is specified that would allow replicas other than the master to observe the 2f+1 quorum. A Byzantine master can selectively forward or withhold Commit messages, causing honest replicas to disagree on whether the quorum was reached, which violates safety. The O(n) communication complexity claimed in Section 3.7 is obtained by centralizing quorum validation in the master, which is exactly the entity that a Byzantine fault-tolerant protocol must not trust. The paper does not provide a view-change or certificate-distribution sub-protocol that would repair this divergence.
  3. [Section 2.5 (DJEP)] The dynamic join/exit protocol replaces a failed consensus node with a candidate node without specifying any state transfer or view change. A replacement joining mid-epoch does not have the prepare/commit history or pending-request state of the current consensus round, so it cannot safely participate in an ongoing consensus. The paper does not prove liveness or safety under repeated failures, and the delay measurements in Section 3.6 do not address correctness of the replacement mechanism.
minor comments (6)
  1. [Section 2.2.1] The activity rate is written as φ_i = (t_i + t_i)/T_i; based on the definitions of t_i (offline level) and l_i (network latency level), this is likely a typo for (t_i + l_i)/T_i.
  2. [Section 2.3] The text states that with 10 nodes and ω = 0.4, the probability of a node not being selected is 0.01%; for a uniform VRF output, the probability that none of 10 nodes is selected is 0.6^10 ≈ 0.6%, so the quoted figure is inconsistent with the stated model.
  3. [Section 2.4, step (v)] The Commit message format in Eq. (10) includes 'sn', which is never defined, and the signature is attributed to 'primary node i' rather than to the sending replica.
  4. [Section 3.3, Eq. (18)] The definitions of T_sum and Δt appear to be swapped: the formula is T_tps = T_sum/Δt, but the text says T_tps stands for throughput, T_sum stands for block time, and Δt stands for the total number of transactions during that time.
  5. [Section 3.5] The narrative reports that the malicious node election count is 'two times less' than normal nodes in one place and 'fourfold' lower in the next; these statements are inconsistent.
  6. [Algorithm 2] The condition 'if Count(CONS ≥ M)' is missing a closing parenthesis, and the subsequent assignment of 'top 85%' as consensus nodes conflicts with Section 2.3, which specifies top 50% consensus nodes and the next 35% as candidates.

Circularity Check

1 steps flagged · score 2.0 of 10

Minor self-definitional validation in Section 3.5's election-practicability experiment; the central EBRC performance claims are structural and benchmarked against PBFT, so no significant circularity.

  1. self definitional [Section 3.5 (Election Performance Analysis, Figure 11), resting on Eq. (2) in Section 2.2.1 and the election cutoff in Table 3 / Algorithm 2 (Section 2.3).]
    "we adjusted their reputation value by increasing the error rate of nodes with odd ids. ... It can be seen that when the error rate increases, the number of times that the node is elected as a consensus node is significantly reduced, which is two times less than the number of ordinary node elections, which shows that if the node conducts suspicious actions, our algorithm can reduce the node's reputation value and the probability of a successful election."

    The demonstrated 'practicability' result is entailed by the paper's own definitions. Eq. (2) defines R_i = 0.1δ_i + 0.3τ_i + 0.3ψ_i + 0.2φ_i + 0.1ρ_i, giving the evil rate ψ_i the largest weight (0.3), and Table 3 / Algorithm 2 restrict election authority to nodes ranking in the top 85% of reputation (consensus nodes in the top 50%, master in the top 25%). Increasing the error rate therefore lowers ψ_i, lowers R_i, and pushes the node below the election cutoff. The experimental observation that such nodes are elected less often cannot fail under these definitions; the experiment re-runs the reputation formula and the cutoff it was designed with, so it validates the design by restating it rather than by independent evidence.

full rationale

The central claims of the paper—lower consensus delay, higher throughput, lower communication overhead, and improved scalability relative to PBFT—are structural and self-contained. The communication reduction follows directly from the protocol's own message pattern in Section 2.4: step (v) has slave nodes send Commit messages only to the master, so the message count is O(n) rather than PBFT's all-to-all O(n^2); Section 3.7's 'reduced from O(n^2) to O(n)' is a count of the paper's own specified messages, not a fitted prediction. The latency and throughput experiments benchmark EBRC against an external PBFT baseline on the same Sawtooth testbed, so those results are not constructed from the paper's own assumptions. The hand-picked reputation weights W = [0.1, 0.3, 0.3, 0.2, 0.1] and the VRF threshold ω = 0.4 are chosen a priori; the experiments do not use these values to predict quantities that were used to choose them, so this is not a fitted-input-called-prediction loop. Self-citation is minimal and not load-bearing: reference [31] (EPBFT, possibly a co-author's prior work) is cited only as related work that lacks reputation assessment, and no uniqueness theorem or prior result from the same authors is invoked to justify the protocol's design. The one genuinely circular element is the Section 3.5 'practicability' experiment, where the finding that error-prone nodes are elected less often is an arithmetic consequence of Eq. (2)'s 0.3 coefficient on the evil rate and the top-85% election cutoff in Table 3; as a validation it reduces to the definitions that produced it. This supports a security-related design choice but is not the basis of the central performance comparison. The safety concern raised by a Byzantine master being able to withhold or selectively forward the 2f+1 commit certificate (Section 2.4 steps (v)-(vi) versus the claim that 'all consensus nodes receive 2f+1 identical acknowledgment messages') is a correctness and fault-tolerance risk, not a circularity, and is therefore not scored here. Overall, the paper is self-contained against an external PBFT benchmark, so the honest finding is minor circularity only.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The protocol adds five free parameters (weights, thresholds, cutoffs, levels, initial values) to tune node election, and relies on at least five unproven assumptions about synchrony, the safety of the two-phase commit, the honesty of reputation inputs, VRF security, and dynamic membership. None of these are validated by independent benchmarks or formal proofs, so the evaluation is a self-consistent demonstration of the chosen design rather than a validated result.

free parameters (5)
  • Reputation weight vector W = [0.1, 0.3, 0.3, 0.2, 0.1]
    Chosen by hand in Section 2.2.1 without data-driven optimization; the weights are said to emphasize non-completion and evil rates, but since those rates appear with positive signs in Eq. (2), higher misbehavior increases reputation.
  • VRF election threshold omega = 0.4
    Set after experimental testing (Section 2.3) to keep the probability of selecting no consensus node low. The stated probability P(0.4)=0.01% does not match (1-0.4)^10 = 0.6%.
  • Reputation ranking cutoffs = Top 25%, top 50%, top 85%, bottom 15%; consensus top 50%, candidates top 50-85%
    Table 3 and Section 2.3(v) define election authority and consensus membership by fixed percentile cutoffs with no stated optimization or sensitivity analysis; the cutoffs directly determine which nodes reach consensus.
  • Time period level scores = Table 2 levels: 10, 8, 6, 4, 2 for offline, latency, and join time
    The integer levels used to compute activity rate phi_i are arbitrary and not justified; changes alter reputation and election outcomes.
  • Initial reputation and growth rate = R=0.5, Y=50%
    New nodes start at R=0.5 and Y=50% (Sections 2.2.1 and 2.2.2). These initial values affect early election probabilities and are not tied to any measurement.
assumptions (5)
  • domain assumption The network is partially synchronous and transactions are provided in a prespecified format.
    Section 2.2 states 'we assume that the network is partially synchronized'. The protocol's safety and liveness depend on a synchrony bound that is not quantified.
  • ad hoc to paper The two-phase prepare/commit protocol with only master-mediated aggregation preserves PBFT safety and liveness.
    Section 2.4(vi) states consensus is reached when 'all consensus nodes receive 2f+1 identical acknowledgment messages', but commit messages are sent only to the master (step (v) and Figure 3). No proof is provided that a faulty master cannot equivocate or suppress commits.
  • domain assumption Reputation inputs such as incomplete count, evil count, offline latency, and transaction sizes are honestly measured and cannot be manipulated by colluding nodes.
    Section 2.2.1 defines evil rate as 'successfully reported' errors; the protocol assumes other nodes report accurately, with no mechanism against collusive false reports or rating spam.
  • standard math VRF provides unpredictable and verifiable randomness, and the seed from the PRNG cannot be biased by the master node.
    Section 2.3 uses VRF [36,37] and a PRNG-generated seed from the previous block hash. Security relies on standard VRF properties plus the assumption that the master node cannot bias the seed beyond the block hash.
  • ad hoc to paper Dynamic membership can be handled by replacing failed nodes with candidate nodes without reconfiguration or state transfer.
    Section 2.5 describes DJEP; it assumes candidate replacement preserves the 3f+1 bound and prior consensus state, but no view-change or state-sync protocol is specified.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Efficient Byzantine Consensus MechanismBased on Reputation in IoT Blockchain." pith.science (2026). https://pith.science/paper/LNLLHCTE

@misc{pith2026250801856,
  author       = {Pith},
  title        = {Pith review of: Efficient Byzantine Consensus MechanismBased on Reputation in IoT Blockchain},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LNLLHCTE}},
  note         = {Machine review of arXiv:2508.01856}
}
read the original abstract

Blockchain technology has advanced rapidly in recent years and is now widely used in a variety of fields. Blockchain appears to be one of the best solutions for managing massive heterogeneous devices while achieving advanced data security and data reputation, particularly in the field of large-scale IoT (Internet of Things) networks. Despite the numerous advantages, there are still challenges while deploying IoT applications on blockchain systems due to the limited storage, power, and computing capability of IoT devices, and some of these problems are caused by the consensus algorithm, which plays a significant role in blockchain systems by ensuring overall system reliability and robustness. Nonetheless, most existing consensus algorithms are prone to poor node reliability, low transaction per second (TPS) rates, and scalability issues. Aiming at some critical problems in the existing consensus algorithms, this paper proposes the Efficient Byzantine Reputation-based Consensus (EBRC) mechanism to resolve the issues raised above. In comparison to traditional algorithms, we reinvented ways to evaluate node reliability and robustness and manage active nodes. Our experiments show that the EBRC algorithm has lower consensus delay, higher throughput, improved security, and lower verification costs. It offers new reference ideas for solving the Internet of Things+blockchain+Internet court construction problem.

Figures

Figures reproduced from arXiv: 2508.01856 by the authors.

Figure 3
Figure 3. In [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

36 extracted references · 36 canonical work pages

  1. [1]

    Blockchain: a game changer for securing IoT data,

    M. Singh, A. Singh, and S. Kim, “Blockchain: a game changer for securing IoT data, ” in 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), pp. 51 –55, Singapore, 2018

    work page 2018

  2. [2]

    Intelligent resource allocation in mobile blockchain for privacy and security transactions: a deep reinforcement learning based approach,

    Z. Ning, S. Sun, X. Wang et al., “Intelligent resource allocation in mobile blockchain for privacy and security transactions: a deep reinforcement learning based approach,” SCIENCE CHINA Information Sciences , vol. 64, no. 6, pp. 1–16, 2020

    work page 2020

  3. [3]

    Internet of Things (IoT) cybersecurity research: a review of current research topics,

    Y. Lu and L. D. Xu, “Internet of Things (IoT) cybersecurity research: a review of current research topics, ” IEEE Internet of Things Journal , vol. 6, no. 2, pp. 2103 –2115, 2019

    work page 2019

  4. [4]

    A hybrid blockchain architecture for privacy-enabled and accountable auctions,

    H. Desai, M. Kantarcioglu, and L. Kagal, “A hybrid blockchain architecture for privacy-enabled and accountable auctions, ” in 2019 IEEE International Conference on Blockchain (Block- chain), pp. 34 –43, Atlanta, GA, USA, 2019

    work page 2019

  5. [5]

    Imitation learning enabled task scheduling for online vehicular edge computing,

    X. Wang, Z. Ning, S. Guo, and L. Wang, “Imitation learning enabled task scheduling for online vehicular edge computing, ” IEEE Transactions on Mobile Computing, 2020

    work page 2020

  6. [6]

    Access control for electronic health records with hybrid blockchain-edge archi- tecture,

    H. Guo, W. Li, M. Nejad, and C.-C. Shen, “Access control for electronic health records with hybrid blockchain-edge archi- tecture,” in 2019 IEEE International Conference on Blockchain (Blockchain), pp. 44 –51, Atlanta, GA, USA, 2019

    work page 2019

  7. [7]

    HaBiTs: blockchain-based telesurgery framework for healthcare 4.0,

    R. Gupta, S. Tanwar, S. Tyagi, N. Kumar, M. S. Obaidat, and B. Sadoun, “HaBiTs: blockchain-based telesurgery framework for healthcare 4.0, ” in 2019 International Conference on Com- puter, Information and Telecommunication Systems (CITS) , pp. 1–5, Beijing, China, 2019

    work page 2019

  8. [8]

    Solutions to scal- ability of blockchain: a survey,

    Q. Zhou, H. Huang, Z. Zheng, and J. Bian, “Solutions to scal- ability of blockchain: a survey, ” IEEE Access , vol. 8, pp. 16440–16455, 2020

    work page 2020

Show all 36 references
  1. [9]

    An overview of blockchain technology: architecture, consensus, and future trends,

    Z. Zheng, S. Xie, H. Dai, X. Chen, and H. Wang, “An overview of blockchain technology: architecture, consensus, and future trends,” in 2017 IEEE international congress on big data (Big- Data congress), pp. 557 –564, Honolulu, HI, USA, 2017

    2017

  2. [10]

    A survey on consensus methods in blockchain for resource-constrained IoT networks,

    M. Salimitari, M. Chatterjee, and Y. P. Fallah, “A survey on consensus methods in blockchain for resource-constrained IoT networks,” Internet of Things , vol. 11, article 100212, 2020

    2020

  3. [11]

    Minimizing the age-of-critical-information: an imitation learning-based scheduling approach under partial observations,

    X. Wang, Z. Ning, S. Guo, M. Wen, and V. Poor, “Minimizing the age-of-critical-information: an imitation learning-based scheduling approach under partial observations, ” IEEE Trans- actions on Mobile Computing, 2021

    2021

  4. [12]

    Xyreum: a highperformance and scalable blockchain for IIoT security and privacy,

    A. S. Sani, D. Yuan, W. Bao et al., “Xyreum: a highperformance and scalable blockchain for IIoT security and privacy,” in 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 1920 –1930, Dallas, TX, USA, 2019

    2019

  5. [13]

    Comparative analy- sis of blockchain consensus algorithms,

    L. M. Bach, B. Mihaljevic, and M. Zagar, “Comparative analy- sis of blockchain consensus algorithms, ” in 2018 41st Interna- tional Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) , pp. 1545–1550, Opatija, Croatia, 2018

    2018

  6. [14]

    Over- view on blockchain consensus mechanisms,

    Y. Z. Liu, J. W. Liu, Z. Y. Zhang, T. G. Xu, and H. Yu, “Over- view on blockchain consensus mechanisms, ” Journal of Cryp- tologic Research, vol. 6, pp. 395 –432, 2019

    2019

  7. [15]

    Bitcoin: a peer-to-peer electronic cash system,

    S. Nakamoto, “Bitcoin: a peer-to-peer electronic cash system, ” Manubot, 2019

    2019

  8. [16]

    Bitcoin- NG: a scalable blockchain protocol,

    I. Eyal, A. E. Gencer, E. G. Sirer, and R. Van Renesse, “Bitcoin- NG: a scalable blockchain protocol, ” in 13th USENIX sympo- sium on networked systems design and implementation (NSDI 16), pp. 45 –59, Santa Clara, CA, 2016

    2016

  9. [17]

    Secure high-rate transaction processing in bitcoin,

    Y. Sompolinsky and A. Zohar, “Secure high-rate transaction processing in bitcoin, ” in International Conference on Finan- cial Cryptography and Data Security , pp. 507 –527, San Juan, Puerto Rico, 2015

    2015

  10. [18]

    PPcoin: peer-to-peer crypto-currency with proof-of-stake,

    S. King and S. Nadal, “PPcoin: peer-to-peer crypto-currency with proof-of-stake,” Self-Published Paper, vol. 19, p. 1, 2012

    2012

  11. [19]

    Proof-of-stake consensus mech- anisms for future blockchain networks: fundamentals, applica- tions and opportunities,

    C. T. Nguyen, D. T. Hoang, D. N. Nguyen, D. Niyato, H. T. Nguyen, and E. Dutkiewicz, “Proof-of-stake consensus mech- anisms for future blockchain networks: fundamentals, applica- tions and opportunities,” IEEE Access, vol. 7, pp. 85727–85745, 2019

    2019

  12. [20]

    Incentivizing consensus propagation in proof-of-stake based consortium blockchain networks,

    J. Kang, Z. Xiong, D. Niyato, D. Ye, D. I. Kim, and P. Wang, “Incentivizing consensus propagation in proof-of-stake based consortium blockchain networks,” IEEE Wireless Communica- tions Letters, vol. 8, no. 1, pp. 157 –160, 2019

    2019

  13. [21]

    Delegated proof of stake with downgrade: a secure and e ffi- cient blockchain consensus algorithm with downgrade mecha- nism,

    F. Yang, W. Zhou, Q. Wu, R. Long, N. N. Xiong, and M. Zhou, “Delegated proof of stake with downgrade: a secure and e ffi- cient blockchain consensus algorithm with downgrade mecha- nism,” IEEE Access, vol. 7, pp. 118541 –118555, 2019

    2019

  14. [22]

    Practical byzantine fault tolerance and proactive recovery,

    M. Castro and B. Liskov, “Practical byzantine fault tolerance and proactive recovery,” ACM Transactions on Computer Sys- tems, vol. 20, no. 4, pp. 398 –461, 2002

    2002

  15. [23]

    An improved scheme for PBFT blockchain consensus algorithm based on ring signature,

    Y. Fang, J. Q. Deng, L. H. Cong, and C. Y. Liu, “An improved scheme for PBFT blockchain consensus algorithm based on ring signature, ” Computer Engineering , vol. 45, pp. 32 –36, 2019

    2019

  16. [24]

    Tendermint: consensus without mining,

    J. Kwon, “Tendermint: consensus without mining, ” Draft v. 0.6, fall, vol. 1, no. 11, 2014

    2014

  17. [25]

    Proof of activity,

    I. Bentov, C. Lee, A. Mizrahi, and M. Rosenfeld, “Proof of activity,” ACM SIGMETRICS Performance Evaluation Review , vol. 42, no. 3, pp. 34 –37, 2014

    2014

  18. [26]

    Multi-agent imitation learning for pervasive edge computing: a decentralized computation offloading algorithm,

    X. Wang, Z. Ning, and S. Guo, “Multi-agent imitation learning for pervasive edge computing: a decentralized computation offloading algorithm,” IEEE Transactions on Parallel and Dis- tributed Systems, vol. 32, no. 2, pp. 411 – 425, 2021

    2021

  19. [27]

    Proof-of-QoS: QoS based blockchain consensus protocol,

    B. Yu, J. Liu, S. Nepal, J. Yu, and P. Rimba, “Proof-of-QoS: QoS based blockchain consensus protocol, ” Computers & Security , vol. 87, p. 101580, 2019

    2019

  20. [28]

    Improved consensus mechanism of blockchain based on proof-of-work and proof- of-stake,

    M. Y. Wu, G. S. Zhu, and S. C. Wu, “Improved consensus mechanism of blockchain based on proof-of-work and proof- of-stake,” Journal of Computer Applications , vol. 40, pp. 2275–2278, 2020

    2020

  21. [29]

    Consistency, availability, and partition tolerance in blockchain: a survey on the consensus mechanism over peer-to-peer networking,

    G. R. Carrara, L. M. Burle, D. S. V. Medeiros, C. V. N. de Albu- querque, and D. M. F. Mattos, “Consistency, availability, and partition tolerance in blockchain: a survey on the consensus mechanism over peer-to-peer networking, ” Annals of Tele- communications, vol. 75, no. 3-...

    2020

  22. [30]

    NEO white paper,

    “NEO white paper, ” https://docs.neo.org/docs/en-us/basic/ whitepaper.html

  23. [31]

    An extensible consensus algo- rithm based on PBFT,

    Y. Li, Z. Wang, J. Fan et al., “An extensible consensus algo- rithm based on PBFT, ” in 2019 International conference on cyber-enabled distributed computing and knowledge discovery (CyberC), pp. 17 – 23, Guilin, China, 2019

    2019

  24. [32]

    Trust-PBFT: a peertrust- based practical byzantine consensus algorithm,

    W. Tong, X. Dong, and J. Zheng, “Trust-PBFT: a peertrust- based practical byzantine consensus algorithm,” in 2019 Inter- national Conference on Networking and Network Applications (NaNA), Daegu, Korea (South), 2019

    2019

  25. [34]

    Improved PBFT scheme based on reputation voting,

    Y. C. Tu, Y. L. Chen, T. Li, X. J. Ren, and X. Y. Qing, “Improved PBFT scheme based on reputation voting, ” Journal of Applied Sciences , vol. 39, no. 1, pp. 79 –89, 2021

    2021

  26. [35]

    An index to quantify an individual's scienti fic research output,

    J. E. Hirsch, “An index to quantify an individual's scienti fic research output,” Proceedings of the National Academy of Sci- ences of the United States of America , vol. 102, no. 46, pp. 16569–16572, 2005

    2005

  27. [36]

    Verifiable random func- tions,

    S. Micali, M. Rabin, and S. V. Adhan, “Verifiable random func- tions,” in 40th Annual Symposium on Foundations of Com- puter Science (Cat. No.99CB37039) , pp. 120 –130, New York, NY, USA, 1999

    1999

  28. [37]

    Verifiable random functions from identity-based key encapsulation,

    M. Abdalla, D. Catalano, and D. Fiore, “Verifiable random functions from identity-based key encapsulation, ” in Annual International Conference on the Theory and Applications of Cryptographic Techniques, Cologne, Germany, 2009. 14 Wireless Communications and Mobile Computing 63...

    2009 doi

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.