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Updated: Apr 30, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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TimesNet-BFT: Mitigating Network State Uncertainty in Byzantine Consensus via Deep Temporal Modeling.

Haolong Wang1, Haijun Liu1, Yahui Liu1

  • 1School of Information Science and Technology, Shihezi University, Shihezi 832000, China.

Entropy (Basel, Switzerland)
|March 28, 2026
PubMed
Summary

This study introduces TimesNet-BFT, an entropy-aware framework enhancing Byzantine fault tolerance (BFT) protocols for blockchains. It optimizes leader election and timeouts, significantly boosting throughput and stability in volatile networks.

Keywords:
Byzantine fault toleranceadaptive timeoutdeep temporal modelingdynamic leader rotationepistemic uncertaintynetwork latency prediction

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Last Updated: Apr 30, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Byzantine fault tolerance (BFT) protocols are crucial for data consistency in permissioned blockchains.
  • Scalability issues arise from leader-centric bottlenecks and rigid timeouts, especially under network volatility.
  • Existing heuristics struggle with high-entropy, time-varying network latency, causing performance degradation.

Purpose of the Study:

  • To propose TimesNet-BFT, an entropy-aware optimization framework for BFT protocols.
  • To address epistemic uncertainty caused by network latency variations.
  • To improve scalability and performance of BFT in dynamic network conditions.

Main Methods:

  • Leveraging TimesNet for transforming time series data into 2D tensors for multi-periodicity analysis.
  • Characterizing stochastic nodal latency patterns to enable entropy-minimized dynamic leader election.
  • Implementing adaptive timeout strategies based on latency predictions.
  • Decoupling consensus safety from AI prediction errors for an aggressive liveness paradigm.

Main Results:

  • Achieved a prediction Mean Absolute Percentage Error (MAPE) below 5% with robust zero-shot generalization.
  • Demonstrated up to a 191.9% increase in throughput under high-entropy network conditions.
  • Mitigated latency variance by 73.3%, neutralizing traditional protocol bottlenecks.
  • Maintained minimal control plane overhead while enhancing consensus process stability.

Conclusions:

  • TimesNet-BFT effectively enhances BFT protocol scalability and performance in volatile networks.
  • The entropy-aware framework significantly improves throughput and reduces latency variance.
  • The novel approach provides a stable and efficient consensus mechanism with minimal overhead.