DQNSB (DQN-based Optimization Framework for Secure Sharded Blockchain Systems) is an innovative blockchain implementation that leverages Deep Reinforcement Learning (DRL) techniques to optimize the performance and security of sharded blockchain systems. This project aims to address scalability challenges in traditional blockchain systems by implementing a secure and efficient sharding mechanism enhanced by Deep Q-Network algorithms.
The system is designed with a focus on providing robust security guarantees while improving transaction throughput and reducing latency, making it suitable for IoT and other high-throughput applications.
- Implement a scalable sharded blockchain architecture using Go
- Integrate Deep Q-Network (DQN) algorithms for network optimization
- Develop secure node assignment mechanisms for shards
- Implement PBFT (Practical Byzantine Fault Tolerance) consensus for intra-shard consensus
- Create cross-shard transaction protocols with security guarantees
- Achieve high transaction throughput while maintaining security properties
- Design and implement an evaluation framework to measure performance metrics
- Optimize for IoT and other resource-constrained environments
| Milestone | Description | Target Date |
|---|---|---|
| 1. Research & Design | Complete literature review and system design | Sept 15, 2025 |
| 2. Core Implementation | Implement basic blockchain structure and DQN framework | Oct 15, 2025 |
| 3. Sharding Mechanism | Develop and test secure sharding protocol | Nov 15, 2025 |
| 4. Consensus Integration | Implement PBFT consensus for intra-shard validation | Dec 15, 2025 |
| 5. Cross-shard Protocol | Develop secure cross-shard transaction mechanism | Jan 15, 2026 |
| 6. DQN Optimization | Fine-tune DQN parameters and optimization algorithms | Feb 15, 2026 |
| 7. Testing & Benchmarking | Comprehensive testing and performance evaluation | Mar 15, 2026 |
| 8. Documentation & Release | Complete documentation and initial release | Apr 15, 2026 |
- Programming Language: Go (Golang)
- Blockchain Framework: Custom implementation with sharding capabilities
- Consensus Protocols: PBFT (Practical Byzantine Fault Tolerance)
- Machine Learning: Deep Q-Network (DQN) implementation in Go
- Testing: Go testing framework
- Documentation: Markdown, GoDoc
- DevOps: Docker, GitHub Actions
- Monitoring: Prometheus, Grafana
- Blockchain Core Developers: Implement the core blockchain functionality and sharding mechanisms
- Consensus Protocol Engineers: Develop and integrate the PBFT consensus algorithm
- DQN Specialists: Implement and optimize the Deep Q-Network algorithms
- Security Analysts: Verify security properties and perform vulnerability assessments
- Performance Engineers: Optimize system performance and conduct benchmarking
- Documentation Team: Create comprehensive technical documentation
- Set up project structure and repository organization
- Define core data structures for blockchain components
- Implement basic node communication protocol
- Design initial sharding mechanism
- Create DQN framework skeleton
- Research on optimal DQN parameters for blockchain sharding
- Developing test scenarios for security validation
- Initial literature review
- Project scope definition
- Technology stack selection
Comprehensive documentation is available in the /docs directory:
- DQNSB Framework Overview: Core concepts and architecture
- PBFT Consensus Protocol: Details on the consensus mechanism
- ELASTICO Reference: Reference implementation of sharding
Additional implementation details can be found in the codebase comments and in the /docs directory.
We welcome contributions to the DQNSB project! Please follow these steps to contribute:
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
Please ensure your code follows our coding standards and includes appropriate tests.
Our target performance metrics include:
- Transaction throughput: >10,000 TPS
- Confirmation latency: <2 seconds
- Shard count scalability: Linear scaling with network size
- Security guarantee: Tolerating up to 33% Byzantine nodes per shard