TINC: Trusted Intelligent NetChain

📅 2025-11-02
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🤖 AI Summary
To address critical challenges in consortium blockchain sharding—including load imbalance, unfair participant allocation, and limited scalability—this paper proposes a multi-plane sharding architecture. It decouples the control plane from the data plane, introduces an adaptive node scheduling mechanism, and employs Dynamic Decentralized Identity (DDID) for identity management, thereby enabling fair shard assignment and elastic trust governance. The architecture integrates state sharding, a dynamic load-balancing algorithm, and Byzantine Fault Tolerant (BFT) consensus to enhance throughput and resource utilization. Experimental results demonstrate that the proposed scheme significantly outperforms mainstream approaches in terms of transaction throughput, end-to-end latency, transaction failure rate, and node-level load balance. Moreover, it provides strong security guarantees and robustness against dynamic adversarial attacks.

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📝 Abstract
Blockchain technology facilitates the development of decentralized systems that ensure trust and transparency without the need for expensive centralized intermediaries. However, existing blockchain architectures particularly consortium blockchains face critical challenges related to scalability and efficiency. State sharding has emerged as a promising approach to enhance blockchain scalability and performance. However, current shard-based solutions often struggle to guarantee fair participation and a balanced workload distribution among consortium members. To address these limitations, we propose Trusted Intelligent NetChain (TINC), a multi-plane sharding architecture specifically designed for consortium blockchains. TINC incorporates intelligent mechanisms for adaptive node assignment and dynamic workload balancing, enabling the system to respond effectively to changing network conditions while maintaining equitable shard utilization. By decoupling the control and data planes, TINC allows control nodes to focus on consensus operations, while data nodes handle large-scale storage, thus improving overall resource efficiency. Extensive experimental evaluation and formal analysis demonstrate that TINC significantly outperforms existing shard-based blockchain frameworks. It achieves higher throughput, lower latency, balanced node and transaction distributions, and reduced transaction failure rates. Furthermore, TINC maintains essential blockchain security guarantees, exhibiting resilience against Byzantine faults and dynamic network environments. The integration of Dynamic Decentralized Identifiers (DDIDs) further strengthens trust and security management within the consortium network.
Problem

Research questions and friction points this paper is trying to address.

Addresses consortium blockchain scalability and efficiency limitations
Ensures fair participation and balanced workload among members
Improves throughput while maintaining security against Byzantine faults
Innovation

Methods, ideas, or system contributions that make the work stand out.

Multi-plane sharding architecture for consortium blockchains
Intelligent adaptive node assignment and workload balancing
Decoupled control and data planes with DDIDs integration
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Qi Xia
School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China, and also with College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China
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Hu Xia
School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China, and also with College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China
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Isaac Amankona Obiri
School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China, and also with College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China
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Adjei-Arthur Bonsu
School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China, and also with College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China
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Grace Mupoyi Ntuala
School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China, and also with College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China
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Ansu Badjie
School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China, and also with College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China
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Tienin Bole Wilfried
School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China, and also with College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China
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Jiaqin Liu
School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China, and also with College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China
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Lan Ma
School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China, and also with College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China
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Jianbin Gao
School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China, and also with College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China
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Feng Yao
University of California, San Diego
Natural Language Processing