Dynamic Blockchain Sharding for 6G Internet of Things Devices Collaboration

doi:10.19678/j.issn.1000-3428.0066577

Abstract

Abstract:

With the massive deployment of Internet of Things (IoT) applications, numerous devices work together to generate massive, high-value data. If the security of these data is not guaranteed, they are vulnerable to threats such as data abuse, privacy leakage, and data tampering. Therefore, a decentralized, immutable, and secure blockchain sharding network is applied in this scenario, gradually replacing the traditional centralized network. However, the performance of blockchain sharding network is limited by the high proportion of cross-shared collaborative transactions or complex environments. To solve these problems, a dynamic blockchain sharding optimization scheme for 6G IoT device collaboration is proposed. First, the sharding architecture of the system is designed, and throughput, security, and delay models of the system are established. Subsequently, a two-stage sharding optimization strategy is proposed. In the first stage, the nodes are screened using a reputation-based sharing and grading strategy. In the second stage, a dynamic sharding strategy based on Deep Reinforcement Learning(DRL) is used to reduce the proportion of cross-shard collaborative transactions and determine the number of shards. By combining these two stages, the throughput of the entire system can be improved while ensuring safety. The experimental results reveal that in the blockchain sharding scenario with the collaboration of IoT devices, the abovementioned scheme, compared with traditional schemes based on reputation-based grading sharding, uniform sharded, or random sharding strategies, can reduce cross-shard collaborative transaction proportions by over 50%. In this case, the throughput of the system improved.

Key words: Internet of Things(IoT), blockchain sharding, Delegated Byzantine Fault Tolerance(DBFT), reputation value, cross-shard collaboration, Deep Reinforcement Learning(DRL)

摘要：

随着物联网规模化应用的不断落地，海量设备协同工作，产生了大量高价值数据。这些数据若得不到有效的安全保障，就容易遭受数据滥用、隐私泄露以及数据篡改等威胁。因此，去中心化、不可篡改、安全的区块链分片网络逐步取代传统的集中式网络，被应用到该场景中。然而，区块链分片网络受限于复杂环境以及高比例跨片协同事务。针对上述问题，提出一种面向6G物联网设备协同的区块链动态分片优化方案。设计分片系统架构，建立整个系统的吞吐量模型、安全模型以及时延模型。在此基础上，提出两阶段分片优化策略。第一阶段采用信誉分级分片策略筛选节点，第二阶段采用基于深度强化学习算法的动态分片策略，降低跨片协同事务比例，决策分片数量。两阶段的设计目的是在保证安全的情况下最大程度地提升整个系统的吞吐量。实验结果表明，在面向物联网设备协同的区块链分片场景下，相较于传统的基于单一的信誉分级分片策略、均匀分片策略或者随机分片策略的方案，所提方案在保证安全性的情况下，平均每轮减少50%以上的跨片协同事务比例，有效地提升了系统的吞吐量。

关键词: 物联网, 区块链分片, 委托拜占庭容错, 信誉值, 跨片协同, 深度强化学习

Ziyue CAI, Beihai TAN, Rong YU, Xumin HUANG, Siming WANG. Dynamic Blockchain Sharding for 6G Internet of Things Devices Collaboration[J]. Computer Engineering, 2024, 50(1): 50-59.

蔡梓越, 谭北海, 余荣, 黄旭民, 王思明. 面向6G物联网设备协同的区块链动态分片[J]. 计算机工程, 2024, 50(1): 50-59.

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Figures/Tables 12

Fig.1 The overall architecture of blockchain sharding system

Fig.2 The consensus process of DBFT

Fig.3 Flowchart of the proposed scheme

Fig.4 Schematic diagram of reputation-based grading and sharding

Fig.5 Structure of Dueling DQN

Fig.6 System performance comparison with different sharding strategies

Fig.7 System performance comparison with changed influencing factors

Fig.8 Change of reputation value for randomly tracked nodes

Fig.9 Change of the proportion of cross-shard collaborative transactions with different sharding strategies

Fig.10 Change of the proportion of cross-shard collaborative transactions with different proportions of multi-cooperative transaction node

Fig.11 System performance comparison with different proportions of global malicious node

References 26

1	NGUYEN D C, DING M, PATHIRANA P N, et al. 6G Internet of Things: a comprehensive survey. IEEE Internet of Things Journal, 2022, 9 (1): 359- 383. doi: 10.1109/JIOT.2021.3103320
2	XIONG Z H, ZHANG Y, NIYATO D, et al. When mobile blockchain meets edge computing. IEEE Communications Magazine, 2018, 56 (8): 33- 39. doi: 10.1109/MCOM.2018.1701095
3	XIONG Z H, ZHANG Y, LUONG N C, et al. The best of both worlds: a general architecture for data management in blockchain-enabled Internet of Things. IEEE Network, 2020, 34 (1): 166- 173. doi: 10.1109/MNET.001.1900095
4	SEKARAN R, PATAN R, RAVEENDRAN A, et al. Survival study on blockchain based 6G-enabled mobile edge computation for IoT automation. IEEE Access, 2020, 8, 143453- 143463. doi: 10.1109/ACCESS.2020.3013946
5	郝敏, 叶东东, 余荣, 等. 区块链赋能的6G零信任车联网可信接入方案. 电子与信息学报, 2022, 44 (9): 3004- 3013.
	HAO M, YE D D, YU R, et al. Trusted access scheme of 6G zero-trust vehicle networking with blockchain empowerment. Journal of Electronics & Information Technology, 2022, 44 (9): 3004- 3013.
6	LUU L, NARAYANAN V, ZHENG C D, et al. A secure sharding protocol for open blockchains[C]//Proceedings of 2016 ACM SIGSAC Conference on Computer and Communications Security. New York, USA: ACM Press, 2016: 17-30.
7	KOKORIS-KOGIAS E, JOVANOVIC P, GASSER L, et al. OmniLedger: a secure, scale-out, decentralized ledger via sharding[C]//Proceedings of 2018 IEEE Symposium on Security and Privacy. Washington D. C., USA: IEEE Press, 2018: 583-598.
8	ZAMANI M, MOVAHEDI M, RAYKOVA M. RapidChain: scaling blockchain via full sharding[C]//Proceedings of 2018 ACM SIGSAC Conference on Computer and Communications Security. New York, USA: ACM Press, 2018: 931-948.
9	YUN J, GOH Y, CHUNG J M. DQN-based optimization framework for secure sharded blockchain systems. IEEE Internet of Things Journal, 2021, 8 (2): 708- 722. doi: 10.1109/JIOT.2020.3006896
10	ZHANG J T, HONG Z C, QIU X Y, et al. SkyChain: a deep reinforcement learning-empowered dynamic blockchain sharding system[C]//Proceedings of the 49th International Conference on Parallel Processing. New York, USA: ACM Press, 2020: 1-11.
11	YUAN S J, LI J E, LIANG J H, et al. Sharding for blockchain based mobile edge computing system: a deep reinforcement learning approach[C]//Proceedings of 2021 IEEE Global Communications Conference. Washington D. C., USA: IEEE Press, 2021: 1-6.
12	温建伟, 姚冰冰, 万剑雄, 等. 结合深度强化学习的区块链分片系统性能优化. 计算机工程与应用, 2022, 58 (19): 116- 123.
	WEN J W, YAO B B, WAN J X, et al. Performance optimization of blockchain fragmentation system combined with deep reinforcement learning. Computer Engineering and Applications, 2022, 58 (19): 116- 123.
13	张立, 段明达, 万剑雄, 等. 车联网区块链吞吐量优化的强化学习方法研究. 计算机科学与探索, 2023, 17 (7): 1708- 1718.
	ZHANG L, DUAN M D, WAN J X, et al. Research on deep reinforcement learning method for throughput optimization of Internet of vehicles blockchain. Journal of Frontiers of Computer Science and Technology, 2023, 17 (7): 1708- 1718.
14	LIU M T, YU F R, TENG Y L, et al. Performance optimization for blockchain-enabled industrial Internet of Things(IIoT) systems: a deep reinforcement learning approach. IEEE Transactions on Industrial Informatics, 2019, 15 (6): 3559- 3570. doi: 10.1109/TII.2019.2897805
15	SU Z, WANG Y T, XU Q C, et al. A secure charging scheme for electric vehicles with smart communities in energy blockchain. IEEE Internet of Things Journal, 2019, 6 (3): 4601- 4613. doi: 10.1109/JIOT.2018.2869297
16	张亮, 刘百祥, 张如意, 等. 区块链技术综述. 计算机工程, 2019, 45 (5): 1- 12. doi: 10.19678/j.issn.1000-3428.0053554
	ZHANG L, LIU B X, ZHANG R Y, et al. Overview of blockchain technology. Computer Engineering, 2019, 45 (5): 1- 12. doi: 10.19678/j.issn.1000-3428.0053554
17	YANG Z X, YANG R Z, YU F R, et al. Sharded blockchain for collaborative computing in the Internet of Things: combined of dynamic clustering and deep reinforcement learning approach. IEEE Internet of Things Journal, 2022, 9 (17): 16494- 16509. doi: 10.1109/JIOT.2022.3152188
18	陈润宇, 王伦文, 朱然刚. 基于信誉值投票与随机数选举的PBFT共识算法. 计算机工程, 2022, 48 (6): 42-49, 56. doi: 10.19678/j.issn.1000-3428.0063904
	CHEN R Y, WANG L W, ZHU R G. PBFT consensus algorithm based on reputation value voting and random number election. Computer Engineering, 2022, 48 (6): 42-49, 56. doi: 10.19678/j.issn.1000-3428.0063904
19	HUANG C Y, WANG Z Y, CHEN H X, et al. RepChain: a reputation-based secure, fast, and high incentive blockchain system via sharding. IEEE Internet of Things Journal, 2021, 8 (6): 4291- 4304. doi: 10.1109/JIOT.2020.3028449
20	SUN W, LEI S Y, WANG L, et al. Adaptive federated learning and digital twin for industrial Internet of Things. IEEE Transactions on Industrial Informatics, 2021, 17 (8): 5605- 5614. doi: 10.1109/TII.2020.3034674
21	王思明, 谭北海, 余荣. 面向6G可信可靠智能的区块链分片与激励机制. 计算机科学, 2022, 49 (6): 32- 38.
	WANG S M, TAN B H, YU R. Blockchain sharding and incentive mechanism for 6G dependable intelligence. Computer Science, 2022, 49 (6): 32- 38.
22	LI Q C, CAO H, WANG S K, et al. A reputation-based multi-user task selection incentive mechanism for crowdsensing. IEEE Access, 2020, 8, 74887- 74900. doi: 10.1109/ACCESS.2020.2989406
23	XIONG Z H, ZHANG Y, NIYATO D, et al. Deep reinforcement learning for mobile 5G and beyond: fundamentals, applications, and challenges. IEEE Vehicular Technology Magazine, 2019, 14 (2): 44- 52. doi: 10.1109/MVT.2019.2903655
24	WANG Z Y, SCHAUL T, HESSEL M, et al. Dueling network architectures for deep reinforcement learning[EB/OL]. [2022-10-12]. https://arxiv.org/abs/1511.06581.pdf.
25	LIU M T, TENG Y L, YU F R, et al. Deep reinforcement learning based performance optimization in blockchain-enabled Internet of vehicle[C]//Proceedings of 2019 IEEE International Conference on Communications. Washington D. C., USA: IEEE Press, 2019: 1-6.
26	BAN T W. An autonomous transmission scheme using dueling DQN for D2D communication networks. IEEE Transactions on Vehicular Technology, 2020, 69 (12): 16348- 16352. doi: 10.1109/TVT.2020.3041458

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