Benzene: Scaling Blockchain With Cooperation-Based Sharding

Cai, Zhongteng; Liang, Junyuan; Chen, Wuhui; Hong, Zicong; Dai, Hong-Ning; Zhang, Jianting; Zheng, Zibin

doi:10.1109/tpds.2022.3227198

Cited by 24 publications

(8 citation statements)

References 38 publications

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“…Furthermore, articles [14][15][16] optimized blockchain sharding by leveraging deep learning techniques to achieve enhanced performance. Finally, Cai et al [17] presented a solution named Benzene, which reduces the cross-shard communication and storage overhead using a Trusted Execution Environment (TEE) and a dual-chain collaborative architecture. Current approaches primarily center on addressing sharding's scalability issue using machine learning techniques, often neglecting the fundamental factors contributing to the sharding problem.…”

Section: Blockchain Shardingmentioning

confidence: 99%

Trust Management Scheme of IoV Based on Dynamic Sharding Blockchain

Han,

Chen,

et al. 2024

Electronics

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With the rapid development of communication technologies, the demand for security and automation of driving has promoted the development of the Internet of Vehicles (IoV). The IoV aims to provide users with a safer, more comfortable, and more efficient driving experience. However, the current IoV also faces a series of potential security risks and privacy breaches, which has further propelled research on trust management for vehicular networks. The introduction of the blockchain has resolved the issue of data security in IoV trust management. However, the blockchain is limited by its own performance and scalability, making it unsuitable for large-scale networks. In order to enhance the transaction-processing efficiency of blockchain-based trust management solutions and address their scalability limitations, this paper presents a graph partition-based blockchain-sharding protocol. Simulation results on real-world datasets demonstrate that the proposed scheme exhibits better scalability compared to existing blockchain-based approaches and can accommodate larger-scale device access.

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Section: Blockchain Shardingmentioning

confidence: 99%

Trust Management Scheme of IoV Based on Dynamic Sharding Blockchain

Han,

Chen,

et al. 2024

Electronics

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“…Particularly, the consensus participants need a special type of hardware to ensure the TEE. A similar approach that relies on TEE is proposed in [21]. Particularly, a sharding scheme is developed that utilizes two separate blockchains to decouple the transaction recording and consensus processes.…”

Section: Sharding In Blockchainmentioning

confidence: 99%

“…Similar to [20], the proposed scheme relies on TEE, and thus it also requires special hardware to participate in the consensus process. Although the schemes in [20] and [21] can enhance the security of the network, the hardware requirement makes them much less attractive to public users, especially MUs who already need a lot of computing power for AR/VR rendering. In [22], the authors develop a sharding scheme based on Practical Byzantine Fault Tolerance (PBFT).…”

Section: Sharding In Blockchainmentioning

confidence: 99%

“…The reason is that MUs who are more engaged (with high contributions and own a lot of assets) might want to protect the network more. Moreover, in existing approaches such as [17]- [19], [21]- [25], the leader is not selected based on stakes/scores (BFT-based approaches). Instead, these approaches rely only on the number of validators.…”

Section: Mu Engagement and Rewardmentioning

confidence: 99%

“…Similar to (21), this condition is always satisfied since there is no inequality constraint in (P3). The fifth condition is…”

Section: Optimality Analysismentioning

confidence: 99%

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MetaChain: A Novel Blockchain-based Framework for Metaverse Applications

Nguyen

Hoang

Nguyen

et al. 2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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Decoding brain signals can not only reveal Metaverse users' expectations but also early detect error-related behaviors such as stress, drowsiness, and motion sickness. For that, this article proposes a pioneering framework using wireless/overthe-air Brain-Computer Interface (BCI) to assist creation of virtual avatars as human representation in the Metaverse. Specifically, to eliminate the computational burden for Metaverse users' devices, we leverage Wireless Edge Servers (WES) that are popular in 5G architecture and therein URLLC, enhanced broadband features to obtain and process the brain activities, i.e., electroencephalography (EEG) signals (via uplink wireless channels). As a result, the WES can learn human behaviors, adapt system configurations, and allocate radio resources to create individualized settings and enhance user experiences. Despite the potential of BCI, the inherent noisy/fading wireless channels and the uncertainty in Metaverse users' demands and behaviors make the related resource allocation and learning/classification problems particularly challenging. We formulate the joint learning and resource allocation problem as a Quality-of-Experience (QoE) maximization problem that takes into the latency, brain classification accuracy, and resources of the system. To tackle this mixed integer programming problem, we then propose two novel algorithms that are (i) a hybrid learning algorithm to maximize the user QoE and (ii) a meta-learning algorithm to exploit the neurodiversity of the brain signals among multiple Metaverse users. The extensive experiment results with different BCI datasets show that our proposed algorithms can not only provide low delay for virtual reality (VR) applications but also can achieve high classification accuracy for the collected brain signals.

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