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FCDSB: A Fog Computing Network Architecture Based on Dynamic Sharding Blockchain for Consumer Electronics in AIoT

文献类型： 外文期刊

作者： Wei, Chenxiang 1 ; Lin, Hui 1 ; Que, Youxiong 3 ; Wang, Xiaoding 1 ;

作者机构： 1.Fujian Normal Univ, Coll Comp & Cyber Secur, Fuzhou 350117, Fujian, Peoples R China

2.Fujian Prov Univ, Engn Res Ctr Cyber Secur & Educ Informatizat, Fuzhou 350117, Fujian, Peoples R China

3.Chinese Acad Trop Agr Sci, Inst Trop Biosci & Biotechnol, Sanya Res Inst, Natl Key Lab Trop Crop Breeding, Sanya 572024, Hainan, Peoples R China

4.Fujian Agr & Forestry Univ, Key Lab Sugarcane Biol & Genet Breeding, Minist Agr & Rural Affairs, Fuzhou 350002, Fujian, Peoples R China

关键词： Artificial Intelligence of Things; consumer elec-tronics; fog computing; sharding blockchain; sharding blockchain; deep reinforcement learning; deep reinforcement learning; deep reinforcement learning

期刊名称：IEEE TRANSACTIONS ON CONSUMER ELECTRONICS （ 影响因子：10.9； 五年影响因子：8.6 ）

ISSN： 0098-3063

年卷期： 2025 年 71 卷 1 期

页码：

收录情况： SCI

摘要： The Artificial Intelligence of Things (AIoT) is revolutionizing the consumer electronics sector by enabling intelligent interaction and real-time adjustments in consumer electronics products to provide personalized services. Fog computing extends computational services to the network edge, further enhancing the potential of AIoT. However, since fog layer devices dedicate most resources to executing core programs and data transmission, supporting additional security and privacy protections becomes highly challenging. Sharding blockchain, with its unique data-sharing mechanism, facilitates information flow in an open and trustless network, allowing secure and efficient management of large-scale AIoT data. Nevertheless, existing sharding blockchain systems face scalability issues due to low throughput and are susceptible to security risks from malicious attacks, limiting their application in AIoT. To address these challenges, this paper proposes a fog computing network architecture based on dynamic sharding blockchain, called FCDSB. The scheme optimizes sharding strategies by establishing a node reputation mechanism to reduce the impact of malicious nodes. The level of malice in the network is assessed by analyzing consensus inconsistencies, which helps determine the sharding boundaries under the current network conditions. Furthermore, deep reinforcement learning (DRL) is utilized to adaptively optimize system throughput and security levels to meet the evolving network conditions and demands. Simulation results indicate that this scheme significantly improves the scalability of sharding blockchain in AIoT applications while maintaining a high level of security.

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