CCP-federated deep learning based on user trust chain in social IoV

Zhao, Pengcheng; Huang, Yuanhao; Zhang, Dexin; Xing, Ling; Wu, Honghai; Gao, Jianping

doi:10.1007/s11276-021-02870-1

Cited by 7 publications

(1 citation statement)

References 38 publications

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“…[6] adopts the minimum forest algorithm to process the signal and detect the electric shock current, which can extract the electric shock current model of the contact object and determine whether the threshold is triggered. [7] proposes an anomaly diagnosis method based on the eigenmode vector integrated with the Internet of Things technology. It studied the practical application in the low-voltage communication network, monitored the leakage trigger point, and solved various problems from operation management to subsequent maintenance.…”

Section: Eeice-2023mentioning

confidence: 99%

Research and Application of Anti-electric Shock Communication Terminal Based on Internet of Things Technology

Ma,

Yu,

You

et al. 2023

J. Phys.: Conf. Ser.

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Leakage protection cannot effectively identify the voltage and current reception signals of electric shock grounding points that may pose a danger to personal safety through analysis of electric shock parameters, which is inherently inadequate. When a communication electric shock safety accident occurs, the accident site can be visually displayed in the safety prevention and control center, and an alarm is sent out. Meanwhile, relevant control instructions are issued to cut off the accident communication phase line to prevent secondary injury. In this paper, artificial intelligence and Internet of Things technology are applied to the communication system. The tag sensor is used in the monitoring system and is also used as the perception layer. The electric shock receiving signal obtained under the control circuit structure of the Internet of Things is determined by means of S transformation. The physical characteristics of wave frequency sensitivity are analyzed. Then, the external characteristics of biological shock are extracted with the help of the working frequency of each band of wavelet multi-resolution analysis, so that the electric shock model can deal with the hazards that cannot be identified by the traditional perception layer. The deep neural network is optimized to improve the recognition probability. Experiments verify that the recognition rate of the biological electric shock is as high as 99%, which further improves the stability of the leakage protection device and provides a guarantee for the normal operation of the communication system.

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Section: Eeice-2023mentioning

confidence: 99%

Research and Application of Anti-electric Shock Communication Terminal Based on Internet of Things Technology

Ma,

Yu,

You

et al. 2023

J. Phys.: Conf. Ser.

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Blockchain‐based federated learning approaches in internet of things applications

Li,

Hu,

Zeng

et al. 2024

Security and Privacy

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The Internet of Things (IoT) is a new well‐structured emerging technology with communication of smart devices using the 5G technology, infrastructures of roads, vehicles, smart cities, traffic systems and user applications. The IoT applications facilitate providing prompt emergency responses, and improved quality of vehicles, and road services, with cost‐effective activities in the intelligent transportation systems. Federated Learning (FL) enhances privacy and security in intelligent transportation systems and the Internet of Vehicles (IoV), using advanced prediction methods. Integrating blockchain with IoT, particularly in FL for transportation systems and IoV, bolsters security and data integrity. This approach keeps data local while only sharing model updates, enhancing privacy. Blockchain's transparency aids in efficient IoT collaboration, crucial for accountability. Its consensus algorithms further ensure network integrity, validating transactions and updates across devices, protecting against attacks, and fostering a transparent, collaborative environment. This comprehensive review paper delves into the innovative integration of blockchain technology with federated learning and the dynamic domain of IoV. It extensively analyzes the primary concepts, methodologies, and challenges associated with the deployment of FL in IoVs. This review presents a novel categorization examining three main types of blockchain‐based FL approaches vertical, horizontal, and decentralized each tailored to specific IoV communication scenarios like Vehicle‐to‐Vehicle (V2V), Vehicle‐to‐Infrastructure (V2I), and Vehicle‐to‐Cloud (V2C). It highlights FL applications in cyber‐attack detection, data sharing, traffic prediction, and privacy, considering Quality of Service factors. Finally, some main challenges and new open issues are discussed and assessed for federated machine learning approaches in the IoV.

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