An intelligent trusted edge data production method for distributed Internet of things

Zhang, Jiangjiang; Ning, Zhenhu; Cao, H

doi:10.1007/s00521-023-08651-5

Cited by 2 publications

(2 citation statements)

References 42 publications

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“…It can be regarded as a distributed ledger, with each node in the network locally storing a copy of the same ledger, containing an unalterable series of data operation records. The emergence of new applications like the IoT has led to the generation of substantial data by smart devices, driving the advancement of artificial intelligence methods in wireless networks and related applications [3,4]. Federated learning (FL), as a prominent distributed machine learning framework, enables the training of data analysis models using data from various sources without revealing user data, thereby mitigating certain security risks associated with data sharing [5].…”

Section: Introductionmentioning

confidence: 99%

A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security

Luo,

Gong,

Zhu

et al. 2023

Applied Sciences

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The machine learning paradigms driven by the sixth-generation network (6G) facilitate an ultra-fast and low-latency communication environment. However, specific research and practical applications have revealed that there are still various issues regarding their applicability. A system named Incentivizing Secure Federated Learning Systems (ISFL-Sys) is proposed, consisting of a blockchain module and a federated learning module. A data-security-oriented trustworthy federated learning mechanism called Efficient Trustworthy Federated Learning (ETFL) is introduced in the system. Utilizing a directed acyclic graph as the ledger for edge nodes, an incentive mechanism has been devised through the use of smart contracts to encourage the involvement of edge nodes in federated learning. Experimental simulations have demonstrated the efficient security of the proposed federated learning mechanism. Furthermore, compared to benchmark algorithms, the mechanism showcases improved convergence and accuracy.

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Section: Introductionmentioning

confidence: 99%

A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security

Luo,

Gong,

Zhu

et al. 2023

Applied Sciences

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“…Intelligent creation of trusted edge data for distributed IoT; • Adaptive configuration of IoT applications in fog infrastructure; • Utilization of cryptographic schemes for secure edge processing, including server deployment in multi-user edge processing; • Intelligent management of resources in fog computing using RL, including multi-user placement of IoT services with QoS consideration [52][53][54][55][56][57][58][59][60][61][62].…”

mentioning

confidence: 99%

Autonomous Threat Response at the Edge Processing Level in the Industrial Internet of Things

Czeczot,

Rojek,

Mikołajewski

2024

Electronics

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Industrial Internet of Things (IIoT) technology, as a subset of the Internet of Things (IoT) in the concept of Industry 4.0 and, in the future, 5.0, will face the challenge of streamlining the way huge amounts of data are processed by the modules that collect the data and those that analyse the data. Given the key features of these analytics, such as reducing the cost of building massive data centres and finding the most efficient way to process data flowing from hundreds of nodes simultaneously, intermediary devices are increasingly being used in this process. Fog and edge devices are hardware devices designed to pre-analyse terabytes of data in a stream and decide in realtime which data to send for final analysis, without having to send the data to a central processing unit in huge local data centres or to an expensive cloud. As the number of nodes sending data for analysis via collection and processing devices increases, so does the risk of data streams being intercepted. There is also an increased risk of attacks on this sensitive infrastructure. Maintaining the integrity of this infrastructure is important, and the ability to analyse all data is a resource that must be protected. The aim of this paper is to address the problem of autonomous threat detection and response at the interface of sensors, edge devices, cloud devices with historical data, and finally during the data collection process in data centres. Ultimately, we would like to present a machine learning algorithm with reinforcements adapted to detect threats and immediately isolate infected nests.

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An intelligent trusted edge data production method for distributed Internet of things

Cited by 2 publications

References 42 publications

A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security

A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security

Autonomous Threat Response at the Edge Processing Level in the Industrial Internet of Things

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