Securing Big Data Integrity for Industrial IoT in Smart Manufacturing Based on the Trusted Consortium Blockchain (TCB)

Juma, Mazen; AlAttar, Fuad; Touqan, Basim

doi:10.3390/iot4010002

Cited by 21 publications

(4 citation statements)

References 58 publications

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“…Another important and dynamically developing area, where the technology of distributed fiber-optic sensors can be applied in the very near future, is a smart home, smart city, smart production, and the Internet of Things [86][87][88]. With the spread of such systems, the issues of energy saving and increasing the lifetime of individual elements become more and more relevant.…”

Section: Discussionmentioning

confidence: 99%

Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application

et al. 2023

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Moving differential and dynamic window moving averaging are simple and well-known signal processing algorithms. However, the most common methods of obtaining sufficient signal-to-noise ratios in distributed acoustic sensing use expensive and precise equipment such as laser sources, photoreceivers, etc., and neural network postprocessing, which results in an unacceptable price of an acoustic monitoring system for potential customers. This paper presents the distributed fiber-optic acoustic sensors data processing and noise suppression techniques applied both to raw data (spatial and temporal amplitude distributions) and to spectra obtained after the Fourier transform. The performance of algorithms’ individual parts in processing distributed acoustic sensor’s data obtained in laboratory conditions for an optical fiber subjected to various dynamic impact events is studied. A comparative analysis of these parts’ efficiency was carried out, and for each type of impact event, the most beneficial combinations were identified. The feasibility of existing noise reduction techniques performance improvement is proposed and tested. Presented algorithms are undemanding for computation resources and provide the signal-to-noise ratio enhancement of up to 13.1 dB. Thus, they can be useful in areas requiring the distributed acoustic monitoring systems’ cost reduction as maintaining acceptable performance while allowing the use of cheaper hardware.

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Section: Discussionmentioning

confidence: 99%

Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application

et al. 2023

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“…The researchers tested their method on motion tracking sensors and showed it significantly improves data quality. Research 22 introduces the Trusted Consortium Blockchain (TCB) framework, a novel approach to address data integrity challenges in smart manufacturing’s big data landscape. TCB harnesses the power of blockchain technology to guarantee the security and verifiability of the massive datasets generated by the multitude of devices and stakeholders interacting within the manufacturing process.…”

Section: Related Workmentioning

confidence: 99%

A two-phase spatiotemporal chaos-based protocol for data integrity in IoT

Alkhonaini,

Alenizi,

Jazyah

et al. 2024

Sci Rep

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One of the biggest problems with Internet of Things (IoT) applications in the real world is ensuring data integrity. This problem becomes increasingly significant as IoT expands quickly across a variety of industries. This study presents a brand-new data integrity methodology for Internet of Things applications. The “sequence sharing” and “data exchange” stages of the suggested protocol are divided into two parts. During the first phase, each pair of nodes uses a new chaotic model for securely exchanging their identity information to generate a common sequence. This phase’s objectives include user authentication and timing calculations for the second phase of the recommended method’s packet validation phase. The recommended approach was tested in numerous settings, and various analyses were taken into account to guarantee its effectiveness. Also, the results were compared with the conventional data integrity control protocol of IoT. According to the results, the proposed method is an efficient and cost-effective integrity-ensuring mechanism with eliminates the need for third-party auditors and leads to reducing energy consumption and packet overhead. The results also show that the suggested approach is safe against a variety of threats and may be used as a successful integrity control mechanism in practical applications.

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“…Big data, no matter how useful for the advancement of medical science and vital to the success of all healthcare organizations, can only be used if security and privacy issues are addressed. To ensure a secure and trustworthy big data environment, it is essential to identify the limitations of existing solutions and envision directions for future research [187], [188].…”

Section: Research Gapsmentioning

confidence: 99%

Federated learning and differential privacy in clinical health: Extensive survey

Odera¹

2023

World J. Adv. Eng. Technol. Sci.

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Federated Learning (FL) is concept that has been adopted in medical field to analyze data in individual devices through aggregation of machine learning model in global server. It also provides data privacy being that the sampled devices are not allowed to share data among themselves. Therefore, it minimizes computation costs and privacy risks to some extent compared to conventional methods of machine learning. However, federation learning provides a different use case in health as compared to other sectors. Preservation of patients’ sensitive information such as electronic health record (EHR) when sharing data among different medical practitioners is of greatest concern. So the question is, how should FL techniques be structured in the current clinical environment where heterogeneity is the order of the day? The EU’s General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act of 1996 (HIPPA) regulations recommends health providers to gain authorizations from patients before sharing their private data for medical analytical progression. This leads to some bottlenecks in clinical analysis. Although attempts have been made to address some of the challenges, privacy, performance, implementation, computation and adversaries still pose some threats. This paper provides a comprehensive review that covers literature, mathematical notations, architecture, process flow, challenges and frameworks used to implement FL with respect to healthcare. Possible solutions on how to address privacy challenges in accordance with HIPPA act and GDPR is discussed. Finally, the study gives future direction of FL in clinical health and a list of practical tools to conduct analysis on patients’ data.

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Securing Big Data Integrity for Industrial IoT in Smart Manufacturing Based on the Trusted Consortium Blockchain (TCB)

Cited by 21 publications

References 58 publications

Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application

Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application

A two-phase spatiotemporal chaos-based protocol for data integrity in IoT

Federated learning and differential privacy in clinical health: Extensive survey

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