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Efficient biometric-based identity management on the Blockchain for smart industrial applications
مدیریت هویت مبتنی بر بیومتریک کارآمد در Blockchain برای کاربردهای صنعتی هوشمند-2021 In this work, we propose a new Blockchain-based Identity Management system for smart industry. First, we describe an efficient biometric-based anonymous credential scheme, which supports selective disclosure, suspension/thaw and revocation of credentials/entities. Our system provides non-transferability through a freshly computed hidden biometric attribute, which is generated using a secure fuzzy extractor during each authentication. This mechanism combined with offchain storage guarantees GDPR compliance, which is required for protecting user’s data. We define blinded (Brands) DLRep scheme to provide multi-show unlinkability, which is a lacking feature in Brands’ credential based systems. For larger organizations, we re-design the system by replacing the Merkle Tree with an accumulator to improve scalability. The new system enables auditing by adapting the standard Industrial IoT (IIoT) Identity Management Lifecycle to Blockchain. Finally, we show that the new proposal outperforms BASS, i.e. the most recent blockchain-based anonymous credential scheme designed for smart industry. The computational cost at the user-side (can be a weak IoT device) of our scheme is 8-times less than that of BASS. Thus, our system is more suitable for IIoT.© 2020 Elsevier B.V. All rights reserved. Keywords: Identity management | Smart industry | Blockchain | Non-transferability | Biometrics | DLRep | Multi-show unlinkability | Selective disclosure | Accumulators |
مقاله انگلیسی |
2 |
Challenges and recommended technologies for the industrial internet of things: A comprehensive review
چالش ها و فن آوری های پیشنهادی برای اینترنت اشیا صنعتی: مرور جامع-2020 Physical world integration with cyber world opens the opportunity of creating smart environments; this
new paradigm is called the Internet of Things (IoT). Communication between humans and objects has
been extended into those between objects and objects. Industrial IoT (IIoT) takes benefits of IoT communications
in business applications focusing in interoperability between machines (i.e., IIoT is a subset
from the IoT). Number of daily life things and objects connected to the Internet has been in increasing
fashion, which makes the IoT be the dynamic network of networks. Challenges such as heterogeneity,
dynamicity, velocity, and volume of data, make IoT services produce inconsistent, inaccurate, incomplete,
and incorrect results, which are critical for many applications especially in IIoT (e.g., health-care, smart
transportation, wearable, finance, industry, etc.). Discovering, searching, and sharing data and resources
reveal 40% of IoT benefits to cover almost industrial applications. Enabling real-time data analysis, knowledge
extraction, and search techniques based on Information Communication Technologies (ICT), such as
data fusion, machine learning, big data, cloud computing, blockchain, etc., can reduce and control IoT and
leverage its value. This research presents a comprehensive review to study state-of-the-art challenges
and recommended technologies for enabling data analysis and search in the future IoT presenting a
framework for ICT integration in IoT layers. This paper surveys current IoT search engines (IoTSEs) and
presents two case studies to reflect promising enhancements on intelligence and smartness of IoT applications
due to ICT integration. Keywords: Industrial IoT (IIoT) | Searching and indexing | Blockchain | Big data | Data fusion Machine learning | Cloud and fog computing |
مقاله انگلیسی |
3 |
Industrial Internet of Things Monitoring Solution for Advanced Predictive Maintenance Applications
راه حل نظارتی اینترنت اشیاء صنعتی برای برنامه های نگهداری پیشگیرانه پیشرفته -2017 Internet of Things (IoT) solutions in industrial environments can lead nowadays
to the development of innovative and efficient systems aiming at increasing op
erational efficiency in a new generation of smart factories. In this direction
the article presents in detail an advanced Industrial IoT (IIoT) solution, the
NGS-PlantOne system, specially designed to enable a pervasive monitoring of
industrial machinery through battery-powered IoT sensing devices, thus allow
ing the development of advanced predictive maintenance applications in the
considered scenario. To the end of evaluating the performance of the devel
oped IIoT system in a real environment, the NGS-PlantOne solution has been
first installed and then set in operation in a real electricity power plant. The
deployed testbed, based on 33 IoT sensing devices performing advanced tem
perature and vibration monitoring tasks, has been kept in operation for two
months while evaluating transmission delays and system operating life through
power consumption measures. Performance results show as the developed IIoT
solution benefits from all the advantages provided by the adopted IoT protocols,
guaranteeing that each node is reachable through simple IP-based techniques
with an acceptable delay, and showing an estimated average life of 1 year in
case of each IoT smart device is configured to send collected and elaborated
data every 30 minutes.
Keywords: Industrial Internet-of-Things | Smart Plants |Industrial monitoring |
مقاله انگلیسی |