PoRF: Proof-of-Reputation-based Consensus Scheme for Fair Transaction Ordering

Shyamsukha, Sidharth; Bhattacharya, Pronaya; Patel, Farnazbanu; Tanwar, Sudeep; Gupta, Rajesh; Pricop, Emil

doi:10.1109/ecai52376.2021.9515090

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…To assure transparency and control across users, fair consensus mechanisms must be designed where fake node additions are impossible. In this regard, Shymasukha et al [83] proposed a scheme named PoRF, where transactions are ordered and added fairly in the blockchain network, which reduces the risk of collusion in the network. In Industry 5.0, this offers end-to-end transparency when diverse and heterogeneous users are communicating via the network.…”

Section: Exai For Industry 50: An Architectural Overviewmentioning

confidence: 99%

Explainable AI for Industry 5.0: Vision, Architecture, and Potential Directions

Trivedi,

Bhattacharya,

Prasad

et al. 2024

IEEE Open J. Ind. Applicat.

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The Industrial Revolution has shifted towards Industry 5.0, reinventing the Industry 4.0 operational process by introducing human elements into critical decision processes. Industry 5.0 would present massive customization via transformative technologies like cyber-physical systems, artificial intelligence (AI), and big-data analytics. In Industry 5.0, the AI models must be transparent, valid, and interpretable. AI models employ machine learning (ML) and deep learning (DL) mechanisms to make the industrial process autonomous, reduce downtime, and improve operational and maintenance costs. However, the models require explainability in the learning process. Thus explainable AI (EXAI) adds interpretability and improves the diagnosis of critical industrial processes, which augments the machine-to-human (M2H) explanations and vice-versa. Recent surveys of EXAI in industrial applications are mostly oriented towards EXAI models, the underlying assumptions. Still, fewer studies are conducted towards a holistic integration of EXAI with human-centric processes that drives the Industry 5.0 applicative verticals. Thus, to address the gap, we propose a first-of-its-kind survey that systematically untangles EXAI integration and its potential in Industry 5.0 applications. First, we present the background of EXAI in Industry 5.0 and cyber-physical systems (CPS) and a reference EXAI-based Industry 5.0 architecture with insights into Large Language Models (LLMs). Then, based on the research questions, a solution taxonomy of EXAI in Industry 5.0 is presented, which is ably supported by applicative use cases (cloud, digital twins, smart grids, augmented reality, and unmanned aerial vehicles). Finally, a case study of EXAI in manufacturing cost assessment is discussed, followed by open issues and future directions. The survey is designed to extend novel prototypes and designs to realize EXAI-based in real-time Industry 5.0 applications.

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Section: Exai For Industry 50: An Architectural Overviewmentioning

confidence: 99%

Explainable AI for Industry 5.0: Vision, Architecture, and Potential Directions

Trivedi,

Bhattacharya,

Prasad

et al. 2024

IEEE Open J. Ind. Applicat.

Self Cite

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“…A potential solution to address the problem is to form sidechain ledgers that can switch transactional data from one chain to another, depending on the application use case. Another approach is to support sharding in BC, which forms mini BC ledgers which are controlled by a sharded authority [154]. The only concern is to ensure a fair consensus principle on sharded BC so that transactions are fairly ordered in the main chain and selective applications are not prioritized.…”

Section: A Deployment Challengesmentioning

confidence: 99%

Blockchain for Industry 5.0: Vision, Opportunities, Key Enablers, and Future Directions

et al. 2022

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Industry 4.0 have witnessed a paradigm shift from cyber-physical systems (CPS) that aims at massive automation, towards a more customer-driven approach. The shift has been attributed to the design of hyper-cognitive systems, integration of virtual and extended reality, digital machinery prototyping and twin designs, trusted machine boundaries, collaborative robots, and artificial intelligence (AI)-based supply chains. This new wave, termed Industry 5.0, is expected to leverage massive production with user-centric customization outside the scope of Industry 4.0 ecosystems. Industry 5.0 is expected to assist diverse industrial verticals like healthcare, smart farming, drones, smart grids, and supply chain production ecosystems. However, data is shared among multiple heterogeneous networks, spanning different authoritative domains. Thus, trusted and secured data transfer is crucial to synergize and secure the industrial perimeters. Blockchain (BC) is a preferred choice as a security enabler to Industry 5.0 ecosystems owing to its inherent property of immutability, chronology, and auditability in industrial systems. Limited works are proposed that present the vision and holistic view of BC-assisted Industry 5.0 applications. The article presents a first-of-its-kind survey on BC as a security enabler in Industry 5.0. Based on a descriptive survey methodology and research questions, we presented the key drivers, and potential applications, and propose an architectural vision of BC-based Industry 5.0 in diverse applicative verticals. The survey intends to present solutions that would assist industry practitioners, academicians, and researchers to drive novel BCassisted solutions in Industry 5.0 verticals.

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“…Their system works by having a side-chain for the reputation values and having additional communication between the users to derive normalized reputation. In [51], the authors proposed a reputation mechanism for a sharded blockchain structure with the objective to include only honest transactions. Here honest transactions refer to the ones that are not due to exploitation of any vulnerabilities present in the blockchain.…”

Section: Decentralised Reputation and Trust Modelsmentioning

confidence: 99%

Reputation-based PoS for the Restriction of Illicit Activities on Blockchain: Algorand Usecase

Pandey¹,

Agarwal²,

Shukla³

et al. 2021

Preprint

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In cryptocurrency-based permissionless blockchain networks, the decentralized structure enables any user to join and operate across different regions. The criminal entities exploit it by using cryptocurrency transactions on the blockchain to facilitate activities such as money laundering, gambling, and ransomware attacks. In recent times, different machine learning-based techniques can detect such criminal elements based on blockchain transaction data. However, there is no provision within the blockchain to deal with such elements. We propose a reputation-based methodology for response to the users detected carrying out the aforementioned illicit activities. We select Algorand blockchain to implement our methodology by incorporating it within the consensus protocol. The theoretical results obtained prove the restriction and exclusion of criminal elements through block proposal rejection and attenuation of the voting power as a validator for such entities. Further, we analyze the efficacy of our method and show that it puts no additional strain on the communication resources.

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PoRF: Proof-of-Reputation-based Consensus Scheme for Fair Transaction Ordering

Cited by 11 publications

References 10 publications

Explainable AI for Industry 5.0: Vision, Architecture, and Potential Directions

Explainable AI for Industry 5.0: Vision, Architecture, and Potential Directions

Blockchain for Industry 5.0: Vision, Opportunities, Key Enablers, and Future Directions

Reputation-based PoS for the Restriction of Illicit Activities on Blockchain: Algorand Usecase

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