Internet-of-Explainable-Digital-Twins: A Case Study of Versatile Corn Production Ecosystem

Bhattacharya, Pronaya; Obaidat, Mohammad S.; Sanghavi, Sakshi; Sakariya, Vatsal; Tanwar, Sudeep; Hsiao, Kuei‐Fang

doi:10.1109/ccci55352.2022.9926502

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Thus, research has shifted toward developing beyond 5G (B5G) and 6G communication networks. In 6G networks, the IoT networks would expand their operational boundaries and would support massive information‐centric networks, 57 digital twins, 58 augmented and virtual reality, 59 and other applications. In 6G‐IoT networks, dynamic, small‐scale, and heterogeneous data would be handled via the massive eMBB services.…”

Section: Background Studymentioning

confidence: 99%

A transformative shift toward blockchain‐basedIoTenvironments: Consensus, smart contracts, and future directions

Trivedi

Rao

Parmar

et al. 2023

Security and Privacy

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Recently, Internet‐of‐Things (IoT) based applications have shifted from centralized infrastructures to decentralized ecosystems, owing to user data's security and privacy limitations. The shift has opened new doors for intruders to launch distributed attacks in diverse IoT scenarios that jeopardize the application environments. Moreover, as heterogeneous and autonomous networks communicate, the attacks intensify, which justifies the requirement of trust as a key policy. Recently, blockchain‐based IoT solutions have been proposed that address trust limitations by maintaining data consistency, immutability, and chronology in IoT environments. However, IoT ecosystems are resource‐constrained and have low bandwidth and finite computing power of sensor nodes. Thus, the inclusion of blockchain requires an effective policy design regarding consensus and smart contract environments in heterogeneous IoT applications. Recent studies have presented blockchain as a potential solution in IoT, but an effective view of consensus and smart contract design to meet the end application requirements is an open problem. Motivated by the same, the survey presents the integration of suitable low‐powered consensus protocols and smart contract design to assess and validate the blockchain‐IoT ecosystems. We present blockchain‐IoT's emerging communication and security aspects with performance issues of consensus protocols, interoperability, and implementation platforms. A case study of a smart contract‐based blockchain‐driven ecosystem is presented with a comparative analysis of mining cost and latency, which shows its suitability in real‐world setups. We also highlight attacks on blockchain IoT, open issues, potential findings, and future directions. The survey intends to drive novel solutions for future consensus and safe, smart contract designs to support applicative IoT ecosystems.

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Section: Background Studymentioning

confidence: 99%

A transformative shift toward blockchain‐basedIoTenvironments: Consensus, smart contracts, and future directions

Trivedi

Rao

Parmar

et al. 2023

Security and Privacy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Artificial intelligence (AI) models analyze the collected real-time data, which improves operational process efficiency by reducing biases. Some common applications includes IoT-based healthcare [5], inventory and supply-chain management [6], cloud services [7], unmanned aerial vehicles (UAVs) communication [8], digital twins [9], and others. In Industry 4.0, the vast amounts of big data and the interconnectedness of machines, processes, and systems used for model training means there's a diminished human touch.…”

Section: Introductionmentioning

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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Explainable Artificial Intelligence (XAI) Empowered Digital Twin on Soil Carbon Emission Management Using Proximal Sensing

An,

Chen

2023

2023 IEEE 3rd International Conference on Digital Twins and Parallel Intelligence (DTPI)

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Internet-of-Explainable-Digital-Twins: A Case Study of Versatile Corn Production Ecosystem

Cited by 4 publications

References 11 publications

A transformative shift toward blockchain‐basedIoTenvironments: Consensus, smart contracts, and future directions

A transformative shift toward blockchain‐basedIoTenvironments: Consensus, smart contracts, and future directions

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

Explainable Artificial Intelligence (XAI) Empowered Digital Twin on Soil Carbon Emission Management Using Proximal Sensing

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