Application of IoT and blockchain technology in the integration of innovation and industrial chains in high-tech manufacturing
In industrial IoT (Internet of Things) environments, accurate anomaly detection and high-quality data management are crucial yet challenging due to noisy and incomplete sensor data. This study introduces BD-IoTQNet, a novel framework designed to address these challenges by integrating data fusion, a...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825000298 |
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| author | Zepei Li Peng Zheng Yanjia Tian |
| author_facet | Zepei Li Peng Zheng Yanjia Tian |
| author_sort | Zepei Li |
| collection | DOAJ |
| description | In industrial IoT (Internet of Things) environments, accurate anomaly detection and high-quality data management are crucial yet challenging due to noisy and incomplete sensor data. This study introduces BD-IoTQNet, a novel framework designed to address these challenges by integrating data fusion, anomaly detection using the Isolation Forest algorithm, and blockchain-enabled DQM (Data Quality Management). The framework leverages blockchain technology to ensure data transparency and security, while smart contracts automate exception handling to enhance efficiency. Experiments conducted on the NASA Turbofan Engine Degradation and UCI Hydraulic Systems datasets demonstrate that BD-IoTQNet outperforms existing models in accuracy, precision, and data quality improvement, with reduced latency and enhanced robustness under noisy and missing data conditions. An ablation study validates the critical role of each component, showing significant performance drops when modules like DQM or blockchain are excluded. These findings highlight BD-IoTQNet as an effective solution for improving anomaly detection, predictive maintenance, and operational efficiency in industrial IoT systems. |
| format | Article |
| id | doaj-art-7e2821ecd98b4ca286ec2776443fa369 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-7e2821ecd98b4ca286ec2776443fa3692025-02-09T04:59:45ZengElsevierAlexandria Engineering Journal1110-01682025-04-01119465477Application of IoT and blockchain technology in the integration of innovation and industrial chains in high-tech manufacturingZepei Li0Peng Zheng1Yanjia Tian2Hebei Vocational University of Technology and Engineering, XingTai 054000, China; Corresponding author.Hebei Vocational University of Technology and Engineering, XingTai 054000, ChinaSchool of Electronics and Information, Shanghai DianJi University, Shanghai 201306, ChinaIn industrial IoT (Internet of Things) environments, accurate anomaly detection and high-quality data management are crucial yet challenging due to noisy and incomplete sensor data. This study introduces BD-IoTQNet, a novel framework designed to address these challenges by integrating data fusion, anomaly detection using the Isolation Forest algorithm, and blockchain-enabled DQM (Data Quality Management). The framework leverages blockchain technology to ensure data transparency and security, while smart contracts automate exception handling to enhance efficiency. Experiments conducted on the NASA Turbofan Engine Degradation and UCI Hydraulic Systems datasets demonstrate that BD-IoTQNet outperforms existing models in accuracy, precision, and data quality improvement, with reduced latency and enhanced robustness under noisy and missing data conditions. An ablation study validates the critical role of each component, showing significant performance drops when modules like DQM or blockchain are excluded. These findings highlight BD-IoTQNet as an effective solution for improving anomaly detection, predictive maintenance, and operational efficiency in industrial IoT systems.http://www.sciencedirect.com/science/article/pii/S1110016825000298Industrial ioTAnomaly detectionBlockchain technologyPredictive maintenanceSmart manufacturingReal-time ioT monitoring |
| spellingShingle | Zepei Li Peng Zheng Yanjia Tian Application of IoT and blockchain technology in the integration of innovation and industrial chains in high-tech manufacturing Alexandria Engineering Journal Industrial ioT Anomaly detection Blockchain technology Predictive maintenance Smart manufacturing Real-time ioT monitoring |
| title | Application of IoT and blockchain technology in the integration of innovation and industrial chains in high-tech manufacturing |
| title_full | Application of IoT and blockchain technology in the integration of innovation and industrial chains in high-tech manufacturing |
| title_fullStr | Application of IoT and blockchain technology in the integration of innovation and industrial chains in high-tech manufacturing |
| title_full_unstemmed | Application of IoT and blockchain technology in the integration of innovation and industrial chains in high-tech manufacturing |
| title_short | Application of IoT and blockchain technology in the integration of innovation and industrial chains in high-tech manufacturing |
| title_sort | application of iot and blockchain technology in the integration of innovation and industrial chains in high tech manufacturing |
| topic | Industrial ioT Anomaly detection Blockchain technology Predictive maintenance Smart manufacturing Real-time ioT monitoring |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825000298 |
| work_keys_str_mv | AT zepeili applicationofiotandblockchaintechnologyintheintegrationofinnovationandindustrialchainsinhightechmanufacturing AT pengzheng applicationofiotandblockchaintechnologyintheintegrationofinnovationandindustrialchainsinhightechmanufacturing AT yanjiatian applicationofiotandblockchaintechnologyintheintegrationofinnovationandindustrialchainsinhightechmanufacturing |