Optimizing Predictive Maintenance in Industrial IoT Cloud Using Dragonfly Algorithm

Cloud computing enables users to access and utilize various computing resources over the internet, including servers, storage, databases, and analytics. This paradigm offers flexibility, scalability, and cost efficiency, making it a critical technology for numerous applications. In the realm of the...

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Veröffentlicht in:	IEEE internet of things journal Jg. 12; H. 17; S. 36001 - 36018
Hauptverfasser:	Rani S, Sheeja, AbuRukba, Raafat, El-Fakih, Khaled
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.09.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Algorithms Brokenstick regression multiobjective dragonfly optimization cloud Cloud computing Data analysis Data collection Downtime Error analysis Error reduction Fault diagnosis Heuristic methods Industrial applications Industrial Internet of Things Industrial IoT Internet of Things Machine learning algorithms Maintenance Multiple objective analysis Optimization Prediction algorithms Predictive maintenance predictive maintenance optimization Real-time systems Recall Scalability
ISSN:	2327-4662, 2327-4662
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Abstract	Cloud computing enables users to access and utilize various computing resources over the internet, including servers, storage, databases, and analytics. This paradigm offers flexibility, scalability, and cost efficiency, making it a critical technology for numerous applications. In the realm of the Internet of Things (IoT), cloud computing provides a scalable and flexible infrastructure for managing the vast amount of data generated by IoT devices. Specifically, in Industrial IoT applications (IIoT), predictive maintenance has become a key focus, leveraging advanced technologies to forecast equipment failures and minimize downtime. However, achieving high accuracy in fault prediction remains a challenge. To address this, we propose a novel approach called Brokenstick Regression-based multiobjective dragonfly predictive optimization (BR-MDPO). This method aims to optimize predictive maintenance with enhanced accuracy and execution time (ET). The process begins with IoT devices collecting data, such as vibration, temperature, speed, torque, and operational hours, from industrial machinery. This data is then sent to centralized cloud data centers for predictive analysis. The BR-MDPO technique utilizes the Multiobjective Dragonfly Optimization algorithm, a metaheuristic inspired by the natural behavior of dragonflies, to solve multiobjective optimization problems. Brokenstick regression analyzes the data to optimize various objective functions. The technique identifies potential failures, facilitating proactive maintenance and informed decision-making to ensure continuous productivity. The proposed method shows a significant improvement in accuracy, precision, and recall by 7%, 5%, and 6%, respectively. The observed results reveal a 6%, 4%, and 5% enhancement in the accuracy, precision, and recall. Furthermore, the proposed technique realizes a substantial reduction in error rate by 68%, 15%, and 13% reduction in ET as well as latency compared to conventional methods.
AbstractList	Cloud computing enables users to access and utilize various computing resources over the internet, including servers, storage, databases, and analytics. This paradigm offers flexibility, scalability, and cost efficiency, making it a critical technology for numerous applications. In the realm of the Internet of Things (IoT), cloud computing provides a scalable and flexible infrastructure for managing the vast amount of data generated by IoT devices. Specifically, in Industrial IoT applications (IIoT), predictive maintenance has become a key focus, leveraging advanced technologies to forecast equipment failures and minimize downtime. However, achieving high accuracy in fault prediction remains a challenge. To address this, we propose a novel approach called Brokenstick Regression-based multiobjective dragonfly predictive optimization (BR-MDPO). This method aims to optimize predictive maintenance with enhanced accuracy and execution time (ET). The process begins with IoT devices collecting data, such as vibration, temperature, speed, torque, and operational hours, from industrial machinery. This data is then sent to centralized cloud data centers for predictive analysis. The BR-MDPO technique utilizes the Multiobjective Dragonfly Optimization algorithm, a metaheuristic inspired by the natural behavior of dragonflies, to solve multiobjective optimization problems. Brokenstick regression analyzes the data to optimize various objective functions. The technique identifies potential failures, facilitating proactive maintenance and informed decision-making to ensure continuous productivity. The proposed method shows a significant improvement in accuracy, precision, and recall by 7%, 5%, and 6%, respectively. The observed results reveal a 6%, 4%, and 5% enhancement in the accuracy, precision, and recall. Furthermore, the proposed technique realizes a substantial reduction in error rate by 68%, 15%, and 13% reduction in ET as well as latency compared to conventional methods.
Author	AbuRukba, Raafat El-Fakih, Khaled Rani S, Sheeja
Author_xml	– sequence: 1 givenname: Sheeja orcidid: 0000-0002-5169-0860 surname: Rani S fullname: Rani S, Sheeja email: sheejaranis@gmail.com organization: Computer Science and Engineering Department, American University of Sharjah, Sharjah, UAE – sequence: 2 givenname: Raafat orcidid: 0000-0003-2695-5836 surname: AbuRukba fullname: AbuRukba, Raafat email: raburukba@aus.edu organization: Computer Science and Engineering Department, American University of Sharjah, Sharjah, UAE – sequence: 3 givenname: Khaled orcidid: 0000-0002-2343-2848 surname: El-Fakih fullname: El-Fakih, Khaled email: kelfakih@aus.edu organization: Computer Science and Engineering Department, American University of Sharjah, Sharjah, UAE
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SubjectTerms	Accuracy Algorithms Brokenstick regression multiobjective dragonfly optimization cloud Cloud computing Data analysis Data collection Downtime Error analysis Error reduction Fault diagnosis Heuristic methods Industrial applications Industrial Internet of Things Industrial IoT Internet of Things Machine learning algorithms Maintenance Multiple objective analysis Optimization Prediction algorithms Predictive maintenance predictive maintenance optimization Real-time systems Recall Scalability
Title	Optimizing Predictive Maintenance in Industrial IoT Cloud Using Dragonfly Algorithm
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