SDN–IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic

Abstract

The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, the manufacturing and socio-economic operations that require human involvement have significantly intervened since the beginning of the outbreak. As experienced, the social-distancing lesson in the potential new-normal world seems to force stakeholders to encourage the deployment of contactless Industry 4.0 architecture. Thus, human-less or less-human operations to keep these IoT-enabled ecosystems running without interruptions have motivated us to design and demonstrate an intelligent automated framework. In this research, we have proposed “EdgeSDN-I4COVID” architecture for intelligent and efficient management during COVID-19 of the smart industry considering the IoT networks. Moreover, the article presents the SDN-enabled layer, such as data, control, and application, to effectively and automatically monitor the IoT data from a remote location. In addition, the proposed convergence between SDN and NFV provides an efficient control mechanism for managing the IoT sensor data. Besides, it offers robust data integration on the surface and the devices required for Industry 4.0 during the COVID-19 pandemic. Finally, the article justified the above contributions through particular performance evaluations upon appropriate simulation setup and environment.

Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning

“…This technique then assigns the given data point to the KNN's most familiar class. The number of potential nearest neighbours for KNN chosen were (2,3,5,8,10,12,15,20). XGBoost is an ensemble approach to build a series of trees successively [84].…”

“…Numerous applications of Machine Learning (ML) have been utilized in activities such as sanitizing places with drones [6], tracking users using face recognition, drug development, automated robots delivering medicine and food, COVID-19 diagnosis, etc. According to the current literature, ML and hybridised models have been successfully applied in several domains of engineering [7][8][9][10], psychometric analysis [11,12], medical and pharmaceutics [13][14][15], graph theory [16], and social sciences [17][18][19].…”

Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process

“…Intelligent manufacturing is a production line trend for the manufacturing industry [46][47][48]. Manufactured textiles and related products, such as face masks and clothes, are commonly used.…”

“…The literature shows that ICT technologies provide contactless operations to maintain sustainable manufacturing without interruptions. Compared with traditional human operations, ICT technologies provide less human operations, which reduces the risk of immense contact restrictions during the COVID-19 pandemic [48]. In this study, we deployed a trained model as PdM API, a convenient interface to programmatically query and interact with equipment databases based on REST patterns for the web.…”

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