{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:06:58Z","timestamp":1787029618074,"version":"build-2736575974"},"reference-count":52,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Coronavirus disease 2019 (COVID-19) has caused severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the globe, impacting effective diagnosis and treatment for any chronic illnesses and long-term health implications. In this worldwide crisis, the pandemic shows its daily extension (i.e., active cases) and genome variants (i.e., Alpha) within the virus class and diversifies the association with treatment outcomes and drug resistance. As a consequence, healthcare-related data including instances of sore throat, fever, fatigue, cough, and shortness of breath are given due consideration to assess the conditional state of patients. To gain unique insights, wearable sensors can be implanted in a patient\u2019s body that periodically generates an analysis report of the vital organs to a medical center. However, it is still challenging to analyze risks and predict their related countermeasures. Therefore, this paper presents an intelligent Edge-IoT framework (IE-IoT) to detect potential threats (i.e., behavioral and environmental) in the early stage of the disease. The prime objective of this framework is to apply a new pre-trained deep learning model enabled by self-supervised transfer learning to build an ensemble-based hybrid learning model and to offer an effective analysis of prediction accuracy. To construct proper clinical symptoms, treatment, and diagnosis, an effective analysis such as STL observes the impact of the learning models such as ANN, CNN, and RNN. The experimental analysis proves that the ANN model considers the most effective features and attains a better accuracy (~98.3%) than other learning models. Also, the proposed IE-IoT can utilize the communication technologies of IoT such as BLE, Zigbee, and 6LoWPAN to examine the factor of power consumption. Above all, the real-time analysis reveals that the proposed IE-IoT with 6LoWPAN consumes less power and response time than the other state-of-the-art approaches to infer the suspected victims at an early stage of development of the disease.<\/jats:p>","DOI":"10.3390\/s23062995","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T02:05:54Z","timestamp":1678413954000},"page":"2995","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats"],"prefix":"10.3390","volume":"23","author":[{"given":"B. D.","family":"Deebak","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Gyeonggido, Seongnam 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6375-4123","authenticated-orcid":false,"given":"Fadi","family":"Al-Turjman","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Engineering Deptartment, AI and Robotics Institute, Near East University, Mersin 10, Turkey"},{"name":"Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/s41579-020-00461-z","article-title":"Considerations for diagnostic COVID-19 tests","volume":"19","author":"Vandenberg","year":"2021","journal-title":"Nat. Rev. Microbiol."},{"key":"ref_2","first-page":"1","article-title":"COVID-19 in early 2021: Current status and looking forward","volume":"6","author":"Wang","year":"2021","journal-title":"Signal Transduct. Target. Ther."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1812","DOI":"10.1039\/C9CS00319C","article-title":"Recent progress, challenges, and prospects of fully integrated mobile and wearable point-of-care testing systems for self-testing","volume":"49","author":"Shrivastava","year":"2020","journal-title":"Chem. Soc. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.dsx.2020.04.012","article-title":"Artificial Intelligence (AI) applications for COVID-19 pandemic","volume":"14","author":"Vaishya","year":"2020","journal-title":"Diabetes Metab. Syndr. Clin. Res. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102814","DOI":"10.1016\/j.ctim.2022.102814","article-title":"AYUSH-64 as an adjunct to Standard Care in mild to moderate COVID-19: An open-label randomized controlled trial in Chandigarh, India","volume":"66","author":"Singh","year":"2022","journal-title":"Complement. Ther. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106357","DOI":"10.1016\/j.cmpb.2021.106357","article-title":"A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work","volume":"209","author":"Salman","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"621","DOI":"10.3390\/fi4030621","article-title":"e-Health cloud: Opportunities and challenges","volume":"4","author":"AbuKhousa","year":"2012","journal-title":"Future Internet"},{"key":"ref_8","first-page":"242","article-title":"Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization","volume":"12","author":"Khourdifi","year":"2019","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"10163","DOI":"10.1007\/s13369-021-06419-4","article-title":"Application of Mathematical Modeling in Prediction of COVID-19 Transmission Dynamics","volume":"47","author":"AlArjani","year":"2022","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"493","DOI":"10.5465\/amp.2019.0053","article-title":"COVID-19, societalization, and the future of business in society","volume":"34","author":"Brammer","year":"2020","journal-title":"Acad. Manag. Perspect."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1109\/MNET.011.2000195","article-title":"Artificial-intelligence-enabled intelligent 6G networks","volume":"34","author":"Yang","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1016\/j.future.2019.09.002","article-title":"Blockiotintelligence: A blockchain-enabled intelligent IoT architecture with artificial intelligence","volume":"110","author":"Singh","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s11276-020-02445-6","article-title":"Unification of Blockchain and Internet of Things (BIoT): Requirements, working model, challenges and future directions","volume":"27","author":"Bhushan","year":"2021","journal-title":"Wirel. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.neucom.2021.10.035","article-title":"Time series predicting of COVID-19 based on deep learning","volume":"468","author":"Alassafi","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-020-17971-2","article-title":"Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets","volume":"11","author":"Harmon","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8591036","DOI":"10.1155\/2021\/8591036","article-title":"IoT-based smart health monitoring system for COVID-19 patients","volume":"2021","author":"Khan","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Al-Kahtani, M.S., Khan, F., and Taekeun, W. (2022). Application of Internet of Things and Sensors in Healthcare. Sensors, 22.","DOI":"10.3390\/s22155738"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"109947","DOI":"10.1016\/j.chaos.2020.109947","article-title":"Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review","volume":"138","author":"Swapnarekha","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/TNET.2014.2364972","article-title":"Information source detection in the SIR model: A sample-path-based approach","volume":"24","author":"Zhu","year":"2014","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mishra, S., Mallick, P.K., Tripathy, H.K., Bhoi, A.K., and Gonz\u00e1lez-Briones, A. (2020). Performance evaluation of a proposed machine learning model for chronic disease datasets using an integrated attribute evaluator and an improved decision tree classifier. Appl. Sci., 10.","DOI":"10.3390\/app10228137"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1016\/j.dsx.2020.06.036","article-title":"Learning opportunities from COVID-19 and future effects on health care system","volume":"14","author":"Iyengar","year":"2020","journal-title":"Diabetes Metab. Syndr. Clin. Res. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"110495","DOI":"10.1016\/j.chaos.2020.110495","article-title":"CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images","volume":"142","author":"Hussain","year":"2021","journal-title":"Chaos Solitons Fractals"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7326","DOI":"10.1109\/ACCESS.2022.3142522","article-title":"A novel metaheuristic framework based on the generalized boltzmann distribution for COVID-19 spread characterization","volume":"10","year":"2022","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1007\/s12539-021-00499-4","article-title":"Clinical and laboratory approach to diagnose COVID-19 using machine learning","volume":"14","author":"Chadaga","year":"2022","journal-title":"Interdiscip. Sci. Comput. Life Sci."},{"key":"ref_25","first-page":"537","article-title":"Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity","volume":"63","author":"Jiang","year":"2020","journal-title":"Comput. Mater. Contin."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"110120","DOI":"10.1016\/j.chaos.2020.110120","article-title":"Comparison of deep learning approaches to predict COVID-19 infection","volume":"140","author":"Alakus","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_27","first-page":"1329","article-title":"Intelligent forecasting model of COVID-19 novel coronavirus outbreak empowered with deep extreme learning machine","volume":"64","author":"Khan","year":"2020","journal-title":"Comput. Mater. Contin."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e12806","DOI":"10.1111\/anec.12806","article-title":"Electrocardiogram analysis of patients with different types of COVID-19","volume":"25","author":"Wang","year":"2020","journal-title":"Ann. Noninvasive Electrocardiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.1002\/clc.23492","article-title":"Risk factors and electrocardiogram characteristics for mortality in critical inpatients with COVID-19","volume":"43","author":"Li","year":"2020","journal-title":"Clin. Cardiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e4031","DOI":"10.1093\/cid\/ciaa1578","article-title":"QTc Interval Prolongation and Life-Threatening Arrhythmias During Hospitalization in Patients with Coronavirus Disease 2019 (COVID-19): Results from a Multicenter Prospective Registry","volume":"73","author":"Santoro","year":"2021","journal-title":"Clin. Infect. Dis."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105335","DOI":"10.1016\/j.compbiomed.2022.105335","article-title":"Attention-based 3D CNN with residual connections for efficient ECG-based COVID-19 detection","volume":"143","author":"Sobahi","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13755-021-00169-1","article-title":"COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network","volume":"10","author":"Rahman","year":"2022","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Du, K.L., and Swamy, M.N.S. (2019). Neural Networks and Statistical Learning, Springer.","DOI":"10.1007\/978-1-4471-7452-3"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3410","DOI":"10.1049\/iet-com.2020.0630","article-title":"Congestion-aware adaptive decentralised computation offloading and caching for multi-access edge computing networks","volume":"14","author":"Tefera","year":"2020","journal-title":"IET Commun."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.1109\/JSAC.2018.2869954","article-title":"Follow me at the edge: Mobility-aware dynamic service placement for mobile edge computing","volume":"36","author":"Ouyang","year":"2018","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.pmcj.2018.08.002","article-title":"Machine-to-machine wireless communication technologies for the Internet of Things: Taxonomy, comparison and open issues","volume":"50","author":"Montori","year":"2018","journal-title":"Pervasive Mob. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Imoize, A.L., Adedeji, O., Tandiya, N., and Shetty, S. (2021). 6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap. Sensors, 21.","DOI":"10.3390\/s21051709"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/MWC.2018.1800365","article-title":"Optimizing space-air-ground integrated networks by artificial intelligence","volume":"26","author":"Kato","year":"2019","journal-title":"IEEE Wirel. Commun."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1109\/COMST.2021.3067807","article-title":"Survey on network slicing for Internet of Things realization in 5G networks","volume":"23","author":"Wijethilaka","year":"2021","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"32258","DOI":"10.1109\/ACCESS.2018.2846609","article-title":"UbeHealth: A personalized ubiquitous cloud and edge-enabled networked healthcare system for smart cities","volume":"6","author":"Muhammed","year":"2018","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1109\/COMST.2015.2444095","article-title":"Internet of things: A survey on enabling technologies, protocols, and applications","volume":"17","author":"Guizani","year":"2015","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"9552","DOI":"10.1109\/JIOT.2020.2993782","article-title":"Flowguard: An intelligent edge defense mechanism against IoT DDoS attacks","volume":"7","author":"Jia","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Qasim, H.M., Ata, O., Ansari, M.A., Alomary, M.N., Alghamdi, S., and Almehmadi, M. (2021). Hybrid feature selection framework for the Parkinson imbalanced dataset prediction problem. Medicina, 57.","DOI":"10.3390\/medicina57111217"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Jeon, H., and Oh, S. (2020). Hybrid-recursive feature elimination for efficient feature selection. Appl. Sci., 10.","DOI":"10.3390\/app10093211"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Yang, X., He, X., Liang, Y., Yang, Y., Zhang, S., and Xie, P. (2020). Transfer learning or self-supervised learning? A tale of two pretraining paradigms. arXiv.","DOI":"10.36227\/techrxiv.12502298.v1"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.artmed.2017.02.005","article-title":"A novel hierarchical selective ensemble classifier with bioinformatics application","volume":"83","author":"Wei","year":"2017","journal-title":"Artif. Intell. Med."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.eswa.2019.01.066","article-title":"Enhancing batch normalized convolutional networks using displaced rectifier linear units: A systematic comparative study","volume":"124","author":"Zanchettin","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1109\/JSAC.2020.3020599","article-title":"Smart mutual authentication protocol for cloud based medical healthcare systems using internet of medical things","volume":"39","author":"Deebak","year":"2020","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/IOTM.0001.2000123","article-title":"Privacy-aware energy-efficient framework using the internet of medical things for COVID-19","volume":"3","author":"Deebak","year":"2020","journal-title":"IEEE Internet Things Mag."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"102274","DOI":"10.1016\/j.scs.2020.102274","article-title":"A novel community-based trust aware recommender systems for big data cloud service networks","volume":"61","author":"Deebak","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Siarry, P., Jabbar, M.A., Aluvalu, R., Abraham, A., and Madureira, A. (2021). The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care, Springer.","DOI":"10.1007\/978-3-030-75220-0"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"10250","DOI":"10.1109\/JIOT.2020.2997651","article-title":"An in-depth analysis of IoT security requirements, challenges, and their countermeasures via software-defined security","volume":"7","author":"Iqbal","year":"2020","journal-title":"IEEE Internet Things J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/2995\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:51:39Z","timestamp":1760122299000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/2995"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,10]]},"references-count":52,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23062995"],"URL":"https:\/\/doi.org\/10.3390\/s23062995","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,10]]}}}