EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats
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...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/6/2995 |
| _version_ | 1797609146916274176 |
|---|---|
| author | B. D. Deebak Fadi Al-Turjman |
| author_facet | B. D. Deebak Fadi Al-Turjman |
| author_sort | B. D. Deebak |
| collection | DOAJ |
| description | 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’s 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>~</mo><mn>98.3</mn><mo>%</mo></mrow></semantics></math></inline-formula>) 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. |
| first_indexed | 2024-03-11T05:56:27Z |
| format | Article |
| id | doaj.art-a447b21b16c1476e982e6fce8ed39a92 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:56:27Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a447b21b16c1476e982e6fce8ed39a922023-11-17T13:44:27ZengMDPI AGSensors1424-82202023-03-01236299510.3390/s23062995EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 ThreatsB. D. Deebak0Fadi Al-Turjman1Department of Computer Engineering, Gachon University, Gyeonggido, Seongnam 13120, Republic of KoreaArtificial Intelligence Engineering Deptartment, AI and Robotics Institute, Near East University, Mersin 10, TurkeyCoronavirus 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’s 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>~</mo><mn>98.3</mn><mo>%</mo></mrow></semantics></math></inline-formula>) 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.https://www.mdpi.com/1424-8220/23/6/2995COVID-19internet of thingsEdgeIoT technologiespower consumption |
| spellingShingle | B. D. Deebak Fadi Al-Turjman EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats Sensors COVID-19 internet of things Edge IoT technologies power consumption |
| title | EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats |
| title_full | EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats |
| title_fullStr | EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats |
| title_full_unstemmed | EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats |
| title_short | EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats |
| title_sort | eei iot edge enabled intelligent iot framework for early detection of covid 19 threats |
| topic | COVID-19 internet of things Edge IoT technologies power consumption |
| url | https://www.mdpi.com/1424-8220/23/6/2995 |
| work_keys_str_mv | AT bddeebak eeiiotedgeenabledintelligentiotframeworkforearlydetectionofcovid19threats AT fadialturjman eeiiotedgeenabledintelligentiotframeworkforearlydetectionofcovid19threats |