An Intelligent and Energy-Efficient Wireless Body Area Network to Control Coronavirus Outbreak

Abstract

The coronaviruses are a deadly family of epidemic viruses that can spread from one individual to another very quickly, infecting masses. The literature on epidemics indicates that the early diagnosis of a coronavirus infection can lead to a reduction in mortality rates. To prevent coronavirus disease 2019 (COVID-19) from spreading, the regular identification and monitoring of infected patients are needed. In this regard, wireless body area networks (WBANs) can be used in conjunction with machine learning and the Internet of Things (IoT) to identify and monitor the human body for health-related information, which in turn can aid in the early diagnosis of diseases. This paper proposes a novel coronavirus-body area network (CoV-BAN) model based on IoT technology as a real-time health monitoring system for the detection of the early stages of coronavirus infection using a number of wearable biosensors to examine the health status of the patient. The proposed CoV-BAN model is tested with five machine learning-based classification methods, including random forest, logistic regression, Naive Bayes, support vector machine and multi-layer perceptron classifiers, to optimize the accuracy of the diagnosis of COVID-19. For the long-term sustainability of the sensor devices, the development of energy-efficient WBAN is critical. To address this issue, a long-range (LoRa)-based IoT program is used to receive biosensor signals from the patient and transmit them to the cloud directly for monitoring. The experimental results indicate that the proposed model using the random forest classifier outperforms models using the other classifiers, with an average accuracy of 88.6%. In addition, power consumption is reduced when LoRa technology is used as a relay node.

RETRACTED: A wireless body area network enabled machine learning-assisted biosensor framework for detecting and analyzing ventilator and hospital-acquired pneumonia using the internet of things

“…The WBAN systems' ability to share sensor-collected data efficiently depends on their selection of the right wireless technology. The Future Trends of WBAN in Artificial Intelligence to control coronavirus outbreak was given by Naveen Bilandi et al [42]. Health-related data may be gleaned from the human body using WBAN, IoT, ML and DL, which can help in the early detection of illnesses.…”

IoT for Global Development to Achieve the United Nations Sustainable Development Goals: The New Scenario After the COVID-19 Pandemic

“…The body parameters collected from the subject were compared with the data of normal non-infected humans using an Artificial Neural Network algorithm. Of the 6 works reviewed, Artificial Neural Network was the most used technique (109-111, 119), followed by Support Vector Machines (110,111,117).…”

“…LoRa has emerged as the second alternative, for transmitting data, used in the reviewed works. Of the 20 works reviewed, 15 proposals were classified within the IoT Sector 1(102,103,(105)(106)(107)(108)(109)(110)(111)(112)(113)(114)(115)119,121); 13 works aligned with the SDG3(102,103,(105)(106)(107)(108)(109)(110)(111)(112)(113)(114)(115). Southern Asia was identified as the region with the most IoT4D projects based on low-cost technology deployed in the wake of COVID-19.…”

AIoT Used for COVID-19 Pandemic Prevention and Control

“…From another point of view, this method still has the problem of insufficient sample size and needs to be expanded. Bilandi et al [ 35 ] put an intelligent, energy-efficient WBAN model for the diagnosis and monitoring of COVID-19 patients and another designed to classify COVID-19 patients with common cold. The proposed LoRa technology architecture is drawn in Figure 7 .…”