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The recent advancement in information technology and evolving of the (IoT) shifted the traditional medical approach to patient-oriented approach (e.g., Telemedicine/Telemonitoring). IoT permits several services including sensing, processing and communicating information with physical and bio-medical constraints. Wireless Body Area Network (WBAN) handles the issues pertaining to the medical purposes in the form of sensor nodes and connected network. The WBAN takes human physiological data as an input to subsequently monitor the patient conditions that are transferred to other IoT components for analysis. Such monitoring and analysis demand a cohesive routing approach to ensure the safe and in-time transfer of data. The temperature rise of bio-medical sensor nodes makes the entire routing operation very crucial because the temperature of implanted nodes rises and ultimately damages body tissues. This needs dispersion in data transmission among different nodes by opting various available routes while avoiding temperature rise. In this paper, we present Adaptive Thermal-Aware Routing algorithm for WBAN. The ATAR is designed to overcome the temperature rise issue of implanted bio-medical sensors nodes. The new protocol is based on Multi-Ring Routing approach to find an alternative route in the case of increasing temperature. The simulation results indicate that proposed protocol is more efficient in terms of temperature rise and throughput than existing approaches.

COVID-19 has infected 223 countries and caused 2.8 million deaths worldwide (at the time of writing this article), and the death rate is increasing continuously. Early diagnosis of COVID patients is a critical challenge for medical practitioners, governments, organizations, and countries to overcome the rapid spread of the deadly virus in any geographical area. In this situation, the previous epidemic evidence on Machine Learning (ML) and Deep Learning (DL) techniques encouraged the researchers to play a significant role in detecting COVID-19. Similarly, the rising scope of ML/DL methodologies in the medical domain also advocates its significant role in COVID-19 detection. This systematic review presents ML and DL techniques practiced in this era to predict, diagnose, classify, and detect the coronavirus. In this study, the data was retrieved from three prevalent full-text archives, i.e., Science Direct, Web of Science, and PubMed, using the search code strategy on 16 March 2021. Using professional assessment, among 961 articles retrieved by an initial query, only 40 articles focusing on ML/DL-based COVID-19 detection schemes were selected. Findings have been presented as a country-wise distribution of publications, article frequency, various data collection, analyzed datasets, sample sizes, and applied ML/DL techniques. Precisely, this study reveals that ML/DL technique accuracy lay between 80% to 100% when detecting COVID-19. The RT-PCR-based model with Support Vector Machine (SVM) exhibited the lowest accuracy (80%), whereas the X-ray-based model achieved the highest accuracy (99.7%) using a deep convolutional neural network. However, current studies have shown that an anal swab test is super accurate to detect the virus. Moreover, this review addresses the limitations of COVID-19 detection along with the detailed discussion of the prevailing challenges and future research directions, which eventually highlight outstanding issues.

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