S-Nav: Safety-Aware IoT Navigation Tool for Avoiding COVID-19 Hotspots

In this article, we present a [Formula Omitted]-learning-enabled safe navigation system—S-Nav—that recommends routes in a road network by minimizing traveling through categorically demarcated COVID-19 hotspots. S-Nav takes the source and destination as inputs from the commuters and recommends a safe path for traveling. The S-Nav system dodges hotspots and ensures minimal passage through them in unavoidable situations. This feature of S-Nav reduces the commuter's risk of getting exposed to these contaminated zones and contracting the virus. To achieve this, we formulate the reward function for the reinforcement learning model by imposing zone-based penalties and demonstrate that S-Nav achieves convergence under all conditions. To ensure real-time results, we propose an Internet of Things (IoT)-based architecture by incorporating the cloud and fog computing paradigms. While the cloud is responsible for training on large road networks, the geographically aware fog nodes take the results from the cloud and retrain them based on smaller road networks. Through extensive implementation and experiments, we observe that S-Nav recommends reliable paths in near real time. In contrast to state-of-the-art techniques, S-Nav limits passage through red/orange zones to almost 2% and close to 100% through green zones. However, we observe 18% additional travel distances compared to precarious shortest paths.

Estimating Time Window to Administer Anti-cytokine Therapeutic Drugs to COVID-19 Patients.

The severe COVID-19 infection often leads to "Cytokine Release Syndrome (CRS)", which is a serious adverse medical condition causing multiple organ failures. Anti-cytokine therapy has shown promising results for the treatment of the CRS. As part of the anti-cytokine therapy, the immuno-suppressants or anti-inflammatory drugs are infused to block the release of cytokine molecules. However, determining the time window to infuse the required dose of drugs is challenging due to the complex processes involving the release of inflammatory markers, such as IL-6 and C-reactive protein (CRP) molecules. In this work, we develop a molecular communication channel to model the transmission, propagation, and reception of cytokine molecules. The proposed analytical model can be used as a framework to estimate the time window to administer anti-cytokine drugs to get successful outcomes. Simulation results show that at a 50s-1 release rate of IL-6 molecules, the cytokine storm is triggered at ~ 10 hours, and consequently, the CRP molecules reach the severe level of 97 mg/L at ~ 20 hours. Further, the results reveal that with one-half of the release rate of IL-6 molecules, the time to observe the severe level of 97 mg/L CRP molecules increases by 50%.

i-Sheet: A Low-Cost Bedsheet Sensor for Remote Diagnosis of Isolated Individuals.

In this article, we propose a smart bedsheet-i-Sheet-for remotely monitoring the health of COVID-19 patients. Typically, real-time health monitoring is very crucial for COVID-19 patients to prevent their health from deteriorating. Conventional healthcare monitoring systems are manual and require patient input to start monitoring health. However, it is difficult for the patients to give input in critical conditions as well as at night. For instance, if the oxygen saturation level decreases during sleep, then it is difficult to monitor. Furthermore, there is a need for a system that monitors post-COVID effects as various vitals get affected, and there are chances of their failure even after the recovery. i-Sheet exploits these features and provides the health monitoring of COVID-19 patients based on their pressure on the bedsheet. It works in three phases: 1) sensing the pressure exerted by the patient on the bedsheet; 2) categorizing the data into groups (comfortable and uncomfortable) based on the fluctuations in the data; and 3) alerting the caregiver about the condition of the patient. Experimental results demonstrate the effectiveness of i-Sheet in monitoring the health of the patient. i-Sheet effectively categorizes the condition of the patient with an accuracy of 99.3% and utilizes 17.5 W of the power. Furthermore, the delay involved in monitoring the health of patients using i-Sheet is 2 s which is very diminutive and is acceptable.

Guest Editorial Special Issue on “Edge-Based Wireless Communications Technologies to Counter Communicable Infectious Diseases”

The COVID-19 pandemic has resulted in one of the major challenges for humanity in the 21st century. The impact of these challenges has led to a tremendous loss of life, impact on long-term health, well-being as well as personal psychology, and negative societal changes and not to mention its impact on the global economy. Since this is a health issue, similar to other forms of diseases and pandemics, society has largely relied on the fields of medical, virology, immunology, biotechnology, and pharmaceutical science to develop novel therapeutic solutions for treatments. This has resulted in vaccines that have been rolled out to elevate immunity levels that will hopefully allow the majority of the population to reach herd immunity. However, given the technological advancements that we have reached in the 21st century, questions have also risen as to how other disciplines can play a role in solving and obtaining new knowledge of communicable disease pandemics.

VIVID: In Vivo End-to-End Molecular Communication Model for COVID-19.

As an alternative to ongoing efforts for vaccine development, scientists are exploring novel approaches to provide innovative therapeutics, such as nanoparticle- and stem cell-based treatments. Thus, understanding the transmission and propagation dynamics of coronavirus inside the respiratory system has attracted researchers' attention. In this work, we model the transmission and propagation of coronavirus inside the respiratory tract, starting from the nasal area to alveoli using molecular communication theory. We performed experiments using COMSOL, a finite-element multiphysics simulation software, and Python-based simulations to analyze the end-to-end communication model in terms of path loss, delay, and gain. The analytical results show the correlation between the channel characteristics and pathophysiological properties of coronavirus. For the initial 50% of the maximum production rate of virus particles, the path loss increases more than 16 times than the remaining 50%. The delayed response of the immune system and increase in the absorption of virus particles inside the respiratory tract delay the arrival of virus particles at the alveoli. Furthermore, the results reveal that the virus load is more in case of asthmatic patients as compared to the normal subjects.