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Artificial Intelligence (AI) has surpassed expectations in opening up different possibilities for machines from different walks of life. Cloud service providers are pushing. Edge computing reduces latency, improving availability and saving bandwidth.

The exponential growth in tensor processing unit (TPU) and graphics processing unit (GPU) combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care. A significant role of pushing and pulling data from the cloud, big data comes into play as velocity, veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record (EHR).

The primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence (PoP). The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients. The impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.

The utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the ODL. The scalability is at 50% with respect to the sensitivity and preservation of the PII values in the local ODL.

Inclusion of mobile nodes (MNs) in Internet of Things (IoT) further increases the challenges such as frequent network disconnection and intermittent connectivity because of high mobility rate of nodes. This paper aims to propose a proactive mobility and congestion aware route prediction mechanism (PMCAR) to find the congestion free route from leaf to destination oriented directed acyclic graph root (DODAG-ROOT) which considers number of MNs connected to a static node. This paper compares the proposed technique (PMCAR) with RPL (OF0) which considers the HOP-COUNT to determine the path from leaf to DODAG-ROOT. The authors performed a simulation with the proposed technique in MATLAB to present the benefits in terms of packet loss and energy consumption.

In this pandemic situation, mobile and IoT play major role in predicting and preventing the CoronaVirus Disease of 2019 (COVID-19). Huge amount of computations is happening with the data generated in this pandemic with the help of mobile devices. To route the data to remote locations through the network, it is necessary to have proper routing mechanism without congestion. In this paper, PMCAR mechanism is introduced to achieve the same. Internet of mobile Things (IoMT) is an extension of IoT that consists of static embedded devices and sensors. IoMT includes MNs which sense data and transfer it to the DODAG-ROOT. The nodes in the IoMT are characterised by low power, low memory, low computing power and low bandwidth support. Several challenges are encountered by routing protocols defined for IPV6 over low power wireless personal area networks to ensure reduced packet loss, less delay, less energy consumption and guaranteed quality of service.

The results obtained shows a significant improvement compared to the existing approach such as RPL (OF0). The proposed route prediction mechanism can be applied largely to medical applications which are delay sensitive, particularly in pandemic situations where the number of patients involved and the data gathered from them flows towards a central root for analysis. Support of data transmission from the patients to the doctors without much delay and packet loss will make the response or decisions available more quickly which is a vital part of medical applications.

The computational technologies in this COVID-19 pandemic situation needs timely data for computation without delay. IoMT is enabled with various devices such as mobile, sensors and wearable devices. These devices are dedicated for collecting the data from the patients or any objects from different geographical location based on the predetermined time intervals. Timely delivery of data is essential for accurate computation. So, it is necessary to have a routing mechanism without delay and congestion to handle this pandemic situation. The proposed PMCAR mechanism ensures the reliable delivery of data for immediate computation which can be used to make decisions in preventing and prediction.