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Recently, with the rising number of chronic diseases along with the rise in global population, researchers as well as healthcare industry are with a great hope of new smart healthcare solutions which can prove to be beneficial for both medical practitioners as well as patients in providing real-time services in case of medical emergencies. Despite of the fact that smart healthcare solutions can prove to be rewarding, yet, industries did not opt for such smart healthcare systems due to the exorbitant nature of IoT solutions. But latterly, in the age of pandemic like coronavirus, it has become a necessity for the industries to adopt such IoT based smart solutions for the betterment of humankind.
The Internet of Things (IoT) is an environment of diverse objects having capabilities to inter-operate and communicate with each other over the network requiring minimum human interaction (Wortmann & Flüchter, 2015). In situations, when there is a need of processing large amount of data which requires high computing power, IoT devices alone cannot satisfy the requirements, hence uses Cloud to meet such requirements (Biswas & Giaffreda, 2014). Cloud computing (Mell & Grance, 2011) is a virtualization based powerful computing technology which provides on-demand services such as computing power, storage, applications etc over the internet. Notwithstanding the various benefits of Cloud, several inescapable issues and downside is also noticed. Cloud datacenters located far away from the users and the presence of high traffic in Cloud results in high latency, raising questions about execution of tasks requiring immediate responses. There comes the need of Fog.
Fog computing, is an intermediate layer of distributed network present in between the Cloud and IoT which is capable of providing Cloud services with a minimal delay to the real-time IoT applications, being close to the source of data (Bonomi et al., 2012). The need for Fog is better realized in healthcare systems, when large amount of requests arrives from delay-sensitive applications like wearable health monitoring devices, ventilators etc. In situations of pandemic or disasters, when there is an unpredictable surge in the number of patients, the requests may also increase vigorously. In such circumstances when there is lack of an efficient task scheduling model, fog computing circumscribed by resources may not be able to handle the high traffic and may fail to serve the delay-sensitive tasks which requires immediate response.