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Fog, Edge and Pervasive Computing in Intelligent Internet of Things Driven Applications in Healthcare: Challenges, Limitations and Future Use
Afroj Alam1, Sahar Qazi2, Naiyar Iqbal3, and Khalid Raza2*
1Department of Computer Science, Integral University, Lucknow, India
2Department of Computer Science, Jamia Millia Islamia, New Delhi
3Department of Computer Science and IT, Maulana Azad National Urdu University, Hyderabad
1.1 Introduction
Today, the Internet of Things (IoT), fog, edge and pervasive computing are buzzwords, which have pivotal applications in different fields of studies including healthcare, engineering, and other intelligent applications. Cloud computing and the IoT have emerged as a new paradigm in the field of information and communication technology (ICT) as a revolution of the 21st century. It was a longâawaited dream of to use computing as a utility. Traditional computing extends the model to a cloud computing paradigm which has the capability to renovate a huge portion of the information technology industry, making the software even more interesting as a service that customers can access onâdemand. The IoT acts as an interconnection between various gadgets and the Internet, including mobile phones, vehicles, farms, factories, home automation systems, and wearable devices from the viewpoint of the enhancement of the competence of realâlife computing usage. This new technology, especially in the healthcare sector, is a change from the conventional approach of visiting clinics or hospitals. It links doctors, patients, and nurses by means of intelligent, affordable sensor gadgets with the support of cloud computing (Qi et al., 2019). Unfortunately, a number of IoT based intelligent sensor gadgets are developing at a rapid rate. On the basis of evaluation, if the pace of extension proceeds constantly from 2020, the number of wearable gadgets on the planet will reach to around 26 billion (Imran and Qadeer, 2019). The volume of data generated using these IoT gadgets is very large. The capability of the present cloud model is not adequate to deal with the requirements of the IoT, i.e., the current cloud has issues regarding volume, latency, and bandwidth. The current cloud cannot fulfill every one of the prerequisites of QoS (Quality of Service) in the IoT, therefore the goal is that another framework, fog computing, is introduced that will solve the issues of volume, latency and bandwidth (Shi et al., 2015).
Fog computing has appeared with a new computation model which is placed between the cloud and intelligence sensorâbased IoT devices through which an assortment of heterogeneous gadgets are pervasively associated as the terminal of a network which provides communication facilities to ease the execution of relevant IoT services (Chang et al., 2019). Fog computing covers the cloud computing approach in the direction of the edge of the network, which has many advantages over cloud computing. Fog computing is appropriate for the applications by which realâtime, high response time, and less latency are important issues, specifically in healthcare utilization (Mutlag et al., 2019). It is enabling new or mutated applications and facilities with a productive transaction between cloud and fog, especially with the issues of volume, latency, and bandwidth regarding data management (JoSEP et al., 2010).
In this chapter, we propose to explain new trends of computing models to understand the evolving IoT applications, exclusively fog and edge computing, their background, features, model architecture and current challenges. This chapter also covers various problems and challenges that have been faced by the practitioners in previous years in the field of cloud computing associated with the IoT that has been solved by fog, edge and pervasive computing (De Donno et al., 2019). Further, because the Cybercrime Report 2016 suggests that cybercrime damages will be around $6 trillion every year by 2021, up from $3 trillion in 2015 it will cover how to secure the privacy of IoT based sensor devices and private data in the cloud using machine learning. Further, we will demonstrate in this chapter that fog computing definitely reduces latency as opposed to cloud computing. The low latency is significant for the medical IoT framework because of realâtime requirements. Although the Cloudâbased IoT (CIoT) structure is a typical way to deal with executing IoT frameworks, it is, however, confronting developing difficulties in the IoT. Specifically, CIoT deals with current challenges such as data transmission rate, latency rate, interruption, limitation of resource and secure system. The developing difficulties of CIoT have brought up an issue â what is needed to conquer the barrier of current cloudâdriven architecture? Fog computing architecture is a visionary model that includes all probabilities to encompass the cloud to the edge network of CIoT, from the distant central cloud datacenter, the interim system hubs to the far edge where the frontâend IoT gadgets are situated.
