IOT Adoption in Healthcare: Security Concerns and Fog Computing Solutions

Literature review:
This section of the paper will discuss and compare different perspectives of several
authors who published their literature in the context of IOT adoption in healthcare. Some
literatures provides useful information which can assist to understand the adoption of IOT in
healthcare and issues around the adoption (Laplante & Laplante, 2016). In the healthcare sector,
IOT has been developed in multiple key areas and some of the applications likewise remote
monitoring and smart sensors are providing optimal value. Even though, the development in the
health care center is rapidly involving, some critical issues is also occurring while development
process (Zhou & Piramuthu, 2018). Today’s technologies provide first transmission of data but
the security concerns are still cannot be ignored. Many literatures provides their explanation
about certain security measures which have massive impact on the healthcare.
IOT based healthcare system:
Advanced IOT application development and widespread adoption of such devices, have
generate multiple ways to improve healthcare system with numerous advantages as accessibility
and cost effective. The adoption of IOT in healthcare system have alter many typical operation
by enabling multiple functionality with better efficiency. Nowadays, IOT applications are
enabling remote monitoring and personalization and experts believe that it would provide more
optimal value in near future (Gope & Hwang, 2016). An increasing number of smart medical
devices and sensors used in IoT-driven healthcare vision implies the fast generation of huge
amounts of diverse data. In order to successfully deal with increasing amounts of excessive
diversity of information and high speed of data generation and processing it is required to deal with
security and technical concerns. The IOT applications are showing promising results since it
enables self-managing disease and enables remote care and assistance through associated
networked monitoring equipment. However, some critical security issues may appear in the

healthcare. Most of the healthcare center, stores the patients crucial data into their own database.
These dataset contains valuable information associated with the patients personal and health
history (Bhatt, Dey & Ashour, 2017). As these technologies are engaging with the healthcare
sectors, this devices are storing huge amount of data in them. For instance, smart wear devices
generate health report of the user and also stores this data in them or transfer to cloud. This data
can be easily compromised as not mush of security measures are implemented in those
technologies. This type of issues are hindering the rapid development of the IOT in health care
sector (Laplante & Laplante, 2016). However, there are many research in active state to
implement of an additional layer between cloud and those devices for faster computing and
connectivity. This type of layer are generally refers as fog computing and offer multiple potential
as well. The fog computing is one of the hottest topic in the IOT development as it can provide
enhance security with time sensitivity (Lee & Lee, 2016). However, fog infrastructure can be
utilized up to the full potential until the healthcare infrastructure overcome the issues for its
implementation.
Fog computing:
Multiple author identified Fog computing as a solution of the security and privacy
concerns associated with the IOT development in the healthcare sectors. They also proposed
some simple computing layer in an IOT system. This information are presented as key area in
this section. Fog computing is an innovative trend in computing established by cisco that
spreads the cloud computing archetype at the edge of the network as processing data around data
source. Fog computing also enable Knowledge generation and data analytics features which
compressed the data volume, need to transfer to cloud (Laplante & Laplante, 2016). Generally, in
cloud computing applications and information are processed in the cloud which is very time

consuming for vast amount of data. As a solution, fog computing placed on the edge of a
network take considerably less time to process the similar tasks. However. Fog computing
cannot work on his own as it cannot replace the cloud computing. It just a solution of cloud
computing limitation (Moosavi et al., 2016). According to the Elhoseny, fog computing is the
most appropriate application compared with other solutions especially for Large-scale distributed
control systems, mobile applications such as smart connected rail or connected vehicle.
Geographically distributed sensor/actuator networks - applications in which thousands or
millions of things across an enormous geographic area are producing data and applications that
involve with very low and predictable expectancy like various emergency response applications
and health-monitoring (Elhoseny et al., 2018). The smart devices and sensors utilized in the
healthcare industry for monitoring the patient’s health status have limited amount of storage
capability and computation. This devices also generates vast amount of data which needs to be
secured from third parties (Bhatt, Dey & Ashour, 2017).
In IoT-powered healthcare applications, event response and real-time processing are vital.
Fog computing enables real-time and online analytic even when connectivity is poor or lost with
the Cloud, and implies less congestion and faster real time interaction and optimizations for IoT
devices what makes it perfect for utilization in IoT-based healthcare systems (Catarinucci et al.,
2015). Author proposed a prototype of the Iot based healthcare system with proper encryption to
the fog node. Generally, the health related data such as patients’ heart rate, blood pressure,
weight and body temperature are directly send to the cloud from the smart devices or sensors as
these type of technologies does not contains sufficient amount of data storage and power source
because of the slim design. Between the smart devices and the cloud there is no security
measures (Da et al., 2014). Fog nodes can be implemented between the devices and cloud to