Real-time FOG computing healthcare monitoring Jared Lynskey, and Choong Seon Hong*

Department of Computer Engineering, Kyung Hee University {jared, cshong}@khu.ac.kr

Abstract – In today’s society, consumers are demanding a richer experience in health and wellbeing smart devices as a way to improve quality of life. As result demand for network resources to stream data via the internet is predicted to increase ten-fold. Therefore, to provide such a real-time health care monitoring service in constrained environments, techniques to adequately distribute and decentralize streams of user device data is essential. This paper proposes two new techniques to manage the flow of data by analyzing the values and generating importance scores to intervals of data.

1. Introduction United nations predict the human population will surpass 9.7 billion by 2050 with 66% of the total population living in urban areas [1]. To date there is approximately 37 megacities across the world each with a population greater than 10 million [2]. While in 2008 the number of IoT devices connected to the internet had surpassed the total human population, approximately 3 devices per person on average. This includes smart wearables that can monitor health real time.

Figure 1: There is three connected devices for one person

One major challenge that arises from the expansion in the population and IoT devices is ensuring adequate network resources are continuously available for real-time healthcare monitoring systems.

Previous research conducted in the field of health care monitoring has proven concept with by implementing a single server real-

time health monitoring system but did not consider the to decentralize the services at the network edge to. [3]

2. System Design The purpose of a real-time healthcare monitoring system with the help of FOG networking is to transform the process of handling generated data from smart devices. Improving real-time health monitoring with lower latency and reduced congestion by allowing storage and computational tasks to be completed at the edge of the network in proximity to users [4]

The following proposed ideas are implemented at each edge node responsible for many user’s smart wearable devices such as ‘edge node 2’ in figure 2. The benefit of this over traditional systems is the reduction in data streams flowing through the internet.

Figure 2: Hierarchy of the decentralized real-time healthcare monitoring

Since FOG computing can be scaled on one to many edge nodes for simplicity figure 2 only presents a small portion of the design.

3. Real time data management between users

and FOG nodes

Real time data streams generated can come

from a variety of healthcare devices belonging

to users, including but not limited to body

temperature sensor, heart rate sensor and

Application

Edge Node 1

Health Professional

Edge Node 2

User

User Edge Node

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oxygen content in blood. When data is

received from a device belong to a new user,

the fog node can begin to create a profile of

the user to generate mean values based on the

user’s devices, which future real-time data will

be compared against.

One major issue with constant streams of data

is that the resources on FOG nodes, especially

storage, are limited. Therefore, constraints are

required to minimize the outflow of data to the

main application via the internet, while

maximizing the value of the data by maintain

the most recent and important data at the

edge node.

This is solved by creating an importance score

system based on the degree of variance from

individual user sensors, and the number of

requests during the interval.

𝐾𝑖 = √(μ − ε)2 ∗ (1 + n)

In the above equation, μ is the overall mean

for the sensor since the beginning of time. Ε is

the timed interval, n is the number of requests

made within the time interval and K is the

importance score for the interval set.

To calculate the variance, such as pulse, the

absolute value is taken from the difference

between the time interval mean and overall

mean.

Variance can provide clues to the likelihood

that the user may request to view the data. For

instance, variance or perceived changes to

one’s own state may cause a natural response

to review the reported data, generally people

aren’t concerned about their health if it’s

ordinary.

Secondly, based on the idea that humans are a

creature of habit, the number of requests for

real time data by the user in an interval should

be considered to calculate the final importance

score.

Thus, if n is large for a given interval, this can

indicate a strong likelihood the user will

request to interact with the data set further.

Furthermore, timeliness is considered in order

to ensure that the most recent data is kept at

the fog node. This is factored in by deducting a

portion of the importance score each

subsequent day the data remains on the fog

node as per the following

K = 𝑑𝐼(i) = K − 𝑑𝑖

In the above equation, 𝐾𝑖−1 is the most recent

importance score and Ki is the new importance

score. di is the fine imposed.

When data being stored is predicted to surpass

available storage on the fog node. Data

beginning with the lowest importance score is

scheduled to be archived first.

Thus, the ‘good health’ regular health state

data is likely to archived first. Reducing the

likely number of requests for archived data

from the application in the cloud.

4. Real time emergency response FOG to FOG.

A sudden prolong change in the real time data against the user’s mean could indicate a medical emergency. For example, a sudden increase in blood pressure with little to no physical activity recorded by a smart wearable.

T < ∑ √(μ − ε)2

This is where a risk threshold known as T is introduced. In the case that the risk threshold constant T is exceeded by one or more sensors, the user’s edge node responses by sending real-time data from the user’s to the hospital fog node, allowing the data to be further analyzed by a health professional to determine the need for emergency services.

User Edge Node Application Edge Node

Health Professional

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5. Conclusion

In this paper, we proposed two new ideas that allows for intelligent management of data at the edge FOG node for a decentralized real-time health monitoring system. 6. Future Work Future work in the area includes considering a practical implementation and deployment of the proposed algorithms in a real time health care monitoring system. 7. Acknowledgements This research was supported by the MSIP(Ministry of Science, ICT and Future Planning), Korea, under the Grand Information Technology Research Center support program (IITP-2017-2015-0-00742) supervised by the IITP(Institute for Information & communications Technology Promotion)" *Dr. CS Hong is the corresponding author References

[1] United Nations, "World Population

Prospects," 2014. [Online]. Available:

http://www.un.org/en/development/des

a/news/population/world-urbanization-

prospects-2014.html. [Accessed 2017].

[2] N. S. Magazine, "How Big Can Cities

Get?," no. 17 June 2006, page 41, 2006.

[3] O. A. M. O. T. H. a. N. Ţ. R. T. Hameed,

"Patient monitoring system based on e-

health sensors and web services," in 8th

International Conference on Electronics,

Computers and Artificial Intelligence

(ECAI), Ploiesti, Romania, 2016.

[4] J. Bar-Magen Numhauser, in Fog

Computing introduction to a New Cloud

Evolution., Spain, University of Alcala,

2013, p. 111–126.

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