The document discusses an intelligent architectural framework for fog computing aimed at enhancing patient monitoring in healthcare applications. It highlights the use of fog computing as an intermediary for real-time data processing to improve decision-making and reduce response times, especially in critical healthcare scenarios. The proposed system integrates various components, such as application and cloud layers, to effectively capture and analyze vital physiological data from patients.

1. An Intelligent Architectural
Framework for Fog Computing
Supported IoT Applications
F. Mohamed shakir
Department of Information Technology
Thiagarajar College of Engineering

2. MOTIVATION
 In healthcare, the patient monitoring is one of the pivotal application
as it deals with human life.
 The patient physiological parameters like Heart rate, Pulse rate,
Temperature, Blood pressure are monitored to know the condition
of the patient.
 The criticality are occurred only if these parameters are not known
to the physician.

3. PROBLEM STATMENT
 To detect emergencies and inform medical personnel about the
status of the patient. Physician has to frequently visit the patient
and asses the parameter like Temperature ,Blood pressure , Pulse
and Heart rate to know the current status of the patient.
 To avoid this problem, data are gathered from patient and
transmitted to fog layer for quick processing rather than sending
huge data to the cloud.

4. FOG COMPUTING
 Fog computing is the computing paradigm, which extends cloud nearer to
the devices. The primary aim of fog is to solve the problems faced by the
cloud computing during the data processing.
 The characteristics of fog computing are
◦ Edge location
◦ Mobility support
◦ Real time interaction
◦ Large number of nodes
 The advantage of fog computing are
◦ Low latency
◦ Quick decision making
◦ Store confidential data on local servers

5. LITERATURE SURVEY
Title of the paper
Fog Assisted-IoT Enabled Patient Health
Monitoring in Smart Homes
Year of Publication 2018
Author Names Prabal Verma and Sandeep K. Sood
Source IEEE Internet of Things Volume:5
Extract of the paper
In this paper they used fog computing at the gateway.
Event triggering based data transmission methodology is
used to process real time patient’s data. For the patient
classification they used Bayesian Belief Network.
Findings
The issue in this paper is, Information that is to be
delivered to the responder from the cloud layer is
challenging. Issues in sensor can make difficulty in
capturing the data

6. CONT..
Title of the paper
Fog Based Intelligent Transportation Big Data
Analytics in The Internet of Vehicles
Environment: Motivations, Architecture,
Challenges, and Critical Issues
Year of Publication 2018
Author Names Tasneem s. j. Darwish and Kamalrulnizam Abu bakar
Source IEEE Access, Volume:6
Extract of the paper
In this paper they merged three dimensions including
intelligent computing (i.e. cloud and fog computing)
dimension, real-time big data analytics dimension, and
IoV dimension. Fog computing complements the cloud
computing by providing distributed, intelligent, and fast
data processing at the network edge
Findings
The ITS concept was introduced to increase road safety,
improve transportation systems efficiency, and preserve
our environment. ITS applications are delay-sensitive and
processing the data at the cloud centers creates long
delays.

7. CONT..
Title of the paper
IFCIoT: Integrated Fog Cloud IoT: A novel
architectural paradigm for the future Internet of
Things
Year of Publication 2017
Author Names Munir, Arslan, Prasanna Kansakar, and Samee U. Khan
Source IEEE Consumer Electronics Magazine, Volume 3.
Extract of the paper
This work presents a IFCIoT architecture by which fog act
as an intermediate layer between cloud and IoT. Fog
comprises of fog nodes like base station, gateway, smart
routers. The entire fog deployment is located locally. A fog
node in the IFCIoT architecture manages all IoT devices
within its radio network. The IoT devices typically
leverage radio-access networks to communicate with the
fog.
Findings
The proposed architecture increased the performance,
energy efficiency, reduced latency and scalability. To
adapt the workload reconfigurable fog-node architecture is
proposed.

8. CONT..
Title of the paper
Exploiting smart e-Health gateways at the edge of healthcare
Internet-of-Things: A fog computing approach
Year of
Publication
2018
Author Names
Amir M. Rahmani, Tuan Nguyen Gia, Behailu Negash, Arman
Anzanpour, Iman Azimi, Mingzhe Jiang , Pasi Liljeberg
Source Elsevier Future generation Computer Systems, Vol 80
Extract of the
paper
This paper proposed a placement of Smart gateway by which it
needs a bridging between the sensor and the internet. The
bridging is the gateway which is at the edge of the network to
perform functionalities. This gateway will have some control
over the sensor data that are transmitted through the internet.
Findings
One more tier adds complexity in terms of integration. High
level services is offered by Smart gateway to sensors and end
users at the edge of the network.

9. CONT..
Title of the paper
Edge cognitive computing based smart healthcare
system
Year of Publication 2018
Author Names Chen, Min, et al
Source Elsevier Future generation Journal, Volume:86
Extract of the paper
This paper proposed a Edge-Cognitive-Computing-based
(ECC-based) smart-healthcare system. This system is used to
monitor and analyse the physical health of users using
cognitive computing . It optimizes the computing resources
by resource allocation of the whole edge computing network
comprehensively according to the health-risk grade of each
user.
Findings
This ECC based system improves the survival rate of patients
in emergency situation. This system solved the problem of
inflexible network resource deployment.

10. FINDINGS
 Damage in sensors leads to difficulty in capturing the data
 Any damage in fog node leads to interruption of communication
between the layers
 The data communication between Application layer and cloud takes
more response time
 The framework is very useful for senior citizens and disabled people

11. PROPOSED SYSTEM
 The objective is to capturing the data from Application layer
and send to the fog layer which consists of fog nodes.
 The fog nodes can be Wi-Fi routers, gateway devices, base
stations etc.
 The Machine learning algorithms and decision-making system
are deployed on those fog nodes based on its capability.
 Based on the results of processing, the alert or notification
kind of output is delivered through actuators to physician or
care taker and data for long term and periodical analysis is
sent to the cloud layer which is the top most layer

12. LAYERED FRAMEWORK FOR PROPOSED SYSTEM

13. APPLICATION LAYER
 The role of application layer is to capture all the
real time data for various applications.
 This can be done by placing various kinds of
sensors.
 Then the collected data are transmitted through
Bluetooth or Wi-Fi to the intermediate layer called
fog layer.

14. FOG LAYER
 The fog layer consists of various fog nodes.
 Each fog node takes responsibility to process the data.
 In our proposed framework, AI methodologies and
decision making systems are deployed on fog nodes.
 Based on the results, alert or notification kind of output
are delivered through the actuators to the cloud layer.

15. CLOUD LAYER
 This is the top most layer in our proposed framework.
 The cloud layer comprises of centralized data centers.
 The data of various applications are stored in the
servers.
 The authority person can fetch the data from the cloud
for further processing.

16. USE CASE

17. OBJECTIVES
 To have assessment of important physiological variables of
elderly patients, disabled patients or patient with chronic
disease during critical periods of their biological functions. It
is necessary to know their actual value or trend of change.
 In critical cases, Artificial Intelligence (AI) techniques can be
applied to manage the patient data.

18. CONCLUSION
We proposed an intelligent architectural framework for fog
computing that can adapt according to the application requirements. The
main motive is to overcome the drawback of cloud which takes more
response time. The health monitoring is the critical application when
compared to other application, so fog layer is added as the intermediate
layer for the quick processing.

19. FUTURE WORK
The proposed work can be extended by incorporating
machine learning algorithm in the fog layer for decision making
during emergency situation and further notification to the care
takers as well as the physicians.

20. REFERENCES
[1].Munir, Arslan, Prasanna Kansakar, and Samee U. Khan. "IFCIoT: Integrated Fog Cloud IoT: A
novel architectural paradigm for the future Internet of Things." IEEE Consumer Electronics
Magazine 6, no. 3 (2017): 74-82.
[2].OpenFog Consortium. (2017, Apr.). OpenFog. [Online]. Available:
http://www.openfogconsortium.org/
[3].S. Yi, Z. Hao, Z. Qin, and Q. Li, “Fog computing: Platform and applications,” in Proc. IEEE
Workshop on Hot Topics in Web Systems and Technologies (HotWeb), Nov. 2015, pp. 73–78
[4].M. Aazam and E.-N. Huh, “Fog computing: The cloud-IoT/IoE middleware paradigm,” IEEE
Potentials, vol. 35, no. 3, pp. 40–44, May 2016.
[5].Chen, Min, et al. "Edge cognitive computing based smart healthcare system." Future Generation
Computer Systems (2018).

21. REFERENCES
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[9].Darwish, Tasneem SJ, and Kamalrulnizam Abu Bakar. "Fog Based Intelligent Transportation Big
Data Analytics in The Internet of Vehicles Environment: Motivations, Architecture, Challenges, and
Critical Issues." IEEE Access 6 (2018): 15679-15701.
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22. Thank You