A REAL TIME SYSTEM TO ANALYZE PATIENT’S CONDITION USING
SECOND LAYER COMPUTING
Dr.E.Dinesh,Assistant Professor
Electronics and Communication Engineering
M.Kumarasamy College of Engineering
Karur, India
dineshe.ece@mkce.ac.in
Ms. K. Poovitha
Electronics and Communication Engineering
M.Kumarasamy College of Engineering
Karur, India
poovithapoovi1@gmail.com
Ms.V. Pranikaa
Electronics and Communication Engineering
M.Kumarasamy College of Engineering
Karur, India
pranikaav24@gmail.com
Ms.M. Rosini
Electronics and Communication Engineering
M. Kumarasamy College of Engineering
Karur, India
rosinimanivel@gmail.com
Abstract: In the line of science, remote health
monitoring is regarded as a hot topic. Regardless of the
fact that there are more senior citizens, it is certain that
a dispersed medical care system with remote
monitoring and the goal of diminishing the rising cost
of healthcare is urgently needed. Rapid detection and
ongoing health monitoring can save up to 60% of
lives. A wireless, wearable, affordable, and automatic
health monitoring system is a good solution because
of these factors. When needed, it can be challenging to
check basic life factors like temperature, heart rate,
gyro, oxygen level, etc. Using Arduino and a standard
ESP8266, we will develop and construct an IOT-based
patient health monitoring system.
I. INTRODUCTION
Fog computing is a concept that was developed to link
devices and data centres. Fog computing has many
applications, including real-time data collection,
analysis, and facilitation from network-connected
devices. Fog computing is not entirely located at the
network's edge, despite the fact that it is becoming
more virtualized and may offer internet connectivity
among edge devices and cloud - based data centres.
Fog computing can be deployed at draws on three
levels: (1) data gathering from network edge (sensors,
automobiles, highways, and ships), (2) numerous
devices linking to a network and providing all input,
and (3) the acquired data from the systems that should
be evaluated within a few second, coupled with
decision making. seen as a flexible global network
architecture wherein entities with various qualities are
combined to offer improved services In the field of
healthcare, wireless sensor networks (wsn are a vital
technology (WBANs)). One way to tell is by looking
at your body's temp, pulse, Gyroscopic, and levels of
blood oxygen. For viewing and diagnostic purposes,
these data are transmitted to authorized customers via
protocols like Wi-Fi.
In healthcare monitoring, data from sensor nodes that
employ cloud computing are analyzed and stored on
remote cloud servers. There are other challenges, too,
including latency sensitivity, large data transmission,
and position awareness. Fog computing extends cloud
services closer to end users by extending memory,
handling, and connection to edge devices, enhancing
network capacity, mobility, privacy, and security.
Applications that require real-time or low latency
performance are ideal for fog computing. For the
management of health and safety, real-time
environmental monitoring, visualization, and
notification systems are being researched to offer a
new perspective on reliable data collection, efficient
visualization, and handling urgent situations. The
number of senior people worldwide has increased
recently, which has led to an increase in difficult health
issues and higher clinical expenditures. People,
especially the elderly, need to have their health
remotely monitored. This may help to lower the
additional expenses related to hospitals. On the other
hand, conventional methods of healthcare monitoring
are cumbersome and time-consuming.
The creation of an efficient system for healthcare
monitoring that lowers hospitalizations while also
enhancing patients' life quality is therefore desired.
For the management of health and safety, real-time
environmental monitoring, visualizations, and
notification systems are being researched to offer a
new perspective on reliable data collection, efficient
visualization, and handling urgent situations. The
number of senior people worldwide has increased
recently, which has led to an increase in difficult health
issues and higher clinical expenditures. People,
especially the elderly, need to have their health
remotely monitored. This may help to lower the
additional expenses related to hospitals. On the other
hand, conventional methods of healthcare monitoring
are cumbersome and time-consuming. As an outcome,
there is a requirement for the creation of an efficient
real time application that lowers hospitalizations while
also enhancing patients' life satisfaction.
In order to offer patients with high-quality care the
practice of mobile health or m-health involves
gathering health information from patients using
accessible devices like smartphones and uploading it
to a cloud server over the internet. The cloud may be
used by hospitals insurance companies and other
organizations to access these data the development of
the m-health system is facilitated by the use of body
sensor networks and wearable technology due to
security concerns and the issue of network failure
using a basic device system is not practical for many
healthcare applications which risks the patient’s
condition due to the delay in getting the patient data
real-time health analysis is made possible by
integrating m-health into the environment of a patient.
Data delivery from sensing devices to the internet as
well as cloud to hospitals is delayed when only the
cloud platform is used for analysis and storage of
healthcare data fog computing is therefore being
employed in health care applications that require quick
responses to emergencies and real-time operations
implantable cardioverter defibrillators are being used
by patients in order to maximize patient survival it
gathers information on things like device functionality
and physiological indications remotely tracking this
data in real time enables early clinical variable
diagnosis and helps treatment adjustments or device
reconfiguration before patients are admitted to the
hospital. Remotely tracking this data in real time
enables early clinical variable diagnosis and helps
treatment adjustments or device reconfiguration
before patients are admitted to the hospital. Since
remote health monitoring is more dependable, safe,
affordable, and responsive to any failures, it is more
effective than traditional one-on-one consultations. It
also reduces the need for hospital visits and eliminates
unpleasant surprises about the patient's health status.
Medical assistants who monitor patients admitted to
hospitals, particularly in Hospitalized Patients (ICUs),
may make mistakes since it is impossible to
completely eliminate human error. By extending cloud
services to the edge of the network, fog computing is
produced. Cloud computing is not an alternative for
fog computing; rather, fog computing is an addition to
it. Currently, heavy network traffic results in
unacceptable latency and subpar services for end
users, as well as a high bandwidth cost when moving
lots of data to the cloud. To meet the needs of the big
data era, it is crucial to restructure computer
paradigms. According to this study, real-time
applications that require a quick response time and
minimal latency are perfect candidates for fog
computing. This study demonstrates how fog
computing lowers latency. According to this study,
real-time applications that require a quick response
time and minimal latency are perfect candidates for
fog computing. This study demonstrates that fog
computing, as opposed to cloud computing, minimizes
latency, which is crucial in the domain of real-time
healthcare.
The goals of the suggested fog computing model
include reducing latency when transmitting and
sharing information signals with remote servers so that
medical services can be provided more quickly in both
the temporal and spatial dimensions. Machine learning
is used as a result to analyze the data before it is
interpreted. The suggested methods and procedures
are described in great detail, including how they might
operate in challenging circumstances and how they
could be used in areas with the greatest demand for
healthcare services. The rest of the essay is organized
as follows: The relevant works completed during the
study process are presented in Section 2, the suggested
methodology is presented in Section 3, the findings
and discussion are presented in Section 4, the
conclusion is presented in Section 5, and finally, the
reference article used in this data analysis is offered
under reference section.
II. RELATED WORK
In a healthcare system, fog computing is an idea that
is used. The use of bandwidth, Quality of Service, and
notification alerts has demonstrated the efficacy of
computing in healthcare monitoring. The deployment
approach has also included a case study on utilizing
ECG characteristics gleaned using clinical evidence at
the network's edge. This study collects and sends data
via Wi-Fi, Bluetooth, ZigBee, or 6LoWPAN
communication protocols, including temperatures,
humidity, patients’ locations, SpO2, Electro
Encephalography (EEG), ECG, and EMG. The
development of wearable technology in the field of
healthcare is made possible by technological
advancement. It is recommended to use everyday
technologies like mobile phones as monitoring tools
rather than using external sensors. Mobile phones are
the only devices that can be used to remotely monitor
patients due to their many built-in sensors, including a
gyroscope, normal heart sensor, accelerometer, and
more. Fog computing is necessary to support cloud
data storage, networking, and processing, though.
Examples of security breaches include impersonating,
data theft, integrity of data, eavesdropping, and
collusion. Sensor or Internet of Things (IoT)-derived
clinical data is incredibly vulnerable to security
breaches. Only allowed people would be able to access
these records, which would be kept in the strictest of
confidence. Because of security weaknesses, the data's
secret is exposed to unauthorized persons. As a result,
a strategy for tackling security flaws in health systems
has been put out. This strategy involves a distributed
healthcare architecture based on fog technology with
numerous virtual computers dispersed locally for data
processing and storage. Different types of data and
requests are handled by each virtual machine.
For emergency department systems, a Resource
Preservation Net architecture has been developed and
is integrated with special cloud and edge computing
technologies. The Capacity Preservation Net is best
suitable for real-time systems where the patient's
average waiting time, length of stay (LoS), and
resource utilization are crucial performance indicators.
As a result, it turns out that this framework
considerably improves the LoS, average waiting time,
and resource usage. This study suggests that there will
be a crucial justification for middleware integration in
e-health systems. Middleware platform integration in
health systems is meant to enhance the process of
tracking a patient's or person's overall health. The
study of cellphone medical information systems
conducted locally is detailed in. This study includes a
questionnaire survey, an analysis of people's opinions
on how hospitals diagnose diseases, cost-effectiveness
of treatments, and hospitals information systems. The
survey results were used to complete the demand
design and analysis of the cellphone medical
information system.
For hospitals, students, health professionals, and
researchers, e-health and m-health services include
disease diagnosis, risk prediction, therapeutic
evaluation, health monitoring, teaching, and machinelearning model construction. The invention of a
semantic e-health model called "k-Healthcare" that
enables patients to effectively access medical data via
smartphones was followed by a review of the literature
on the effective usage of the healthcare domain. Khealthcare uses phones to sense and deliver data
because the majority of current e-health and m-health
systems do not use smartphone sensors to do so. The
sensor layer, network layer, internet layer, and service
layer are its four architectural layers. To provide
effective services to lower levels, each layer serves a
certain role. In particular, when a broad range of
regions is covered and real-time decision-making
scenarios are applied, this work concentrates on three
specifications: mobility, actuation, and control of
dependability and scalability. the application of cloud
and fog computing to home healthcare. Additionally,
this work presents the idea of edge computing in telehealth and remote patient health monitoring, which
makes it easier for patients and healthcare providers to
communicate. According this study, healthcare
technology that combines cloud and fog computing
delivers patients high-quality services. Additionally,
this method proves how safe, dependable, and efficient
it is.
In contrast to clinic-centric therapy, an e-health system
that is based on fog computing offers patient-centric
healthcare. All authorities, including patients,
hospitals, and services, are completely networked with
one another. It has been demonstrated and recorded
that switching from health center to patient-centric
techniques has its advantages and disadvantages.
According to this study, using e-health systems has the
advantages listed below: Analysis and processing of
big data, personalizing services, foreseeing and
predicting future health issues so that patients can take
necessary steps for prevention or curing, lifetime
monitoring, seamless integration with different
technologies regardless of their complexity, allencompassing (i.e., it can be used in a variety of
purposes such as healthcare, exercise, beauty, safety,
and so on), lifetime monitoring, and seamless
integration with existing technologies. A sophisticated
fog-based patients health monitoring system is on
display. This technology's primary objective is to
constantly monitor patients who need critical care at
home. This strategy consists of five layers: gathering
data, categorizing events, information mining, making
decisions, and cloud storage. In order to provide
improved services to the levels above it, each layer is
responsible for carrying out specific tasks. This paper
offers services like event identification using
computing to provide valid response, temporal mining
of patient health-related data based on event activating
in the fog layer, and actual notification-based
decision-making and data transmitted to professionals
and hospitals when patients are in unsafe conditions.
Use of a wearable Internet of Things (wIoT) system
with fog assistance has enabled end-to-end analytics.
In this study, a smart for gateway prototype for an
intermediate layer was created using Intel Edison and
a Raspberry Pi. This prototype aims provide the data
condition, filtration, analytics, and transmission of
pertinent data stored in the cloud for long-term
archiving and monitoring of temporal variability. This
prototype was put to the test using a system for smart
e-textile gloves, and it was found that using
knowledge-based models to translate real-world data
into pertinent analytics improves the usability of the
system. It may thus make it possible for wearable and
the cloud to interact more effectively from beginning
to end.
3.1. Fog Computing
Fog computing operates in the space between devices
and cloud computing. A new processing unit is
introduced between the user and the cloud in order to
improve latency, dependability, energy efficiency, and
privacy protection. In addition to the benefits of cloud
computing, fog computing offers compute, network
support, storage capabilities, and actual information
analysis. Fog computing also makes actual notification
systems on consumer electronics possible. Contrary to
cloud computing, fog computing immediately reaches
users, whereas delay is the key issue with cloud
computing. Fog computing is less effective than cloud
computing since it uses fewer computers,
smartphones, and other portable electronics.
Data from all devices has been transmitted to fog
computing devices, as illustrated in Figure 1, which
then travel via several layers including (1) the data
center/cloud layer, (2) the core domain/network layer,
(3) the edge domain layer, (4) the smart sensors layer,
and (5) the smart monitoring layer. To avoid data
corruption, there is a security barrier between the end
user device and the computer hardware. The
information is then processed by fog computing
devices. Preprocessing is the term for this. These are
not sizable data sets. Only a little amount of data can
be processed by fog computing devices. Another
benefit of fog computing is that it provides a
temporary storage layer where processed data may be
kept before being sent to the cloud for additional
processing.
III. CONSTRAINTS ON FOG COMPUTING
Fig 1
A fog computing network will be very challenging to
maintain. If a failure arises in the network during data
transfer, the maintenance personnel will take care of
it. The main advantage is that the computational
service can learn and adapt to the different
consequences of fog computing.
3.2. Fog Computing in Health Monitoring
A distinct standard utilized in the healthcare tracking
system is fog computing. Fog computing reduces data
volume and avoids network congestion while
improving data processing. In order to measure
specific signals and help patients get precise
information, the remote healthcare system uses body
sensors that are either integrated into the patient's body
or strategically placed on the body. A specialized
gadget collects data from those bodily sensors and also
facilitates connection between the sensors and the
monitoring equipment.
IV PROPOSED METHODOLOGY
The Patient Health Monitoring System Based on IoT
The suggested setup describes using ESP8266 &
Arduino. Our system may use sensors to measure and
maintain a watch on a number of indications, such as
temperature, heart rate, and blood oxygen levels, both
in hospitals and at home. A heartbeat sensor is used to
monitor the patient's heartbeat, while a temperature
sensor is used to determine the patient's body
temperature. SPO2 sensors are devices that monitor
the oxygen saturation and quantity in your blood,
respectively. The results can be recorded using
Arduino. The Arduino processes the code, which is
then shown on the LCD screen. A wireless connection
is established and data is sent to an IoT device server
via the ESP8266 Wi-Fi module. Lastly, IOT makes it
possible to monitor data from anywhere in the world.
In overpopulated countries like India, healthcare
issues are becoming more and more common as the
population grows and the need for medical care rises.
While treatment costs are declining, the population's
need for high-quality care is increasing. Health can
now be remotely monitored by a machine, which is
more dependable than manual monitoring, thanks to
advancements in technology. It can aid in reducing the
time required for individualized personal training
while also improving the dependability of cuttingedge equipment. One form of fog computing that is
influencing our daily lives is the use of wearable
technology, which tracks everything and everyone in
every imaginable way. The devices, which have a
variety of sensors, can be worn by people. These
devices keep an eye on a variety of human actions.
Wearable technology
In order to assure secure communication, there is a
proven approach for trip arty, one-round key
authenticated agreement that makes use of fog
computing resources. This study suggests a novel
computational architecture for scaling highperformance computing for prognosis and diagnostics,
sensing, and remote real-time monitoring.
4.1. Sensor Network Layer
In order to make fitness trackers comfortable for
patients to use in order to measure a patient's
temperature, pulse rate, and blood pressure, sensors
such as temperature sensors, heartbeat sensors, and
blood oxygen sensors are used in conjunction with a
wearable device in which these sensors are integrated.
All of these sensing apparatuses are a part of the sensor
network layer. Using sensors, this layer locates and
collects information on the patient's physiological
parameters, including body temperature, heart rate,
and systolic and diastolic blood pressure. Following
that, this data is transmitted over wired or wireless
communication protocols to fog computing
equipment.
4.2. Fog Layer
There are many dispersed nodes in the fog layer.
Gateways are the name for these nodes. The gateway
is a piece of equipment placed close to the sensors that
enables network connectivity, storing, and computing.
These sensors are responsible for recording events and
providing information. Four functions are made
possible by this layer: data collection from sensing
devices, computational modeling for health-related
decisions, career notification, and cloud data storage.
In order to increase its intelligence, boost system
dependability, reduce latency, get rid of network
connectivity issues, and quicken decision-making
processes, fog computing has implemented a remote
data processing system. The characteristics of the fog
layer are as follows:
(i)Data gathering: Information is received for any
further evaluation and decision-making from a variety
of sensors attached to the wearable, such as
temperature measurement, Electrocardiogram sensor
systems, and blood pressure sensors. Screening, noise
control, and preprocessing are done after data
gathering.
(ii)Data security: By gateways devices to the cloud,
patient data collected from sensors will be transmitted.
As a result, when developing a framework for
healthcare monitoring, considerations for the safety
and confidentiality of such info must be made. As a
conclusion, a watermarking and encryption process
has been developed to safeguard the suggested system.
To protect the information from unauthorized access,
it is encrypted into an unrecognizably different format.
During the watermarking procedure, the data is
concealed behind a picture without impacting its
validity or visibility.
(iii)In the suggested system, the patient's information
is hidden behind the patient's facial image when being
saved in the fog nodes or transferred to the cloud
server. A suitable encryption method was then used to
convert the watermarked image into a cypher image.
This encrypted data cannot be read by unauthorized
parties. It is necessary to save the data under patient's
image since it cannot be opened until the authorized
person gives their consent. Since the information is
utilized for theft and is gathered by people to improve
their blackmailing skills, it must be kept secret.
According to the author, data must be reduced because
storage capacity is a concern. It turns out that
broadcasting the patient's image for every piece of data
collected was overly overheard. By altering the
model's inputs and the way the network is built, it is
possible to make fog computing more reliable.
4.3. Cloud Layer
Distributed servers, repositories, and resources make
up the cloud layer. The cloud manager has the
responsibility of managing every piece of hardware
connected to a cloud layer and simplifying patient data
collection, processing, and storage. This information
can be used to analyze the patient's current health and
medical history. The characteristics of the cloud layer
are as follows:
(i) Data storage: Following the fog layer's data
analysis process, the processed data are transported to
the cloud layer, which provides a huge storage
capacity for keeping the patient healthcare data for
future study by careers, physicians, hospitals, and
insurance providers.
(ii) Data analysis: For upcoming studies in the domain
of clinical decision-making, the patient's health data in
the cloud—which includes images of the ill areas,
descriptions of the symptoms, therapies, and
therapeutic plans—is studied. To comprehend the data
better, a variety of machine learning algorithms and
data visualization tools may be employed.
(iii) Disease prediction: Depending on the patient's
age, height, weight, and genetic makeup, it is possible
to anticipate which diseases may affect them in the
future. Based on the correlation of vital indicators, a
machine learning algorithm may be used to forecast
the expected percentage of disease.
V DISCUSSION
The suggested health monitoring framework's data
flow
(i) The sensor data is gathered and examined.
(ii) Inappropriate sensor attachments, unwanted noise,
and electromagnetic interferences are filtered out. To
determine the patient's current health status, the data
collected is assessed.
(iii) A message will be sent to the doctors or caretakers
of the patients who need help with their health state if
any inconsistencies in the detected healthcare
parameter values are found following data processing.
(iv) The collected and analyzed data may be stored on
the cloud so that researchers, medical professionals, or
patients would be able to access it as needed in the
future. To guarantee the security of the patient's data
during transmission and storage, the information
stored will be photo shopped with patient's photo and
encrypted.
(v) Since storage space is a major issue in the big data
era, this information is compacted to decrease the
space for storage required.
(vi) The information is stored temporarily local in the
fog in the case of a network failure.
(vii) Placing a bridge system called a gateway in each
location is the most often used technique for obtaining
data from smart sensors. Data from sensors is received
by a gateway, which transforms it into information that
may be used. The sensors and the gateway both
wirelessly transmit data.
VI. RESULT
(viii) Fog computing is used, among other things, in a
smart electrical grid. In order to maintain cost
effectiveness, smart cities must be able to adjust to
changing demand, including ups and downs. This
suggests that actual data on electricity supply and
consumption is necessary for smart grids.
Fog Computing Serves as The Best Choice Due to
Its Following Features:
CIRCUIT DIAGRAM
(i Fog computing can offer improved delay
performance since fog resources are situated between
smart devices and cloud-based data centres.
(ii)Fog computing requires tiny centres with little
computation, communications, and storage capacities
in comparison to cloud data centres; the expense of
spreading micro fog centres close to end users will be
very low.
(iii)Because fog computing systems are highly
scalable, even as end-user population grows, more
micro fog centres may be deployed to meet the
increased demand. Increased cloud data center
deployment is unlikely because the expenses will be
extremely expensive.
Result 1
(iv)The services provided by fog computing are
repeated and resilient.
(v) Because fog resources are placed in close
proximity to end users, they can act as mobile clouds.
(vi) With fog computing, real-time services may reach
great performance.
(vii) The fog resources are capable of communicating
with a wide range of cloud-based service providers. As
an outcome, fog computing and its associated
resources have become highly structured.
(viii)Fog can be used to aggregate data. Resources for
sending partially processed data, such as in place of
actual data, to fog data centres for additional
processing
Result 2
VII. CONCLUSION
This paper proposed a framework for fog-based health
monitoring that uses fog gateways for medical
decision based on information gathered from sensors
like a temperature sensor, heartbeat sensor, and blood
oxygen detector implanted in a handheld device to
measure a person's temperature, heart rate, and the
diastolic and systolic pressure. In the event of an
emergency, these data are delivered to doctors or
careers through the cloud after being encrypted and
watermarked and maintained in the fog node unties
enhances the utility of the suggested system. The
enormous amount of data produced by these detectors
is compressed and kept on the cloud platform for later
use by medical facilities and research institutions. In
this paper, the benefits of deploying fog computing
services for clinical decision-making and monitoring
healthcare are underlined.
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