A SECURED ARCHITECTURE FOR IOT-BASED
HEALTHCARE SYSTEM
Palak Barapatre
B.Tech Student, Department of Electronics and Communication Shri Ramdeobaba College of
Engineering and Management Nagpur, (India).
E-mail: barapatrepv@rknec.edu
Yash Ingolikar
B.Tech Student, Department of Electronics and Communication Shri Ramdeobaba College of
Engineering and Management Nagpur, (India).
E-mail: ingolikaryj@rknec.edu
Prajakta Desai
B.Tech Student, Department of Electronics and Communication Shri Ramdeobaba College of
Engineering and Management Nagpur, (India).
E-mail: desaipm@rknec.edu
Pooja Jajoo
B.Tech Student, Department of Electronics and Communication Shri Ramdeobaba College of
Engineering and Management Nagpur, (India).
E-mail: jajoopd@rknec.edu
Prasheel Thakre
Assistant Professor, Department of Electronics and Communication Shri Ramdeobaba College of
Engineering and Management Nagpur, (India).
E-mail: thakrepn2@rknec.edu
Reception: 11/11/2022 Acceptance: 26/11/2022 Publication: 29/12/2022
Suggested citation:
Palak Barapatre, Yash Ingolikar, Prajakta Desai, Pooja Jajoo, and Prasheel Thakre. (2022). A secured architecture
for IoT-based healthcare system. 3C Empresa. Investigación y pensamiento crítico, 11(2), 222-230. https://
doi.org/10.17993/3cemp.2022.110250.222-230
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ABSTRACT
Healthcare has gradually moved away from the model centered on traditional health centers due to
the emergence of highly accurate sensors and Internet of Things (IoT) enabled medical equipment.
Ambient intelligence takes whatever actions are required in response to a recognized event in order to
enable continuous learning about patient data. The capabilities of IoT-assisted healthcare services
might be improved by incorporating autonomous control and human-computer interface (HCI)
technologies into ambient intelligence. Major unsolved issues include the privacy and security of
information collected by medical IoT devices, both during transmission to and during cloud storage.
This research explores different techniques, IoT factors, and features, with an emphasis on the data
security concerns connected to data flow in medical IoT. In order to guarantee data security and
privacy at all data levels, this study suggests a safe design for the IoT healthcare system.
KEYWORDS
HealthCare, Internet of Things, Secure IoT Networks, Privacy and Security HCI, Cloud Computing.
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1. INTRODUCTION
The Internet of Things principal objective is to connect every device on the earth. Today, IoT is mostly
employed in healthcare to provide instant access to information. The IoT is a network of autonomous
computing devices, mechanical and digital machines, animals, or humans. It connects everything to
the Internet, encourages information exchange, organizes correspondence, and enables item
positioning, tracking, administration, and monitoring. It provides information technology (IT)
solutions, which employ computers to store, retrieve, transfer, and modify data without requiring
human-to-human or human-to-computer interaction. According to its definition, the Internet of Items
(IoT) is a "dynamic worldwide interconnected network technology with self-configuring capabilities
based on standard and coherent communication protocols where virtual and physical things have
identities, physical characteristics, and virtual personalities". Integrating a number of promising
technologies will enable the IoT idea to be realized in the real world Jeong et al. [2016] Darshan et al.
[2015] Thakre et al. [2022]. IoT may be combined with identification, sensing, and communication
technologies. Security concerns, in addition to heterogeneity, scalability, connection, and many other
problems, are significant hindrances to the growth of the Internet of Things and must be adequately
addressed if IoT is to be successful. Among other security challenges like confidentiality, integrity,
etc., authentication of devices participating in the IoT is one of the more significant ones and is the
main theme of this article. IoT development has now been supported by a variety of technologies,
including NOMA in wireless technology, MEMS, and the Internet Thakre et al. [2022] Kinhikar, et al.
[2022]. A market worth more than 2.1 trillion dollars is predicted to emerge by 2025 owing to the low
cost of sensor devices, resulting in more than 25 billion installed units by 2020. A market worth more
than 2.1 trillion dollars is predicted to emerge by 2025 owing to the low cost of sensor devices,
resulting in more than 25 billion installed units by 2020.
2. RELATED WORK
2.1 ARCHITECTURES
In the context of the IoT, the authors Almotiri et al. [2016] presented a mobile health (m-health)
system (IoT). The use of mobile devices to obtain real-time health information from patients and store
it on network servers connected to the Internet is known as m-Health.
Fig 2.1: Protocol Stack. Fig 2.2: Related Architecture.
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In real-time, intelligence algorithms evaluate m-health data to find trends and raise different alarm
levels based on the status of the observed patients. An m-health system's information technology
architecture is a component of the IoT architecture, which is multi-layered and includes data
collection, data storage, and data processing layers. In , T. N. Gia et al. [2014] the 6LoWPAN
architecture, which is composed of low-power wireless area networks (LoWPANs)2, which are IPv6
stub networks for IoT networks, is described. 6LoWPAN has emerged as the favored option. Sensor
nodes employ the 6LoWPAN protocol stack, represented in Figure 2, to transmit data to the network.
The sensor data is encapsulated in a 6LoWPAN datagram and delivered over an IEEE 802.15.4 frame
to the edge router or gateway. The packets are converted to IPV6 packets by the gateway and sent to
the server for additional data processing through the standard IPV6 network. In Fig 2 depicts the end-
to-end architectural features of the 6LOWPAN-based healthcare system.
2.2. SECURITY ENCURITY MODELS
Privacy protection has been widely researched and accepted as a bottleneck in smart medical
healthcare. Goldwasser and Micali introduced a GM encryption system that proved semantic security
under the assumption of quadratic residuosity (Shafi & Silvio, 1984).The ideal lattice-based
encryption scheme proposed by Gentry [2009] was the first response to all encryption-related
problems up to 2009 Cheon & Kim [2015]. A method to significantly boost totally homomorphic
encryption's efficacy was put out by Chen, Ben, & Huang [2014]. In Cheon & Kim [2015] suggested
sacrificing additional public keys in favor of lowering the exponentiation circuit's degree. In Ichibane
et al. [2014] explained how to choose encryption parameters in several real-world settings.
3. PROPOSED ARCHITECTURE FOR IOT-BASED HEALTHCARE SYSTEM
The system under the proposed design uses IoT sensors to gather patient data, which is subsequently
sent to the hospital's cloud storage. Five parts make up the architecture.
3.1. IOT DEVICES IDENTIFIED FOR MEASUREMENT ARE
Blood glucose sensor:
An opto-physiological glucose sensor, which measures blood glucose levels
using a photodiode and an accelerometer, can be utilized in a non-invasive IoT system for glucose
monitoring.
Temperature monitoring sensor:
A Raspberry Pi-based temperature monitoring system and a planned
wireless network of sensors would alert a doctor if a patient's temperature rose over a certain level.
The LM35 sensor is proposed as a device for sensing the body's core temperature.
Healthcare systems for the elderly:
An approach for detecting falls among the elderly and informing
concerned parties is described. The system detects falls using sensor values from the accelerometer
and gyroscope. These should be simple for the monitoring systems to dismiss as false positives and
not register as falls. The waist is the right location to implant the sensors to detect geriatric falls,
according to an examination of diverse sensor implantation sites on the body using various types of
sensors and algorithms for machine learning.
Electrocardiogram heart monitoring systems:
A minimal-cost ECG system is recommended. The ECG
sensor in this system is merely a data collection device. Multiple users' ECG values are collected with
the ECG sensor and sent via Zigbee to a centralized server.
Heart Rate Monitoring: Instant heart rate monitoring is a feature of several popular models, including
Fitbit and Garmin as reviewed in Kumar N. [2017]
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Fig 3.1: Proposed Architecture.
3.1.1. SENSORS IN IOT-BASED HEALTHCARE SYSTEMS
A minimal cost ECG system is recommended. The ECG sensor in this system is merely a data
collection device. Multiple users' ECG values are collected with the ECG sensor and sent via Zigbee
to a centralized server. The MMA7260QT accelerometer is specifically suggested for use in detecting
body movement. Angular velocity can be measured along the x, y, and z axes using a device with three
axes. A gyroscope can help alert medical workers when a person falls by detecting body tilt. A
magnetometer aids in determining the relative direction by measuring the magnetic field. A
magnetometer can detect a human fall when combined with an accelerometer, gyroscope, and other
sensors, and is frequently seen in aged care equipment. Applications used: Raspberry Pi: According to,
body temperature monitoring devices are created using the Raspberry Pi platform. A multispectral
system that monitors body temperature, respiration rate, heart rate, and movement is developed using a
Raspberry Pi as reviewed in Kumar N. [2017].
3.2. COLLECTION AND TRANSMISSION
Sensors are fastened to the patient's body to collect and transmit data to the master node in real time.
The data from the master node is received by the mediator device and sent to the cloud. The collection
and storage of data in the cloud is an ongoing process. The IoT sensors based on WiFi and Zigbee
have the lowest latency, as shown in Table 3.2. Material is delivered to hospital servers using gateway
nodes installed in the area for the protocol conversion from ZigBee to TCP/IP.
Table I. Comparison of available wireless communication technologies for smart healthcare.
Technology Frequency Types Power usage Range Rate of Data
NFC 13.56 MHz PAN Very low 10 cm 100-400kbps
Bluetooth 4 2.4 GHz PAN Low 0.1km 1Mbps
Bluetooth 5 2.4 GHz PAN Very low 0.25 km 2Mbps
Z-wave Alliance 900 MHz LAN Very low 30m 9.6/40/100
kbps
Wi-Fi 2.4 GHz and 5GHz LAN Low-High 50 m 1Gbps
Zigbee 2.4GHz LAN Very low 10-100 m 250kbps
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Low-power sensor nodes can now be connected to the internet utilizing the Internet protocol, owing to
communication protocols like 6LoWPAN. In order to function, the IPV6 protocol needs a substantial
amount of computing power and bandwidth. They foresee "always-on" activity, which IoT devices do
not. The study Ge et al. [2016] discovered that delivering these signals as JSON packets rather than
XML packets significantly reduces response, processing, and interpretation times.
3.3. COMPUTING AND SECURITY
In fog computing, fog nodes (which can be access points, switchers, gateways, routers, etc.) are spread
at the network's edge and move toward terminal facilities at a certain location. The fog computing
layer is composed of the security, storage, and monitoring layers. Fog computing converts cloud data
centers into uniformly spread platforms while aiming to sustain cloud services. As a result, there is a
decrease in processing time for data from wireless medical sensors, which improves customer
satisfaction and service level. Because storage nodes are low and also have limited power capacity, the
public key encryption method cannot be employed for security. The research defines fog computing as
the addition of cloud computing to the system's edge, which is a highly virtualized stage of the source
pool that delivers computation, storage, and networking resources to local end users. The results
indicate that fog computing may achieve more than 89% low latency and bandwidth efficiency.
Because the fog nodule has a specified limited size, it cannot contain a large number of activities each
second Yi et al. [2014], Kumar Y. et al. [2019]. Fog computing in health care was the focus of Isa et al.
[2020]. In this study, a heart monitoring application was developed in which each patient was required
to provide a 30-minute recording of their electrocardiogram signal to fog processing units for
handling, analysis, and decision-making. The values are decreased so that less energy is utilized in
both processors and networking equipment. When compared to the central cloud, the results show that
energy may be saved by up to 69%.
To keep information confidential and private, it is necessary to encrypt it before it is delivered across a
public channel and stored on fog nodes or cloud servers. After receiving information and instructions
from the user and the cloud, they are responsible for filtering the raw data and uploading it to the
cloud for long-term storage or additional analysis.
Fig 3.3: Homomorphic encryption. Fig 3.4: Comparison between different encryption models.
Steps for Homomorphic encryption
Step 1:
The inquirer describes the data collected as a vector Z = (z1,z2,z3,.....,z8). After that, the secret
eight-order invertible matrix M is generated. MZ is then computed and sent to the server.
Step 2:
On the server, an intermediate vector V represented by V = (v1,v2,v3....v8) is stored. The
greatest (denoted as Mx) and smallest (denoted as Mn) values of each physiological item's normal
zone are likewise kept, and Li = Mx - Mn is discovered, with xi set to be 1/Li. In addition, we create
matrix A, the elements that form the leading diagonal are equal to Mid, where Mid= -Mn/Li.
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Step 3:
When the medical result MZ arrives at the server, it is multiplied by V to get MZV. MZV and
matrix A are then returned to the enquirer by the server. The enquirer then obtains ZV by left
multiplying it by M-1. Finally, Di is obtained by multiplying matrix A by ZV.
Step 4: If Di is in the range [0, 1], the data item remains normal. If Di is more than one, the data item
appears too high; otherwise, it displays too low.
Even if the attackers are capable of stealing data passing through the communications platform, such
as MZ and A, the data vector Z is left multiplied by M. If the proposed homomorphic encryption
mechanism is used, hackers will be unable to access genuine data because they do not know M.
Fig (3.4) shows that the proposed encryption algorithm outperforms RC5 and RC6 in terms of
efficiency. Several experiments were carried out to demonstrate its speed.
3.4. ANALYSIS OF DATA
Following the acquisition, the acquired data must be handled, filtered, and compressed to eliminate
extraneous information. We can use the ontology technique, as proposed in the article Kumar V.
[2015]. Clinic data for patients is specified as a source with a specific URL address in the
recommended approach. To enable information transmission via Ontology data access, mapping with
both previously collected and newly acquired records should be carried out after the acquisition of
patient records. The doctor will create a treatment strategy for the patient after considering these
variables, which will include the drugs and dosages that will be used. In this instance, the relationship
is represented by the Protégé tool. Run a sparql query to get data or an owl/rdf file from the database.
4. CONCLUSION
This paper provides an in-depth examination of the most recent advancements in the IoT-based
healthcare ecosystem, as well as a framework for IoT healthcare, a smart healthcare system, and its
logical architecture. Sensors in a smart healthcare system collect medical information, data is collected
via mobile and smart networks, data is transferred to cloud computing for analysis with advanced
algorithms, and medical professionals can make treatment and diagnosis recommendations. The
implementation of the IoT based healthcare system is split up into three features, with details on each
presented. A homomorphic strategy based on a scrambling matrix was developed to address current
problems of security in the IoT field. With the rapid development of IoT, we can anticipate that our
medical healthcare system will have a wide range of applications.
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AUTORS BIOGRAPHY
Ms Palak Vilas Barapatre is pursuing 3rd year B.Tech. in the department of
Electronics and communication Engineering, at Shri Ramdeobaba College of
Engineering and Management, Nagpur
Email: barapatrepv@rknec.edu
Mr. Yash Jayant Ingolikar is pursuing 3rd year B.Tech. in the department of
Electronics and communication Engineering, at Shri Ramdeobaba College of
Engineering and Management, Nagpur
Email: ingolikaryj@rknec.edu
Ms Prajakta Milind Desai is pursuing 3rd year B.Tech. in the department of
Electronics and communication Engineering, at Shri Ramdeobaba College of
Engineering and Management, Nagpur
Email: desaipm@rknec.edu
Ms Pooja Devendra Jajoo is pursuing 3rd year B.Tech. in the department of
Electronics and communication Engineering, at Shri Ramdeobaba College of
Engineering and Management, Nagpur
Email: jajoopd@rknec.edu
P. N. Thakre has received Bachelor’s degree in Electronics Engineering from RTM
Nagpur University in 2010. He has done M.Tech. in Electronics Engineering from
Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded
University in 2013. Presently he is pursuing Ph. D. from Shri Ramdeobaba College
of Engineering and Management, RTM Nagpur University, under the fellowship of
Visvesvaraya PhD Scheme for Electronics & IT. His research area includes Non-
Orthogonal Multiple Access (NOMA) for 5G Wireless Communication Systems and
Wireless channel Estimation Algorithms. Presently he is working as Assistant Professor in
Electronics & Communication Engineering Department, Shri Ramdeobaba College of
Engineering and Management, Nagpur.
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