IoT in Healthcare: Securing Data and Enhancing Authentication for Wearable Devices

Verified

Added on  2023/04/25

|29
|9374
|104
AI Summary
This paper discusses the importance of Internet of Things (IoT) in the healthcare sector and the need for securing IoT data from external threats. A novel authentication algorithm based on Constrained Application Protocol (CoAP) and Elliptic Curve Cryptography (ECC) is proposed to address security concerns and resource constraints in IoT devices. The algorithm ensures data security while maintaining efficient computing power.The application of IoT in healthcare, particularly through wearable devices, has revolutionized patient monitoring. This technology tracks various aspects of the human body in real-time, providing accurate health data. The paper aims to explore the development of wearable devices in the healthcare sector and proposes an authentication algorithm to enhance security and address resource constraints.  

Contribute Materials

Your contribution can guide someone’s learning journey. Share your documents today.
Document Page
Internet of Things for Smart Healthcare: Technologies, Challenges, and
Opportunities
1

Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.
Document Page
Abstract
In the current context the Internet of Things has become a very important domain where the
sensors and the embedded systems are capable of connect to each other and are also capable
of exchanging data among them. The IoT devices has gained so much importance in the last
few decades as this has provided many advantages to most of the industries and sectors which
includes the healthcare sector also. The data of the IoT is very much critical so it must be
ensured that the data are always secured from external threats and hackers. Thus in this
aspects the security constrains are very much important to keep the data of IoT safe from the
hackers and the intruders. The nature of the IoT devices as a resource requires authentication
process for the data retrieval and this process of authentication does not requires a high
computing power. Thus getting a brief aspects of the IoT devices in this paper a brief
discussion has been done on the usage of IoT in the healthcare sector. Here a brief discussion
has been done on the IoT and its importance regarding the development of the wearable
devices that can track the health regarding aspects of the human body.
Also, in the aspects of IoT in the healthcare industry a novel authentication process has been
proposed for the IoT devices. It has been assessed that the proposed architecture is capable of
providing a secure and a structure which is very much important for the healthcare sectors. In
this aspects the CoAP protocol has been dominated by the ECC algorithm, thus CoAP has
been used in this case.
The proposed algorithm has integrated 256 bit encryption which ensures higher security than
previous models. Systems that provided 256 bit encryption has one problem and that is slow
authentication speed. This proposed algorithm is light on resource and hence requires less
time for authentication which is significant improvement than previous models.
2
Document Page
The proposed mechanism of authentication is capable of providing an authentication
procedure with some high security. In previous proposed mechanisms, either designs were
not so accurate or it was so complex that it is not suitable for IOT environment.
Consideration of resource constraint in designing algorithm makes this proposed algorithm
different from another authentication algorithm as propped by other authors in their previous
research.
Keywords:
IOT, technology in healthcare, encryption, data security, resource constraint
Introduction
In this paper, the usage of the IoT for application in the healthcare sector and importance of
security along with resource constraint in IOT has been considered. Application and security aspect of
IOT has been reviewed and has been presented. From the discussion, it has been assessed that EEC
keys are capable of reducing the calculation requirement while it provides some powerful encryption
process and it is better compared with many types cryptography encryption procedures.
The wearable sensors are one of the most important and efficient devices in the aspect of
internet of things in health care devices (Azzawi, Hassan & Bakar, 2016). The wearable sensors has
become very much important in the current technological aspects as it provides accurate information
about a health of a patient (Javdani & Kashanian, 2017). The main purpose of this system is
effectively monitoring various of aspects of human body in real time which includes blood pressure,
heart rate, flow of oxygen, etc. This system achieves its purpose of gathering the data regarding
human body through its integrated sensors which is directly connected with the human body.
The main technology that has been used in this case is the wearable device technology which
effectively monitors biological aspects of the human body in the healthcare sector and for that IoT has
been leveraged in this case (Sankar, & Srinivasan, 2018). In the generic domain of healthcare system
this technology is used for measuring the blood pressures, heart rate, body temperature and many
more things (Govinda & Saravanaguru, 2016). This technology is also very much useful for in the sub
domain of surgeries as this can provide information of the human body on a real time basis which is
very much important to monitor during the operating hours on a patient (Cook et al., 2018). The main
limitation in this case using a wearable gadget during the operation is not suitable.
The main objective of this project is making the healthcare sector more advance so that that
various risks in this sector can be minimised and more better health can be provided to the patients
(Azzawi et al., 2016). Classification of the used components is very much important so that they so
that the users can effectively understand how this system works actually. The main gap in this case is
that previous wearable devices were capable of measuring some basic properties of human bodies but
in this case the proposed device is more advanced and it is capable of measuring the blood pressure
also of the human bodies which is actually a hard task (Gubbi et al., 2015).
In this case the previous solutions has been chosen regarding development of the wearable
devices for gaining an idea about the wearable devices in the context of healthcare devices and what
3
Document Page
type of innovation has been done so far in this sector (Mishra et al., 2016). Some of the papers has
been chosen out of a vast number of papers for the proceeding with the further research as the selected
paper was having more relevancy with domain of the research while the other papers were only
having some basic information about the wearable device (Zhang et al., 2014).
For organizing the paper perfectly first the existing technologies on the wearable technologies
must be evaluated properly. Then a survey has been taken of a hospital which uses this technology
and it is asked to them that how this technology has affected them and their responses has been
recorded (Khoo, 2015). In the further cases a brief analysis has been done on the responses and from
there further discussion has been started.
The wearable technology in the healthcare sector is sort of some advanced devices which can
effectively track the healthcare record of the humans on a real time basis (Jing et al., 2014). This
technology helps to monitor various of aspects of a human body which includes body temperature,
heart rate monitoring and many more other aspects (Kim, 2017). In the domain of healthcare industry
this is having a huge importance as this helps the doctors to determine the health conditions quickly
without much effort (Tewari & Gupta, 2017). This can be very much helpful for the emergency cases.
4

Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.
Document Page
Literature Review
Previous work:
According to u Farooq et al. (2015) Most of the previous researches that has focused on
integrating IOT for application in healthcare that has only directed the focus on
authentication technology. However, algorithm proposed in this context is not analysed in
terms of resource constraints, which is an important criteria to consider, as resource
constraint is an important issue in the IOT environment (Yaqoob et al., 2017). however as
identified in the analysis , it is seen that no dedicated effort has been provided by the previous
researchers and hence there is a requirement to conduct extensive research on this aspect as
without properly identifying solution for dealing with resource constraints in the IOT
environment, it is not possible to propose effective authentication algorithm for IOT
application (Hassanalieragh et al., 2015). Here in this context, an authentication algorithm has
been proposed that addresses authentication issues in IOT environment and this proposed
algorithm is also light on resource, as it does not require extensive computing resources and
computing power.
YANG, Hao & ZHANG (2016) have proposed a mutual authentication model for securing
devices in IOT environment. It is a modification of existing authentication protocol that is
based on RFID technique, which is applicable for database implemented in distributed
environment. The modification has been made to match with IOT environment and. It
consists of three important components such as backup for each terminal devices integrated
for controlling, integration of devices that monitors this controlling application and
integration of an alarm system to notify about any issues in the authentication process.
In another research paper, the authors have provided two phase authentication protocol. It is
a certification based authentication protocol. This protocol allows in two way authentication,
which means with this technology, it is possible to authenticate both the IOT devices and the
5
Document Page
controlling station as well and allow for mutual communication, and authentication at the
same time.
In this research paper, the authors have proposed for an authentication algorithm that is based
on Elliptic Curve Cryptography (ECC). According to the authors, this method is more
secured and efficient as well compared to public key cryptography, which is not that secure.
The authors also specify that when considering application on healthcare sector security
needs to be ensured properly. Elliptic Curve Cryptography (ECC) as mentioned by the
authors has smaller key seize and hence appropriate for IOT based application. This protocol
has three main components that is Registration phase, Pre-computed and login phase and
authentication phase. In Pre-computed, login phase, and authentication phase, the IOT
devices need to send a login request for authenticating with the server. Authentication of the
IOT devices with the server is decided in the authentication phase. Once authentication of the
devices are established with the server, the devices are then allowed to login to the system
and access system data through internet.
In their research, Mahalle, Prasad, & Prasad (2014) have provided "Threshold Cryptography-
based Group Authentication (TCGA) scheme for the Internet of Things (IoT)," for securing
IOT devices. Here authentication of the IOT devices are done on group communication
model. However, this model of authentication is only applicable for Wi-Fi environment.
Hence, this model is not possible to extend to environment where different communication
technology is considered.it generates a secret key for which is also known as session key.
Different session key is generated for different group communication so that it is possible to
manage each group communication independently.
S. R. Moosavi et al. (2015) have proposed for SEA, which is a “Secure, and Efficient
Authentication and Authorization Architecture” for IoT-Based Healthcare and this
6
Document Page
mechanism is developed with Smart Gateways. This mechanism is based on “certificate-
based DTLS handshake protocol”.
Proposed system:
According to Hossain, Fotouhi, & Hasan, R. (2015) Although ECC encryption algorithm
reduce he key size, it does increases the message size significantly. Along with that, it is also
important to recognize that ECC algorithm has better authentication protocol than other
cryptographic algorithm such as public key cryptography, but it is complex to implement
(Bui & Zorzi, 2015). As already mentioned, one of the aspects that makes IOT a challenging
field for composing authentication algorithm, is that resources and computing power is very
limited (Lee, 2015). IOT devices are often equipped with very less memory and hence
execution of sophisticated authentication algorithm is not possible and hence alternative yet
effective authentication algorithm is required that not only address security challenges of IOT
devices, but also consider resource constraint of IOT devices (Perera, Liu & Jayawardena,
2015). Hence in this context an efficient authentication algorithm is proposed that is based on
Constrained Application Protocol (CoAP) and Elliptic Curve Cryptography. This CoAP
model is based on authentication mechanism proposed by Z. Shelby, K. Hartke, and C.
Bormann, "The constrained application protocol (CoAP)," 2014. and ECC based mechanism
is proposed by V. Kapoor, V. S. Abraham, and R. Singh, "Elliptic curve cryptography,"
ACM Ubiquity, vol. 9, pp. 20-26, 2014.
CoAP is based on IETF working group of Constrained Restful Environment (CoRE).
Constrained Restful Environment (CoRE) provides an effective architecture that is consistent
with the constraint nature of sensors nodes. CoAP provides an efficient context, which makes
it possible for these constrained nodes in providing mechanism that is required to implement
web transfer, and one significant benefit is that it allows for secure IoT communication. The
7

Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
Document Page
propose algorithm deploys three different protocols that are integrated with IOT protocol
stack. These three stacks include CoAP, MQTT and XMPP and it includes 6LoWPAN. This
protocol as provided is specially designed for environment with limited resources for
application. Hence, this protocol is consistent with the IOT environment.
Another technology that is available for communication over internet, an important feature in
IOT enabled network, though it is not considered here for some specific reasons. While
CoAP allows machine to work in two different mode that is as client and as a server. One
important feature of CoAP that makes this protocol secured than the HTTP technology is that
communication of message in CoAP is executed in asynchronous manner, which increases
security of application. In this proposed mechanism, an additional request/response layer is
added which increases security of the communication.
Both CoAP and ECC protocol has some benefits as well as some drawback and hence the
proposed mechanism has combined both this protocol. An ECC authentication mechanism
over CoAP connection has been deployed here. Combination of both these protocols has
helped in optimizing overhead associated with IOT network. Due to this, it is possible to
reduce communication and processing power that is required for IOT device authentication,
which ensures enhanced security.
The proposed authentication method consist of four stages that is required for execution of
this algorithm.
Stage1: it is the initialization phase. Here Control system provides a private key along with a
public key that is required for communication using ECC.
Stage2: registration of the devices is done in this phase. This phase includes the pre
authentication process where it is checked if the device in consideration is already
8
Document Page
authenticated with the IOT network or not and this process is done over CoAP. Control
station, which is an important part of the network, checks the associated device ID to identify
if any corresponding entry has been considered for it. If this is not done, in order to
authenticate the devices, a strong password is generated with associated device ID that is
unique for a specific device. The private key generated in this process acts as a password for
encryption and this password is stored in the IOT devices.
Stage3: it is also known as Mutual authentication stage. IoT enabled device, needs
appropriate password for generation of authentication key. Once the process to generate
authentication key is completed, it is then sent to control system. This is executed when the
control system try to connect with the server. Control system generates this authentication
key and corresponding IOT entries is stored in the control system that is associated with the
IOT device.
Stage4: all traffic that pass between different IoT devices and control station is secured with
encryption. This encryption is done to provide protection to the IOT devices from various
cyber security crimes that are often targeted at IOT devices for easy vulnerability and ease of
execution due to lack of proper security measures.
Consideration of resource constraint in designing algorithm makes this proposed algorithm
different from another authentication algorithm as proposed by other authors in their previous
research. Most of the research papers on this topic has described one factor for IOT
environment and that is security of information. Although security of IOT devices are
important, it is important to note that only increasing authentication bit is not enough, it is
required to consider authentication speed as well. In addition to that, consideration of
resource constraint is also an important design constraint. Hence, improvement is required in
algorithm design, which provide motivation for this research.
9
Document Page
10

Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.
Document Page
Proposed System Components
The authentication mechanism based on CoAP and ECC algorithm has been developed with
review of past as well present authentication protocol that has been applied in IOT
environment so far. In order to develop this system, practical experience and knowledge has
been considered as well. Assistance from security experts specialized in IOT based security
applications has been taken for developing this proposed mechanism for security of IOT
devices. It has also included criteria for evaluating proposed system based on applicability
and effectiveness of this proposed authentication algorithm. These factors are important for
development and validation of this developed system for effective evaluation.
The search for the relevant article has provided 440 result of which 105 papers were selected,
as these articles were in accordance with the search criteria. These articles have described
security issues in IOT environment. 40 article have described need for security mechanism in
IOT environment. 30 articles have described about resource constraints in IOT environment.
35 articles have described about both resource constraints and integration with authentication
protocol. Others articles were rejected as they were not relevant to the topic. 270 articles have
only described about application of IOT in reference with healthcare sector, which is not
research of interest here. 55 articles have only discussed about security issues in IOT without
any proposed solution. 45 articles have not even considered healthcare sector for application
of IOT. 20 articles were based on application of IOT in smart city, another 20 articles were
about IOT in logistics and 5 articles were about application of IOT in transportation system
with
Base one the analysis and previous knowledge in this field implies that, in order to have
proper application of IOT in medical context, three main points should be considered:
11
Document Page
1) What are the available option for data acquisition regarding patient medical
information
2) How data should be secured
3) How data should be distributed with data availability and data integrity
The first factor that is considered in this CoAP and ECC based authentication mechanism is
data. It includes data such as patient medical history, medicine information with the
properties of dimensionality, modality, semantic and it is considered to identify if data has
been properly allocated. Then authentication protocols that are considered are deployed for
data encryption for data security. Next, data distribution is considered for making data
available for proper application.
Factors Main attributes Common instances
Data Dimensionality 2D, 3D
Raw patient data Acquisition techniques Data communication, sever
connection
Data encryption Algorithm CoAP, ECC, SEA
Data distribution Location Distributed database
12
Document Page
References:
Abtoy, A., Touhafi, A., & Tahiri, A. (2018). Ambient Assisted living system’s models and
architectures: A survey of the state of the art. Journal of King S****** University-
Computer and Information Sciences.
Alfano, F. R. D. A., Olesen, B. W., &Palella, B. I. (2017). Povl Ole Fanger’s impact ten years
later. Energy and Buildings, 152, 243-249.
Al-khafajiy, M., Baker, T., Chalmers, C., Asim, M., Kolivand, H., Fahim, M., & Waraich, A.
(2019). Remote health monitoring of elderly through wearable sensors. Multimedia
Tools and Applications, 1-26
Alsina-Pagès, R., Navarro, J., Alías, F., & Hervás, M. (2017). homesound: Real-time
******io event detection based on high performance computing for behaviour and
surveillance remote monitoring. Sensors, 17(4), 854.
Alvear-Sandoval, R. F., &Figueiras-Vidal, A. R. (2018). On building ensembles of stacked
denoising auto-encoding classifiers and their further improvement. Information
Fusion, 39, 41-52
Amin, R., & Biswas, G. P. (2016). A secure light weight scheme for user authentication and
key agreement in multi-gateway based wireless sensor networks. Ad Hoc Networks,
36, 58-80.
Amin, R., Islam, S. H., Biswas, G. P., Khan, M. K., & Kumar, N. (2018). A robust and
anonymous patient monitoring system using wireless medical sensor networks. Future
Generation Computer Systems, 80, 483-495.
13

Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
Document Page
Azzawi, M. A., Hassan, R., & Bakar, K. A. A. (2016). A Review on Internet of Things (IoT)
in Healthcare. International Journal of Applied Engineering Research, 11(20), 10216-
10221.
Azzawi, M. A., Hassan, R., & Bakar, K. A. A. (2016). A Review on Internet of Things (IoT)
in Healthcare. International Journal of Applied Engineering Research, 11(20), 10216-
10221.
Bagaa, M., Chelli, A., Djenouri, D., Taleb, T., Balasingham, I., & Kansanen, K. (2017).
Optimal placement of relay nodes over limited positions in wireless sensor networks.
IEEE Transactions on Wireless Communications, 16(4), 2205-2219.
Benson, L. C., Clermont, C. A., Bošnjak, E., & Ferber, R. (2018). The use of wearable
devices for walking and running gait analysis outside of the lab: A systematic review.
Gait & posture.
Benson, L. C., Clermont, C. A., Osis, S. T., Kobsar, D., & Ferber, R. (2018). Classifying
running speed conditions using a single wearable sensor: Optimal segmentation and
feature extraction methods. Journal of Biomechanics, 71, 94-99.
Bui, N., & Zorzi, M. (2015, October). Health care applications: a solution based on the
internet of things. In Proceedings of the 4th international symposium on applied
sciences in biomedical and communication technologies (p. 131). ACM.
Camps, J., Samà, A., Martín, M., Rodríguez-Martín, D., Pérez-López, C., Alcaine,
S., ...&Bayés, À. (2017, June). Deep learning for detecting freezing of gait episodes in
Parkinson’s disease based on accelerometers. In International Work-Conference on
Artificial N******al Networks (pp. 344-355). Springer, Cham.
14
Document Page
Cao, L., Wang, Y., Zhang, B., Jin, Q., &Vasilakos, A. V. (2018). GCHAR: An efficient
Group-based Context—Aware human activity recognition on smartphone. Journal of
Parallel and Distributed Computing, 118, 67-80.
Chou, J. S. (2014). An efficient mutual authentication RFID scheme based on elliptic curve
cryptography. The Journal of Supercomputing, 70(1), 75-94.
Constant, N., Borthakur, D., Abtahi, M., Dubey, H., & Mankodiya, K. (2017). Fog-assisted
wiot: A smart fog gateway for end-to-end analytics in wearable internet of things.
arXiv preprint arXiv:1701.08680.
Cook, A., Robinson, M., Ferrag, M. A., Maglaras, L. A., He, Y., Jones, K., & Janicke, H.
(2018). Internet of cloud: Security and privacy issues. In Cloud Computing for
Optimization: Foundations, Applications, and Challenges (pp. 271-301). Springer,
Cham.
de Oliveira Júnior, W. G., de Oliveira, J. M., Munoz, R., & de Albuquerque, V. H. C. (2018).
A proposal for Internet of Smart Home Things based on BCI system to aid patients
with amyotrophic lateral sclerosis. N******al Computing and Applications, 1-11
Dixon, P. C., Schütte, K. H., Vanwanseele, B., Jacobs, J. V., Dennerlein, J. T., &Schiffman,
J. M. (2018). Gait adaptations of older adults on an uneven brick surface can be
predicted by age-related physiological changes in strength. Gait & posture, 61, 257-
262.
Dwivedi, A. D., Srivastava, G., Dhar, S., & Singh, R. (2019). A Decentralized Privacy-
Preserving Healthcare Blockchain for IoT. Sensors, 19, 326.
15
Document Page
Farahani, B., Firouzi, F., Chang, V., Badaroglu, M., Constant, N., & Mankodiya, K. (2018).
Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and
healthcare. Future Generation Computer Systems, 78, 659-676.
Farash, M. S. (2014). Cryptanalysis and improvement of an efficient mutual authentication
RFID scheme based on elliptic curve cryptography. The Journal of
Supercomputing, 70(2), 987-1001.
Fujiwara, K., Miyajima, M., Yamakawa, T., Abe, E., Suzuki, Y., Sawada, Y., ...&Sasano, T.
(2016). Epileptic seizure prediction based on multivariate statistical process control of
heart rate variability features. IEEE transactions on biomedical engineering, 63(6),
1321-1332.
Gong, T., Huang, H., Li, P., Zhang, K., & Jiang, H. (2015, December). A medical healthcare
system for privacy protection based on IoT. In 2015 Seventh International
Symposium on Parallel Architectures, Algorithms and Programming (PAAP) (pp.
217-222). IEEE.
Gope, P., & Hwang, T. (2016). BSN-Care: A secure IoT-based modern healthcare system
using body sensor network. IEEE Sensors Journal, 16(5), 1368-1376.
Govinda, K., & Saravanaguru, R. A. K. (2016). Review on IOT technologies. International
Journal of Applied Engineering Research, 11(4), 2848-2853.
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A
vision, architectural elements, and future directions. Future generation computer
systems, 29(7), 1645-1660.
16

Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.
Document Page
Haider, D., Ren, A., Fan, D., Zhao, N., Yang, X., Shah, S. A., ... & Abbasi, Q. H. (2019). An
efficient monitoring of eclamptic seizures in wireless sensors networks. Computers &
Electrical Engineering, 75, 16-30.
Haladjian, J., Haug, J., Nüske, S., & Bruegge, B. (2018). A wearable sensor system for
lameness detection in dairy cattle. Multimodal Technologies and Interaction, 2(2), 27.
Hamidi, H. (2019). An approach to develop the smart health using Internet of Things and
authentication based on biometric technology. Future Generation Computer Systems,
91, 434-449.
Hassan, M. M., Uddin, M. Z., Mohamed, A., &Almogren, A. (2018). A robust human activity
recognition system using smartphone sensors and deep learning. Future Generation
Computer Systems, 81, 307-313
Hassan, R., Jubair, A. M., Azmi, K., & Bakar, A. (2016, December). Adaptive congestion
control mechanism in CoAP Application Protocol for Internet of Things (IoT).
In 2016 International Conference on Signal Processing and Communication
(ICSC) (pp. 121-125). IEEE.
Hassanalieragh, M., Page, A., Soyata, T., Sharma, G., Aktas, M., Mateos, G., & Andreescu,
S. (2015, June). Health monitoring and management using Internet-of-Things (IoT)
sensing with cloud-based processing: Opportunities and challenges. In 2015 IEEE
International Conference on Services Computing (pp. 285-292). IEEE.
Hossain, M. M., Fotouhi, M., & Hasan, R. (2015, June). Towards an analysis of security
issues, challenges, and open problems in the internet of things. In 2015 IEEE World
Congress on Services (pp. 21-28). IEEE.
17
Document Page
Hu, B., Dixon, P. C., Jacobs, J. V., Dennerlein, J. T., &Schiffman, J. M. (2018). Machine
learning algorithms based on signals from a single wearable inertial sensor can detect
surface-and age-related differences in walking. Journal of biomechanics, 71, 37-42.
Hu, J. X., Chen, C. L., Fan, C. L., & Wang, K. H. (2017). An intelligent and secure health
monitoring scheme using IoT sensor based on cloud computing. Journal of Sensors,
2017.
Igelström, H., Åsenlöf, P., Emtner, M., & Lindberg, E. (2017). Improvement in obstructive
sleep apnea after a tailored behavioural sleep medicine intervention targeting healthy
eating and physical activity: a randomised controlled trial. Sleep and Breathing, 1-9.
Jan, M. A., Nanda, P., He, X., Tan, Z., & Liu, R. P. (2014, September). A robust
authentication scheme for observing resources in the internet of things environment.
In 2014 IEEE 13th International Conference on Trust, Security and Privacy in
Computing and Communications (pp. 205-211). IEEE.
Janidarmian, M., Roshan Fekr, A., Radecka, K., &Zilic, Z. (2017). A comprehensive analysis
on wearable acceleration sensors in human activity recognition. Sensors, 17(3), 529.
Javdani, H., & Kashanian, H. (2017). Internet of things in medical applications with a
service-oriented and security approach: a survey. Health and Technology, 1-12.
Jing, Q., Vasilakos, A. V., Wan, J., Lu, J., & Qiu, D. (2014). Security of the Internet of
Things: perspectives and challenges. Wireless Networks, 20(8), 2481-2501.
Kahng, M., Andrews, P. Y., Kalro, A., & Chau, D. H. P. (2018). A cti v is: Visual exploration
of industry-scale deep n******al network models. IEEE transactions on visualization
and computer graphics, 24(1), 88-97.
18
Document Page
Kalra, S., & Sood, S. K. (2015). Secure authentication scheme for IoT and cloud
servers. Pervasive and Mobile Computing, 24, 210-223.
Kalra, S., & Sood, S. K. (2015, July). Elliptic curve cryptography: survey and its security
applications. In proceedings of the international conference on advances in
computing and artificial intelligence (pp. 102-106). ACM.
Khan, F. A., Haldar, N. A. H., Ali, A., Iftikhar, M., Zia, T. A., &Zomaya, A. Y. (2017). A
continuous change detection mechanism to identify anomalies in ECG signals for
WBAN-based healthcare environments. IEEE Access, 5,***********4.
Khojasteh, S., Villar, J., Chira, C., González, V., & de la Cal, E. (2018). Improving fall
detection using an on-wrist wearable accelerometer. Sensors, 18(5), 1350.
Khoo, B. (2011, October). RFID as an Enabler of the Internet of Things: Issues of Security
and Privacy. In 2011 International Conference on Internet of Things and 4th
International Conference on Cyber, Physical and Social Computing (pp. 709-712).
IEEE.
Kim, J. H. (2017). A survey of IoT security: Risks, requirements, trends, and key
technologies. Journal of Industrial Integration and Management, 2(02), 1750008.
Kim, J., Zhou, Y., Schiavon, S., Raftery, P., & Brager, G. (2018). Personal comfort models:
predicting individuals' thermal preference using occupant heating and cooling
behavior and machine learning. Building and Environment, 129, 96-106.
Kobsar, D., & Ferber, R. (2018). Wearable Sensor Data to Track Subject-Specific Movement
Patterns Related to Clinical Outcomes Using a Machine Learning Approach. Sensors,
18(9), 2828.
19

Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
Document Page
Köping, L., Shirahama, K., & Grzegorzek, M. (2018). A general framework for sensor-based
human activity recognition. Computers in biology and medicine, 95, 248-260.
Kumari, S., Khan, M. K., Li, X., & Wu, F. (2016). Design of a user anonymous password
authentication scheme without smart card. International Journal of Communication
Systems, 29(3), 441-458.
Lee, B. M. (2015). Personalized service model for sharing medical devices in iot health-
platform. Advanced Science and Technology Letters, 99, 180-182.
Li, Y., Chen, C. S., Chi, K., & Zhang, J. (2018). Two-tiered relay node placement for WSN-
based home health monitoring system. Peer-to-Peer Networking and Applications, 1-
15.
Lobato, F. M. F., de Resende, D. C. O., do Nascimento, R. P., Siqueira, A. L. C., Jacob, A. F.
L., & de Santana, Á. L. (2017). Multimodal low-invasive system for sleep quality
monitoring and improvement. In Beyond the Internet of Things (pp. 223-242).
Springer, Cham.
Luo, M., Wang, Z., Ke, K., Cao, B., Zhai, Y., & Zhou, X. (2018). Human metabolic rate and
thermal comfort in buildings: the problem and challenge. Building and Environment,
131, 44-52
Mahalle, P. N., Prasad, N. R., & Prasad, R. (2014, May). Threshold cryptography-based
group authentication (TCGA) scheme for the internet of things (IoT). In 2014 4th
International Conference on Wireless Communications, Vehicular Technology,
Information Theory and Aerospace & Electronic Systems (VITAE) (pp. 1-5). IEEE.
Marzouqi, H., Al-Qutayri, M., & Salah, K. (2015). Review of elliptic curve cryptography
processor designs. Microprocessors and Microsystems, 39(2), 97-112.
20
Document Page
Mauldin, T., Canby, M., Metsis, V., Ngu, A., & Rivera, C. (2018). Smartfall: a smartwatch-
based fall detection system using deep learning. Sensors, 18(10), 3363
McGinnis, E. W., McGinnis, R. S., Hruschak, J., Bilek, E., Ip, K., Morlen, D., ... & Muzik,
M. (2018). Wearable sensors detect childhood internalizing disorders during mood
induction task. PloS one, 13(4), e0195598.
McGinnis, R. S., Mahadevan, N., Moon, Y., Seagers, K., Sheth, N., Wright Jr, J. A., ... &
Pindado, J. A. (2017). A machine learning approach for gait speed estimation using
skin-mounted wearable sensors: From healthy controls to individuals with multiple
sclerosis. PloS one, 12(6), e0178366.
McGinnis, R. S., McGinnis, E. W., Hruschak, J., Lopez-Duran, N. L., Fitzgerald, K.,
Rosenblum, K. L., & Muzik, M. (2018, March). Wearable sensors and machine
learning diagnose anxiety and depression in young children. In 2018 IEEE EMBS
International Conference on Biomedical & Health Informatics (BHI) (pp. 410-413).
IEEE.
Mietz, R., Abraham, P., & Römer, K. (2014, April). High-level states with CoAP: Giving
meaning to raw sensor values to support IoT applications. In 2014 IEEE Ninth
International Conference on Intelligent Sensors, Sensor Networks and Information
Processing (ISSNIP) (pp. 1-6). IEEE.
Min, W., Cui, H., Rao, H., Li, Z., & Yao, L. (2018). Detection of human falls on furniture
using scene analysis based on deep learning and activity characteristics. IEEE Access,
6, 9324-9335.
Mishra, D., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Dubey, R., & Wamba, S.
(2016). Vision, applications and future challenges of Internet of Things: A
21
Document Page
bibliometric study of the recent literature. Industrial Management & Data
Systems, 116(7), 1331-1355.
Monowar, M. I., Shakil, S. R., Kafi, A. H., &Rhaman, M. K. (2019). Framework of an
Intelligent, Multi Nodal and Secured RF Based Wireless Home Automation System
for Multifunctional Devices. Wireless Personal Communications, 1-16.
Monteriù, A., Prist, M., Frontoni, E., Longhi, S., Pietroni, F., Casaccia, S., ...&Pescosolido,
L. (2018). A Smart Sensing Architecture for Domestic Monitoring: Methodological
Approach and Experimental Validation. Sensors, 18(7), 2310.
Moosavi, S. R., Gia, T. N., Rahmani, A. M., Nigussie, E., Virtanen, S., Isoaho, J., &
Tenhunen, H. (2015). SEA: a secure and efficient authentication and authorization
architecture for IoT-based healthcare using smart gateways. Procedia Computer
Science, 52, 452-459.
Moraes, J., Rocha, M., Vasconcelos, G., VasconcelosFilho, J., de Albuquerque, V., &
Alexandria, A. (2018). Advances in photopletysmography signal analysis for
biomedical applications. Sensors, 18(6), 1894.
Moummadi, K., Abidar, R., Medromi, H., &Ziani, A. (2018, July). Secured Remote Control
of Greenhouse Based on Wireless Sensor Network and Multi Agent Systems. In
International Conference on Advanced Intelligent Systems for Sustainable
Development (pp. 427-439). Springer, Cham.
Nataliia, L., & Elena, F. (2015). Internet of things as a symbolic resource of power. Procedia-
Social and Behavioural Sciences, 166, 521-525.
22

Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.
Document Page
Navarro, J., Vidaña-Vila, E., Alsina-Pagès, R., & Hervás, M. (2018). Real-Time Distributed
architecture for remote acoustic elderly monitoring in Residential-Scale ambient
assisted living scenarios. Sensors, 18(8), 2492.
Nizam, Y., Mohd, M. N. H., & Jamil, M. M. A. (2017). Human fall detection from depth
images using position and velocity of subject. Procedia Computer Science, 105, 131-
137.
Nizam, Y., Mohd, M., & Jamil, M. (2018). Development of a user-adaptable human fall
detection based on fall risk levels using depth sensor. Sensors, 18(7), 2260.
Nweke, H. F., Teh, Y. W., Al-Garadi, M. A., &Alo, U. R. (2018). Deep learning algorithms
for human activity recognition using mobile and wearable sensor networks: State of
the art and research challenges. Expert Systems with Applications, 105, 233-261.
Pal, D., Funilkul, S., Charoenkitkarn, N., & Kanthamanon, P. (2018). Internet-of-things and
smart homes for elderly healthcare: An end user perspective. IEEE Access,
6,***********6.
Palumbo, F., La Rosa, D., Ferro, E., Bacciu, D., Gallicchio, C., Micheli, A., ...&Parodi, O.
(2017). Reliability and human factors in Ambient Assisted Living environments.
Journal of Reliable Intelligent Environments, 3(3), 139-157.
Panarello, A., Tapas, N., Merlino, G., Longo, F., & Puliafito, A. (2018). Blockchain and IoT
integration: A systematic survey. Sensors, 18(8), 2575.
Pease, S. G., Trueman, R., Davies, C., Grosberg, J., Yau, K. H., Kaur, N., ...& West, A.
(2018). An intelligent real-time cyber-physical toolset for energy and process
prediction and optimisation in the future industrial Internet of Things. Future
Generation Computer Systems, 79, 815-829.
23
Document Page
Perera, C., Liu, C. H., & Jayawardena, S. (2015). The emerging internet of things
marketplace from an industrial perspective: A survey. IEEE Transactions on
Emerging Topics in Computing, 3(4), 585-598.
Radu, V., Lane, N. D., Bhattacharya, S., Mascolo, C., Marina, M. K., & Kawsar, F. (2016,
September). Towards multimodal deep learning for activity recognition on mobile
devices. In Proceedings of the 2016 ACM International Joint Conference on Pervasive
and Ubiquitous Computing: Adjunct (pp. 185-188). ACM
Rinaldi, A. (2016). Biometrics' new identity—measuring more physical and biological traits:
Research into the characteristics that are unique to an individual is addressing the
need to correctly identify people in a variety of medical, social and security contexts.
EMBO reports, 17(1), 22-26.
Rodrigues, J. J., Segundo, D. B. D. R., Junqueira, H. A., Sabino, M. H., Prince, R. M., Al-
Muhtadi, J., & De Albuquerque, V. H. C. (2018). Enabling technologies for the
internet of health things. Ieee Access, 6,***********1.
Roselin, J., Latha, P., & Benitta, S. (2017). Maximizing the wireless sensor networks lifetime
through energy efficient connected coverage. Ad Hoc Networks, 62, 1-10.
Salamone, F., Belussi, L., Currò, C., Danza, L., Ghellere, M., Guazzi, G., ... & Meroni, I.
(2018). Integrated Method for Personal Thermal Comfort Assessment and
Optimization through Users’ Feedback, IoT and Machine Learning: A Case Study.
Sensors, 18(5), 1602
Sankar, S., & Srinivasan, P. (2018). Mobility and Energy Aware Routing Protocol for
Healthcare IoT Application. Research Journal of Pharmacy and Technology, 11(7),
3139-3144.
24
Document Page
Santoyo-Ramón, J., Casilari, E., & Cano-García, J. (2018). Analysis of a smartphone-based
architecture with multiple mobility sensors for fall detection with supervised learning.
Sensors, 18(4), 1155.
Sendra, S., Parra, L., Lloret, J., &Tomás, J. (2018). Smart system for children's chronic
illness monitoring. Information Fusion, 40, 76-86.
Shelby, Z., Hartke, K., & Bormann, C. (2014). The constrained application protocol
(CoAP) (No. RFC 7252).
Shirahama, K., & Grzegorzek, M. (2017). On the Generality of Codebook Approach for
Sensor-Based Human Activity Recognition. Electronics, 6(2), 44.
Shirali-Shahreza, S., &Ganjali, Y. (2018). Protecting home user devices with an SDN-based
firewall. IEEE Transactions on Consumer Electronics, 64(1), 92-100.
Siirtola, P., Koskimäki, H., &Röning, J. (2016, December). Personal models for eHealth-
improving user-dependent human activity recognition models using noise injection. In
2016 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-7). IEEE
Siirtola, P., Koskimäki, H., Mönttinen, H., &Röning, J. (2018). Using sleep time data from
wearable sensors for early detection of migraine attacks. Sensors, 18(5), 1374
Sodhro, A. H., Chen, L., Sekhari, A., Ouzrout, Y., & Wu, W. (2018). Energy efficiency
comparison between data rate control and transmission power control algorithms for
wireless body sensor networks. International Journal of Distributed Sensor Networks,
14(1),***************30.
Stojkoska, B. L. R., & Trivodaliev, K. V. (2017). A review of Internet of Things for smart
home: Challenges and solutions. Journal of Cleaner Production, 140, 1454-1464.
25

Paraphrase This Document

Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser
Document Page
Sun, J., Fu, Y., Li, S., He, J., Xu, C., & Tan, L. (2018). Sequential Human Activity
Recognition Based on Deep Convolutional Network and Extreme Learning Machine
Using Wearable Sensors. Journal of Sensors, 2018
Sun, W., Cai, Z., Li, Y., Liu, F., Fang, S., & Wang, G. (2018). Security and privacy in the
medical internet of things: a review. Security and Communication Networks, 2018.
Suresh, P., Daniel, J. V., Parthasarathy, V., & Aswathy, R. H. (2014, November). A state of
the art review on the Internet of Things (IoT) history, technology and fields of
deployment. In 2014 International Conference on Science Engineering and
Management Research (ICSEMR) (pp. 1-8). IEEE.
Tariq, M., Majeed, H., Beg, M. O., Khan, F. A., &Derhab, A. (2019). Accurate detection of
sitting posture activities in a secure IoT based assisted living environment. Future
Generation Computer Systems, 92, 745-757.
Tewari, A., & Gupta, B. B. (2017). Cryptanalysis of a novel ultra-lightweight mutual
authentication protocol for IoT devices using RFID tags. The Journal of
Supercomputing, 73(3), 1085-1102.
Thapliyal, H., Nath, R. K., &Mohanty, S. P. (2018). Smart home environment for mild
cognitive impairment population: solutions to improve care and quality of life. IEEE
Consumer Electronics Magazine, 7(1), 68-76.
Thompson, W. R. (2017). Worldwide survey of fitness trends for 2018: the CREP edition.
ACSM's Health & Fitness Journal, 21(6).
u Farooq, M., Waseem, M., Mazhar, S., Khairi, A., & Kamal, T. (2015). A Review on
Internet of Things (IoT). International Journal of Computer Applications, 113(1), 1-7.
26
Document Page
Uddin, M. Z. (2019). A wearable sensor-based activity prediction system to facilitate edge
computing in smart healthcare system. Journal of Parallel and Distributed Computing,
123, 46-53
Vasilomanolakis, E., Daubert, J., Luthra, M., Gazis, V., Wiesmaier, A., & Kikiras, P. (2015,
September). On the security and privacy of Internet of Things architectures and
systems. In 2015 International Workshop on Secure Internet of Things (SIoT) (pp. 49-
57). IEEE.
Wan, J., Al-awlaqi, M. A., Li, M., O’Grady, M., Gu, X., Wang, J., & Cao, N. (2018).
Wearable IoT enabled real-time health monitoring system. ******ASIP Journal on
Wireless Communications and Networking, 2018(1), 298.
Wang, K. H., Chen, C. M., Fang, W., & Wu, T. Y. (2017). A secure authentication scheme
for Internet of Things. Pervasive and Mobile Computing, 42, 15-26.
Weber, R. H. (2010). Internet of Things–New security and privacy challenges. Computer law
& security review, 26(1), 23-30.
Willy, R. W. (2018). Innovations and pitfalls in the use of wearable devices in the prevention
and rehabilitation of running related injuries. Physical Therapy in Sport, 29, 26-33.
Wilson, C., Hargreaves, T., & Hauxwell-Baldwin, R. (2017). Benefits and risks of smart
home technologies. Energy Policy, 103, 72-83.
Woo, M. W., Lee, J., & Park, K. (2018). A reliable IoT system for personal healthcare
devices. Future Generation Computer Systems, 78, 626-640
Wu, F., Wu, T., & Yuce, M. (2019). An Internet-of-Things (IoT) Network System for
Connected Safety and Health Monitoring Applications. Sensors, 19(1), 21.
27
Document Page
Yacchirema, D. C., Sarabia-Jácome, D., Palau, C. E., & Esteve, M. (2018). A smart system
for sleep monitoring by integrating IoT with big data analytics. IEEE Access,
6,***********1.
Yacchirema, D., de Puga, J. S., Palau, C., &Esteve, M. (2019). Fall detection system for
elderly people using IoT and ensemble machine learning algorithm. Personal and
Ubiquitous Computing, 1-17.Doi: https://doi.org/10.1007/s***************-8
Yang, X., &Tian, Y. (2017). Super normal vector for human activity recognition with depth
cameras. IEEE transactions on pattern analysis and machine intelligence, 39(5), 1028-
1039.
Yang, X., Shah, S. A., Ren, A., Fan, D., Zhao, N., Zheng, S., ... & Abbasi, Q. H. (2018). S-
band sensing-based motion assessment framework for cerebellar dysfunction patients.
IEEE Sensors Journal.
Yang, Z., Zhou, Q., Lei, L., Zheng, K., & Xiang, W. (2016). An IoT-cloud based wearable
ECG monitoring system for smart healthcare. Journal of medical systems, 40(12),
286.
Yaqoob, I., Ahmed, E., Hashem, I. A. T., Ahmed, A. I. A., Gani, A., Imran, M., & Guizani,
M. (2017). Internet of things architecture: Recent advances, taxonomy, requirements,
and open challenges. IEEE wireless communications, 24(3), 10-16.
Yue, X., Wang, H., Jin, D., Li, M., & Jiang, W. (2016). Healthcare data gateways; found
healthcare intelligence on blockchain with novel privacy risk control. Journal of
medical systems, 40(10), 218.
Zang, W., Miao, F., Gravina, R., Sun, F., Fortino, G., & Li, Y. (2019). CMDP-based
intelligent transmission for wireless body area network in remote health monitoring.
28

Secure Best Marks with AI Grader

Need help grading? Try our AI Grader for instant feedback on your assignments.
Document Page
N******al Computing and Applications, 1-9. Doi: https://doi-
org.ezproxy.csu.edu.au/10.1007/s***************-x
Zhang, Z. K., Cho, M. C. Y., Wang, C. W., Hsu, C. W., Chen, C. K., & Shieh, S. (2014,
November). IoT security: ongoing challenges and research opportunities. In 2014
IEEE 7th international conference on service-oriented computing and
applications (pp. 230-234). IEEE.
.
29
1 out of 29
circle_padding
hide_on_mobile
zoom_out_icon
[object Object]

Your All-in-One AI-Powered Toolkit for Academic Success.

Available 24*7 on WhatsApp / Email

[object Object]