IoT with Cloud Based End to End Secured Disease Diagnosis Model using Light Weight Cryptography and Gradient Boosting Tree

2020 ◽  
Vol 13 ◽  
Author(s):  
K. Shankar
Keyword(s):  
Internet Of Things ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Gradient Boosting ◽  
The Internet ◽  
Pima Indians ◽  
Healthcare Applications ◽  
Diagnosis Model ◽  
Consistent Performance ◽  
The Internet Of Things

Background: With the evolution of the Internet of Things (IoT) technology and connected devices employed in the medicinal domain, the different characteristics of the online healthcare applications become advantageous. Aim: The objective of this paper is to present an IoT and cloud-based secured disease diagnosis model. At present, various e-healthcare applications with the use of the Internet of Things (IoT) offers diverse dimensions and services online. Method: In this paper, an efficient IoT and cloud-based secured classification model are proposed for disease diagnosis. It is used to avail efficient and secured services to the people globally over online healthcare applications. The presented model includes an effective gradient boosting tree (GBT) based data classification and lightweight cryptographic technique named rectangle. The presented GBT–R model offers a better diagnosis in a secure way. Results: It is validated using the Pima Indians diabetes data, and extensive simulation takes place to verify the consistent performance of the employed GBT-R model. Conclusion: The experimental outcome strongly suggested that the presented model shows maximum performance with an accuracy of 94.92.

Future Aspects and Research Perspectives of the Internet of Things

2021 ◽  
pp. 1-18
Author(s):  
Harshit Bhardwaj ◽  
Pradeep Tomar ◽  
Aditi Sakalle ◽  
Taranjeet Singh ◽  
Divya Acharya ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Fog Computing ◽  
The Internet ◽  
Literature Analysis ◽  
Healthcare Applications ◽  
Web Based ◽  
Iot Applications ◽  
Research Perspectives ◽  
Software Production ◽  
The Internet Of Things

Fog computing has latency, particularly for healthcare applications, which is of the utmost importance. This research aims to be a comprehensive literature analysis of healthcare innovations for fog computing. All of these components involved special abilities. In sequence, developers must be qualified to write stable, healthy IoT programs in four distinct fields of software production: embedded, server, tablet, and web-based. Furthermore, the distributed results, IoT structure essence, dispersed abilities in programming play a deciding position. This chapter discusses the difficulties in creating the IoT method and summarizing findings and observations. Experiences of the need for and co-presence of various kinds of skills in software creation in the construction of IoT applications are discussed.

scholarly journals A Hybrid Energy Harvesting Design for On-Body Internet-of-Things (IoT) Networks

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 407 ◽  
Author(s):  
Omar A. Saraereh ◽  
Amer Alsaraira ◽  
Imran Khan ◽  
Bong Jun Choi
Keyword(s):  
Energy Harvesting ◽  
Internet Of Things ◽  
Thermal Energy ◽  
The Internet ◽  
Healthcare Applications ◽  
Hybrid Energy ◽  
Iot Devices ◽  
Hybrid Energy Harvesting ◽  
Monitoring Devices ◽  
The Internet Of Things

The Internet-of-things (IoT) has been gradually paving the way for the pervasive connectivity of wireless networks. Due to the ability to connect a number of devices to the Internet, many applications of IoT networks have recently been proposed. Though these applications range from industrial automation to smart homes, healthcare applications are the most critical. Providing reliable connectivity among wearables and other monitoring devices is one of the major tasks of such healthcare networks. The main source of power for such low-powered IoT devices is the batteries, which have a limited lifetime and need to be replaced or recharged periodically. In order to improve their lifecycle, one of the most promising proposals is to harvest energy from the ambient resources in the environment. For this purpose, we designed an energy harvesting protocol that harvests energy from two ambient energy sources, namely radio frequency (RF) at 2.4 GHz and thermal energy. A rectenna is used to harvest RF energy, while the thermoelectric generator (TEG) is employed to harvest human thermal energy. To verify the proposed design, extensive simulations are performed in Green Castalia, which is a framework that is used with the Castalia simulator in OMNeT++. The results show significant improvements in terms of the harvested energy and lifecycle improvement of IoT devices.

scholarly journals An IoT based SMART patient health monitoring system

2020 ◽  
Vol 18 (3) ◽  
pp. 1657
Author(s):  
C.R Srinivasan ◽  
Guru Charan ◽  
P Chenchu Sai Babu
Keyword(s):  
Internet Of Things ◽  
Early Stage ◽  
Well Being ◽  
Living System ◽  
The Body ◽  
The Internet ◽  
Physical And Mental Health ◽  
Healthcare Applications ◽  
Patient Health ◽  
The Internet Of Things

<span>Smart and connected health care is of specific significance in the spectrum of applications enabled the Internet of Things (IoT). Networked sensors, either embedded inside our living system or worn on the body, enable to gather rich information regarding our physical and mental health. In specific, the accessibility of information at previously unimagined scales and spatial longitudes combined with the new generation of smart processing algorithms can expedite an advancement in the medical field, from the current post-facto diagnosis and treatment of reactive framework, to an early-stage proactive paradigm for disease prognosis combined with prevention and cure as well as overall administration of well-being rather than ailment. This paper sheds some light on the current methods accessible in the Internet of Things (IoT) domain for healthcare applications. The proposed objective is to design and create a healthcare system centered on Mobile-IoT by collecting patient information from different sensors and alerting both the guardian and the doctor by sending emails and SMS in a timely manner. It remotely monitors the physiological parameters of the patient and diagnoses the illnesses swiftly. </span>

scholarly journals ADLAuth: Passive Authentication Based on Activity of Daily Living Using Heterogeneous Sensing in Smart Cities

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2466 ◽  
Author(s):  
Maryam Naseer Malik ◽  
Muhammad Awais Azam ◽  
Muhammad Ehatisham-Ul-Haq ◽  
Waleed Ejaz ◽  
Asra Khalid
Keyword(s):  
Internet Of Things ◽  
User Authentication ◽  
Smart Cities ◽  
Activity Patterns ◽  
Wearable Sensors ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
The Internet ◽  
Healthcare Applications ◽  
The Internet Of Things

The Internet of Things is a rapidly growing paradigm for smart cities that provides a way of communication, identification, and sensing capabilities among physically distributed devices. With the evolution of the Internet of Things (IoTs), user dependence on smart systems and services, such as smart appliances, smartphone, security, and healthcare applications, has been increased. This demands secure authentication mechanisms to preserve the users’ privacy when interacting with smart devices. This paper proposes a heterogeneous framework “ADLAuth” for passive and implicit authentication of the user using either a smartphone’s built-in sensor or wearable sensors by analyzing the physical activity patterns of the users. Multiclass machine learning algorithms are applied to users’ identity verification. Analyses are performed on three different datasets of heterogeneous sensors for a diverse number of activities. A series of experiments have been performed to test the effectiveness of the proposed framework. The results demonstrate the better performance of the proposed scheme compared to existing work for user authentication.

scholarly journals The Internet of Things in Geriatric Healthcare

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Deblu Sahu ◽  
Bikash Pradhan ◽  
Anwesha Khasnobish ◽  
Sarika Verma ◽  
Doman Kim ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Cost Effective ◽  
Healthcare Services ◽  
The Internet ◽  
Technological Advancement ◽  
Healthcare Applications ◽  
Geriatric Population ◽  
Healthcare Resources ◽  
Cost Efficient ◽  
The Internet Of Things

There is a significant increase in the geriatric population across the globe. With the increase in the number of geriatric people and their associated health issues, the need for larger healthcare resources is inevitable. Because of this, healthcare service-providing industries are facing a severe challenge. However, technological advancement in recent years has enabled researchers to develop intelligent devices to deal with the scarcity of healthcare resources. In this regard, the Internet of things (IoT) technology has been a boon for healthcare services industries. It not only allows the monitoring of the health parameters of geriatric patients from a remote location but also lets them live an independent life in a cost-efficient way. The current paper provides up-to-date comprehensive knowledge of IoT-based technologies for geriatric healthcare applications. The study also discusses the current trends, issues, challenges, and future scope of research in the area of geriatric healthcare using IoT technology. Information provided in this paper will be helpful to develop futuristic solutions and provide efficient cost-effective healthcare services to the needy.

scholarly journals A Systematic Analysis of Internet of Things in Healthcare Applications : A Review

2021 ◽  
Vol 23 (09) ◽  
pp. 112-125
Author(s):  
Shagun Arora ◽  
◽  
Gurvinder Singh ◽  
Keyword(s):  
Internet Of Things ◽  
Power Consumption ◽  
Healthcare System ◽  
Management Strategies ◽  
The Elderly ◽  
The Internet ◽  
Healthcare Applications ◽  
Systematic Analysis ◽  
Iot Devices ◽  
The Internet Of Things

The Internet of Things (IoT) is a network of physical devices, software, and hardware that communicate with one another. As the population ages, healthcare resources become scarce, and medical expenses rise, IoT-based solutions must be adapted to meet these issues in healthcare. To enhance the monitoring efficiency of the IoT-based healthcare system, several studies have been conducted. In this paper, the architecture utilized in the IoT, particularly cloud-integrated systems and security in IoT devices is explored. Factors like accuracy and power consumption are major concerns in the Internet of Things, therefore research projects aimed at enhancing the performance of IoT-based healthcare systems are highlighted. In this work, data management strategies in an IoT-based healthcare system with cloud capabilities are thoroughly examined. The performance of the IoT-based healthcare system is examined, as well as its benefits and drawbacks. Moreover, a comparative analysis is also done on some existing technologies that are utilized in healthcare. It has been observed from past studies that IoT protocol such as 6LoWPAN is mostly utilized in the domain of health care. The majority of research studies are effective in detecting many symptoms and accurately predicting illnesses. The IoT-based healthcare system built specifically for the elderly is an effective way to keep track of their medical concerns. High power consumption, a scarcity of resources, and security concerns major drawbacks of current systems are included in the proposed study.

scholarly journals Robustness Evaluations of Sustainable Machine Learning Models against Data Poisoning Attacks in the Internet of Things

2020 ◽  
Vol 12 (16) ◽  
pp. 6434 ◽  
Author(s):  
Corey Dunn ◽  
Nour Moustafa ◽  
Benjamin Turnbull
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Large Scale ◽  
Gradient Boosting ◽  
The Internet ◽  
Learning Models ◽  
Detection Rates ◽  
Ongoing Research ◽  
The Internet Of Things ◽  
Machine Learning Models

With the increasing popularity of the Internet of Things (IoT) platforms, the cyber security of these platforms is a highly active area of research. One key technology underpinning smart IoT systems is machine learning, which classifies and predicts events from large-scale data in IoT networks. Machine learning is susceptible to cyber attacks, particularly data poisoning attacks that inject false data when training machine learning models. Data poisoning attacks degrade the performances of machine learning models. It is an ongoing research challenge to develop trustworthy machine learning models resilient and sustainable against data poisoning attacks in IoT networks. We studied the effects of data poisoning attacks on machine learning models, including the gradient boosting machine, random forest, naive Bayes, and feed-forward deep learning, to determine the levels to which the models should be trusted and said to be reliable in real-world IoT settings. In the training phase, a label modification function is developed to manipulate legitimate input classes. The function is employed at data poisoning rates of 5%, 10%, 20%, and 30% that allow the comparison of the poisoned models and display their performance degradations. The machine learning models have been evaluated using the ToN_IoT and UNSW NB-15 datasets, as they include a wide variety of recent legitimate and attack vectors. The experimental results revealed that the models’ performances will be degraded, in terms of accuracy and detection rates, if the number of the trained normal observations is not significantly larger than the poisoned data. At the rate of data poisoning of 30% or greater on input data, machine learning performances are significantly degraded.

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