Toward an efficient healthcare CloudIoT architecture by using a game theory approach

2019 ◽  
Vol 27 (3) ◽  
pp. 189-200 ◽  
Author(s):  
Douglas Dyllon Jeronimo de Macedo ◽  
Gustavo Medeiros de Araújo ◽  
Moisés Lima Dutra ◽  
Silvana Toriani Dutra ◽  
Álvaro Guillermo Rojas Lezana

The increasing adoption of the Internet of things and cloud computing in recent years provided the increasing development and improvement of various well-known approaches, such as the ambient assisted living approach. The merging of Internet of things and cloud brought about the so-called CloudIoT paradigm. CloudIoT intends to extend both technologies to make possible developing the next generation of smart environments, such as healthcare applications. New healthcare applications demand an increasing capacity of resources for storing, processing, and transmitting data. Looking at this scenario, along with the growing number of devices connected to the Internet of things, we must consider providing mechanisms to mitigate the excessive data offloading on the network, the latency between nodes, and even the unnecessary waste of computing power. In this article, we present an efficient and effective CloudIoT-based healthcare architecture for ambient assisted living environments. The innovation of our approach lies on the use of a game theory approach (by means of a stochastic search algorithm) to improve efficiency and latency of the CloudIoT network. This proposal aims to provide better availability levels to the whole environment. Experiments performed through simulation have shown us a remarkable improvement of network parameters, by applying a stochastic search algorithm called Gur game, when compared to a baseline application.

2019 ◽  
Vol 11 (12) ◽  
pp. 259 ◽  
Author(s):  
Rytis Maskeliūnas ◽  
Robertas Damaševičius ◽  
Sagiv Segal

The internet of things (IoT) aims to extend the internet to real-world objects, connecting smart and sensing devices into a global network infrastructure by connecting physical and virtual objects. The IoT has the potential to increase the quality of life of inhabitants and users of intelligent ambient assisted living (AAL) environments. The paper overviews and discusses the IoT technologies and their foreseen impacts and challenges for the AAL domain. The results of this review are summarized as the IoT based gerontechnology acceptance model for the assisted living domain. The model focuses on the acceptance of new technologies by older people and underscores the need for the adoption of the IoT for the AAL domain.


2020 ◽  
Vol 57 (6) ◽  
pp. 102308 ◽  
Author(s):  
Christian Esposito ◽  
Oscar Tamburis ◽  
Xin Su ◽  
Chang Choi

Author(s):  
Harshit Bhardwaj ◽  
Pradeep Tomar ◽  
Aditi Sakalle ◽  
Taranjeet Singh ◽  
Divya Acharya ◽  
...  

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.


Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 407 ◽  
Author(s):  
Omar A. Saraereh ◽  
Amer Alsaraira ◽  
Imran Khan ◽  
Bong Jun Choi

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.


Author(s):  
C.R Srinivasan ◽  
Guru Charan ◽  
P Chenchu Sai Babu

<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>


Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2466 ◽  
Author(s):  
Maryam Naseer Malik ◽  
Muhammad Awais Azam ◽  
Muhammad Ehatisham-Ul-Haq ◽  
Waleed Ejaz ◽  
Asra Khalid

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.


Author(s):  
K. Shankar

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.


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