Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

Farahani, Bahar; Firouzi, Farshad; Chang, Victor; Badaroglu, Mustafa; Constant, Nicholas; Mankodiya, Kunal

doi:10.1016/j.future.2017.04.036

Cited by 818 publications

(413 citation statements)

References 104 publications

Supporting

Mentioning

371

Contrasting

Unclassified

Order By: Relevance

“…Bluetooth Low Energy -BLEand Radio-Frequency Identification -RFID-). However, it is necessary to integrate these devices and the data they collect into a new health model [36,37]. Such a model should allow the integration of physicians, health professionals, clinics, and the patients.…”

Section: Healthcarementioning

confidence: 99%

Fog orchestration for the Internet of Everything: state-of-the-art and research challenges

Velásquez

Abreu

Assis

et al. 2018

J Internet Serv Appl

View full text Add to dashboard Cite

Recent developments in telecommunications have allowed drawing new paradigms, including the Internet of Everything, to provide services by the interconnection of different physical devices enabling the exchange of data to enrich and automate people's daily activities; and Fog computing, which is an extension of the well-known Cloud computing, bringing tasks to the edge of the network exploiting characteristics such as lower latency, mobility support, and location awareness. Combining these paradigms opens a new set of possibilities for innovative services and applications; however, it also brings a new complex scenario that must be efficiently managed to properly fulfill the needs of the users. In this scenario, the Fog Orchestrator component is the key to coordinate the services in the middle of Cloud computing and Internet of Everything. In this paper, key challenges in the development of the Fog Orchestrator to support the Internet of Everything are identified, including how they affect the tasks that a Fog service Orchestrator should perform. Furthermore, different service Orchestrator architectures for the Fog are explored and analyzed in order to identify how the previously listed challenges are being tackled. Finally, a discussion about the open challenges, technological directions, and future of the research on this subject is presented.

show abstract

Section: Healthcarementioning

confidence: 99%

Fog orchestration for the Internet of Everything: state-of-the-art and research challenges

Velásquez

Abreu

Assis

et al. 2018

J Internet Serv Appl

View full text Add to dashboard Cite

show abstract

“…To meet the increasing health demands of an aging population with chronic diseases, Farahani et al designed a multilayer architecture of IoT eHealth ecosystem including device, fog computing, and Cloud to empower handling a complex data in terms of its variety, speed, and latency. The proposed fog‐driven IoT architecture is implemented by various case examples of services and applications including mobile health, assisted living, e‐medicine, and corresponding challenges such as data management, scalability, regulations, interoperability, security, and privacy are also addressed.…”

Section: Related Workmentioning

confidence: 99%

Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN

Dan

Shah

et al. 2018

Softw Pract Exp

View full text Add to dashboard Cite

Summary Brain healthcare, when supported by Internet of Things, can perform online and accurate analysis of brain big data for the classification of multivariate Electroencephalogram (EEG), which is a prerequisite for the recent boom in neurofeedback applications and clinical practices. However, it remains a grand research challenge due to (1) the embedded intensive noises and the intrinsic nonstationarity determined by the evolution of brain states; and (2) the lack of a user‐friendly computing platform to sustain the complicated analytics. This study presents the design of an online EEG classification system aided by Cloud centering on a lightweight Convolutional Neural Network (CNN). The system incrementally trains the CNN on Cloud and enables hot deployment of the trained classifier without the need to restart the gateway to adapt to the users' needs. The classifier maintains a High Convolutional Layer to gain the ability of processing high‐dimensional EEG segments. The number of hidden layers is minimized to ensure the efficiency of training. The lightweight CNN adopts an “hourglass” block of fully connected layers to reduce the number of neurons quickly toward the output end. A case study of depression evaluation has been performed against raw EEG datasets to distinguish between (1) Healthy and Major Depression Disorder with an accuracy, sensitivity, and specificity of [98.59% ± 0.28%], [97.77% ± 0.63%], and [99.51% ± 0.19%], respectively; and (2) Effective and Noneffective treatment outcome with an accuracy, sensitivity, and specificity of [99.53% ± 0.002%], [99.50% ± 0.01%], and [99.58% ± 0.02%], respectively. The results show that the classification can be completed several magnitudes faster when EEG is collected on the gateway (several milliseconds vs. 4 seconds).

show abstract

“…Last but not least, recent research points out the emergence of IoT-driven data acquisition systems [78,89].…”

Section: Challenges and Open Issuesmentioning

confidence: 99%

Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities

Obinikpo

Kantarcı

2017

JSAN

View full text Add to dashboard Cite

Abstract:With the advent of the Internet of Things (IoT) concept and its integration with the smart city sensing, smart connected health systems have appeared as integral components of the smart city services. Hard sensing-based data acquisition through wearables or invasive probes, coupled with soft sensing-based acquisition such as crowd-sensing results in hidden patterns in the aggregated sensor data. Recent research aims to address this challenge through many hidden perceptron layers in the conventional artificial neural networks, namely by deep learning. In this article, we review deep learning techniques that can be applied to sensed data to improve prediction and decision making in smart health services. Furthermore, we present a comparison and taxonomy of these methodologies based on types of sensors and sensed data. We further provide thorough discussions on the open issues and research challenges in each category.

show abstract

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

Cited by 818 publications

References 104 publications

Fog orchestration for the Internet of Everything: state-of-the-art and research challenges

Fog orchestration for the Internet of Everything: state-of-the-art and research challenges

Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN

Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities

Contact Info

Product

Resources

About