Enabling technologies for fog computing in healthcare IoT systems

Mutlag, Ammar Awad; Ghani, Mohd Khanapi Abd; Arunkumar, N.; Mohammed, Mazin Abed; Othman, Mohd Fairuz Iskandar

doi:10.1016/j.future.2018.07.049

Cited by 451 publications

(187 citation statements)

References 91 publications

Supporting

Mentioning

173

Contrasting

Unclassified

Order By: Relevance

“…[16][17][18] The information collected by the perception layer can be uploaded to the microprocessor unit through the data serial port. Effective extraction and processing of information data is an important part of the implementation of the IoT technology.…”

Section: Central Centralized Control System Designmentioning

confidence: 99%

Retracted: Design and implementation of music teaching assistant platform based on Internet of Things

2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

With the development of society, Internet of Things (IoT) technology has attracted more and more attention. This paper uses IoT technology to enhance the music teaching assistant platform. Firstly, through analyzing users' requirements for music teaching assistant platform, based on IoT technology, the assistant platform is analyzed, and the wireless information transmission system is established. Secondly, aiming at the consumption of router nodes, the consumption model of nodes is established. By analyzing the energy consumption and service life of router nodes, it is concluded that traditional IoT has great disadvantages. Then, an improved repair strategy of IoT is proposed according to immune algorithm. By comparing three kinds of wireless sensor networks, the improved repair strategy of IoT is better when the number of router nodes is more than 400. When it comes to saving energy, there are great advantages in quantity consumption. Finally, by simulating and analyzing the fault repair of router nodes, it is concluded that the number of effective router nodes in traditional IoT decreases the fastest with the number of simulated runs, followed by energy‐gathering IoT, and the lifetime of effective router nodes in IoT is the longest with the energy‐gathering improved by immune algorithm. This research mainly focuses on the energy consumption of router nodes in the perception layer of IoT, as well as the network immunity and repair mechanism of the system. It improves and designs the traditional music teaching assistant platform, which has certain practical significance.

show abstract

Section: Central Centralized Control System Designmentioning

confidence: 99%

Retracted: Design and implementation of music teaching assistant platform based on Internet of Things

2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…The assessment is at basis of calculating four performance metrics [18][19][20] : Structural Similarity Index (SSIM), Probabilistic Rand Index (PRI), Variation of Information (VoI), and Global Consistency Error (GCE), which are greatly employed toward assessing segmentation schemes, which could be understood from the following section: The assessment is at basis of calculating four performance metrics [18][19][20] : Structural Similarity Index (SSIM), Probabilistic Rand Index (PRI), Variation of Information (VoI), and Global Consistency Error (GCE), which are greatly employed toward assessing segmentation schemes, which could be understood from the following section:…”

Section: New Approach and Threshold Vs Manually Traced Ground Truthmentioning

confidence: 99%

“…The performance of the new approach has been evaluated originally by means of first database constituting 274 images with related ground-truth that are represented previously for 60 images. The assessment is at basis of calculating four performance metrics [18][19][20] : Structural Similarity Index (SSIM), Probabilistic Rand Index (PRI), Variation of Information (VoI), and Global Consistency Error (GCE), which are greatly employed toward assessing segmentation schemes, which could be understood from the following section:…”

Section: New Approach and Threshold Vs Manually Traced Ground Truthmentioning

confidence: 99%

Fully automatic model‐based segmentation and classification approach for MRI brain tumor using artificial neural networks

Arunkumar

Mohammed

Mostafa

et al. 2018

Concurrency and Computation

Self Cite

115

View full text Add to dashboard Cite

The accuracy of brain tumor diagnosis based on medical images is greatly affected by the segmentation process. The segmentation determines the tumor shape, location, size, and texture. In this study, we proposed a new segmentation approach for brain tissues using MR images. The method includes three computer vision fiction strategies which are enhancing images, segmenting images, and filtering out non ROI based on the texture and HOG features. A fully automatic model-based trainable segmentation and classification approach for MRI brain tumour using artificial neural networks to precisely identifying the location of the ROI. Therefore, the filtering out non ROI process have used in view of histogram investigation to avert the non ROI and select the correct object in brain MRI. However, identification the tumor kind utilizing the texture features. A total of 200 MRI cases are utilized for the comparing between automatic and manual segmentation procedure. The outcomes analysis shows that the fully automatic model-based trainable segmentation over performs the manual method and the brain identification utilizing the ROI texture features. The recorded identification precision is 92.14%, with 89 sensitivity and 94 specificity.

show abstract

“…5,6 To be specific, instead of sending all data collected to the cloud, FC suggests to processing data at the edges. FC can be used in several related domains, including big data analytics, 7,8 Internet of Things (IoT), 9,10 wireless sensor networks, [11][12][13] smart homes, 14 smart cities, 15 health care, 16 and mobile/wearable computing. 17,18 The benefits of FC are increasing throughput, reducing latency, saving energy, consolidating resources, and enhancing privacy and security.…”

Section: Introductionmentioning

confidence: 99%

Quality of service‐aware approaches in fog computing

Kashani

Rahmani

Navimipour

2020

Int J Communication

View full text Add to dashboard Cite

Summary In recent years, fog computing, a novel paradigm, has emerged for location and latency‐sensitive applications. It is a powerful complement for cloud computing that enables provisioning services and resources outside the cloud near the end devices. In a fog system, the existence of several nonhomogenous devices, which are potentially mobile, led to quality of service (QoS) worries. QoS‐aware approaches are presented in various parts of the fog system, and several different QoS factors are taken into account. In spite of the importance of QoS in fog computing, no comprehensive study on QoS‐aware approaches exists in fog computing. Hence, this paper reviews the current research used to guarantee QoS in fog computing. This paper investigates the QoS‐ensuring techniques that fall into three categories: service/resource management, communication management, and application management (published between 2013 and October 2018). Regarding the selected approaches, this paper represents merits, demerits, tools, evaluation types, and QoS factors. Finally, on the basis of the reviewed studies, we suggest some open issues and challenges which are worth further studying and researching in QoS‐aware approaches in fog computing.

show abstract

Enabling technologies for fog computing in healthcare IoT systems

Cited by 451 publications

References 91 publications

Retracted: Design and implementation of music teaching assistant platform based on Internet of Things

Retracted: Design and implementation of music teaching assistant platform based on Internet of Things

Fully automatic model‐based segmentation and classification approach for MRI brain tumor using artificial neural networks

Quality of service‐aware approaches in fog computing

Contact Info

Product

Resources

About