SOA-FOG: Secure service-oriented edge computing architecture for smart health big data analytics

Barik, Rabindra K.; Dubey, Harishchandra; Mankodiya, Kunal

doi:10.1109/globalsip.2017.8308688

Cited by 46 publications

(10 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A three-tier architecture for the management of big data through SOA-FOG was presented. e research gives security information based on the client layer, cloud layer, and fog layer [75]. e Complexity study presented a comprehensive report of the industry 4.0 for addressing the challenges in the existing mHealth system and then provided a mobile smart health system known as mHealth 4.0 for providing effective solution based on big data for healthcare [76].…”

Section: Research In the Area Of Healthcare Based On Big Data And Iotmentioning

confidence: 99%

Parallel Computing for Efficient and Intelligent Industrial Internet of Health Things: An Overview

et al. 2021

View full text Add to dashboard Cite

Internet of Things (IoT) is expanding and evolves into all aspects of the society. Research and developments in the field of IoT have shown the possibility of producing huge volume of data and computation among different devices of the IoT. The data collected from IoT devices are transferred to a central server which can further be retrieved and accessed by the service providers for analyzing, processing, and using. Industrial Internet of Health Things (IIoHT) is the expansion of the Internet of Health Things (IoHT) which plays an important role in observing, consulting, monitoring, and treatment process of remote exchange data processes. The linkage of computation and interoperability are supported through various intelligent sensors, controllers, and actuators. The role of parallel computing for efficient and Intelligent Industrial Internet of Health Things is obvious to analyze and process different healthcare situations. A detailed overview of this existing literature is needed through which the research community will provide new solutions for efficient healthcare with the help of IoT based on parallel computing. Therefore, the current study presents a detailed overview of the existing literature for facilitating IIoHT.

show abstract

Section: Research In the Area Of Healthcare Based On Big Data And Iotmentioning

confidence: 99%

Parallel Computing for Efficient and Intelligent Industrial Internet of Health Things: An Overview

et al. 2021

View full text Add to dashboard Cite

show abstract

“…However, these studies did not take the communication and relationship inter-human under the proposed cloud-assisted IoMTs framework. Unlike cloud computing, fog computing is much closer to the users in IoMTs and can improve throughout and decrease the delay [98]- [100]. Both cloudassisted and fog-assisted IoMTs in health monitoring are in the early stage while the energy-efficient and delay-aware mechanisms need to be developed deeply.…”

Section: A Cloud Computing and Fog Computingmentioning

confidence: 99%

Health Monitoring Based on Internet of Medical Things: Architecture, Enabling Technologies, and Applications

et al. 2020

View full text Add to dashboard Cite

Recently, wearable devices have got increasing popularity in wide applications in medical and disaster rescue efforts to ensure the health and safety of users, which facilitates the development of the Internet of Medical Things (IoMT). Due to the posture alteration and mobility of users, the topology of the IoMT changes frequently, which increases the difficulty for resource allocation and routing strategy. In this paper, we respectively probe into the health monitoring architectures of the IoMT for both individual and group, allowing the monitored users to move at will. Furtherly, combined with the diversity of disaster rescuers, we build an IoMT-based disaster rescuer health monitoring system with searchers, doctors and porters. For each application, we point out the enabling technologies and demonstrate the existing researches. It is worth noting that the complexity of environment and high mobility of rescuers increase the probability of route breakage. Thus, this paper creatively addresses effective routing repair solutions for route breakage in IoMT-based disaster rescuer health monitoring system by exploiting the mobility of rescuers. Finally, we forecast three most likely directions in the field of IoMTs. INDEX TERMS IoMT, health monitoring, individual, group, disaster rescuer.

show abstract

“…Cisco defined fog computing as a distributed paradigm that provides cloud-like services to the edge of the network. They have been applied in various areas like the analysis of geospatial data [32][33][34], designing augmented reality-based gaming applications [35], smart city applications like vehicular network management [36,37] for smart parking, and sensor data analysis [38][39][40][41][42][43].…”

Section: Related Workmentioning

confidence: 99%

DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks

2018

Self Cite

View full text Add to dashboard Cite

The use of wearable and Internet-of-Things (IoT) for smart and affordable healthcare is trending. In traditional setups, the cloud backend receives the healthcare data and performs monitoring and prediction for diseases, diagnosis, and wellness prediction. Fog computing (FC) is a distributed computing paradigm that leverages low-power embedded processors in an intermediary node between the client layer and cloud layer. The diagnosis for wellness and fitness monitoring could be transferred to the fog layer from the cloud layer. Such a paradigm leads to a reduction in latency at an increased throughput. This paper processes a fog-based deep learning model, DeepFog that collects the data from individuals and predicts the wellness stats using a deep neural network model that can handle heterogeneous and multidimensional data. The three important abnormalities in wellness namely, (i) diabetes; (ii) hypertension attacks and (iii) stress type classification were chosen for experimental studies. We performed a detailed analysis of proposed models’ accuracy on standard datasets. The results validated the efficacy of the proposed system and architecture for accurate monitoring of these critical wellness and fitness criteria. We used standard datasets and open source software tools for our experiments.

show abstract

SOA-FOG: Secure service-oriented edge computing architecture for smart health big data analytics

Cited by 46 publications

References 20 publications

Parallel Computing for Efficient and Intelligent Industrial Internet of Health Things: An Overview

Parallel Computing for Efficient and Intelligent Industrial Internet of Health Things: An Overview

Health Monitoring Based on Internet of Medical Things: Architecture, Enabling Technologies, and Applications

DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks

Contact Info

Product

Resources

About