Fog Computing in Healthcare Internet of Things: A Case Study on ECG Feature Extraction

Gia, Tuan Nguyen; Jiang, Mingzhe; Rahmani, Amir-Mohammad; Westerlund, Tomi; Liljeberg, Pasi; Tenhunen, Hannu

doi:10.1109/cit/iucc/dasc/picom.2015.51

Cited by 345 publications

(195 citation statements)

References 13 publications

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“…Utilizing fog as a smart gateway to give services for computing in some complicated methods like embedded data mining and prioritized storage has also been proved [95].…”

Section: Pros Consmentioning

confidence: 99%

“…Those extended systems frequently can monitor whole traffic and transmit all monitored data in real-time environment through smart devices and sensors, which preempt the whole traffic and provide safety to commuters. Some application works for fog structure, in the domain of healthcare high latency intolerant and augmented reality, have been proposed [87,94,95] that can be utilized for improvement of websites response through preprocessing and caching functionality. Saharan and Kumar [96] explained their work in four divergent domains of fog computing applications (e.g., STL system, wireless and actuator networks, smart grid, and connected vehicles).…”

Section: Fog Proposed Application Taxonomymentioning

confidence: 99%

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Fog Computing: An Overview of Big IoT Data Analytics

Anawar

Wang

Zia

et al. 2018

Wireless Communications and Mobile Computing

152

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A huge amount of data, generated by Internet of Things (IoT), is growing up exponentially based on nonstop operational states. Those IoT devices are generating an avalanche of information that is disruptive for predictable data processing and analytics functionality, which is perfectly handled by the cloud before explosion growth of IoT. Fog computing structure confronts those disruptions, with powerful complement functionality of cloud framework, based on deployment of micro clouds (fog nodes) at proximity edge of data sources. Particularly big IoT data analytics by fog computing structure is on emerging phase and requires extensive research to produce more proficient knowledge and smart decisions. This survey summarizes the fog challenges and opportunities in the context of big IoT data analytics on fog networking. In addition, it emphasizes that the key characteristics in some proposed research works make the fog computing a suitable platform for new proliferating IoT devices, services, and applications. Most significant fog applications (e.g., health care monitoring, smart cities, connected vehicles, and smart grid) will be discussed here to create a well-organized green computing paradigm to support the next generation of IoT applications.

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“…Utilizing fog as a smart gateway to give services for computing in some complicated methods like embedded data mining and prioritized storage has also been proved [95].…”

Section: Pros Consmentioning

confidence: 99%

Section: Fog Proposed Application Taxonomymentioning

confidence: 99%

Fog Computing: An Overview of Big IoT Data Analytics

Anawar

Wang

Zia

et al. 2018

Wireless Communications and Mobile Computing

152

View full text Add to dashboard Cite

show abstract

“…Fog computing is usually recommended to address the above problem. When Fog computing is usually put in place at the edge of a circle, it offers a superior very low latency, area awareness, and also helps quality-ofservices (QoS) for internet streaming and also real-time software [2]. Common these include industrial automation, transfer, and also systems associated with receptors and also actuators.…”

Section: Need Of Fogmentioning

confidence: 99%

“…Fog equipment can be given at every flooring and may work with others about higher level regarding actuation. With Fog computing applied in this kind of circumstance, good houses can preserve their materials, internal and external surroundings to save power, mineral water as well as other assets [2].…”

Section: Application Of Fog Computingmentioning

confidence: 99%

A Review on Various IOT Analytics Techniques for Bridge Failure Detection in Fog Computing

Amar¹,

Kaur²

2017

IJCA

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IoT analytics is the alteration of enormous quantities of information in significant styles and rules. To expect describing the previous in addition to calculating the long term via data analysis. IoT analytics is a multi-disciplinary area which mixes, machine learning, research, data source technologies and artificial intelligence. IOT analytics is usually achieved in several stages of development: Business enterprise knowing, Information knowing, Information preparing, Acting, Review, and Deployment. There are various IOT analytics methods when Affiliation, Distinction, Clustering, Sensation problems System and Regression. This research work presents different IOT analytics algorithms intended for effectively mining the particular health-related facts set. IOT analytics algorithms have grown to be favorite every day live apps including breach prognosis procedure, diabetes mellitus exploration, e-mail spam distinction etc. In this paper we discuss about the various techniques of IOT and also IOT analytics techniques for bridge failure detection IOTs. The overall objective of this paper is to bridge failure detection in IOT KeywordsFog computing; bridge failure detection;Internet Of Things (IOT).

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“…With the help of Fog computing, healthcare systems provide services from a nearby location, store heterogeneous data, consists of smart low power devices, and are able to switch among various communication protocols as well as facilitating distributed computing [57]. Another application of Fog computing in healthcare includes Electrocardiogram (ECG) feature extraction to diagnose cardiac diseases [58]. This involves medical sensors transmitting data to a Fog layer that stores data in distributed databases, extract ECG features, and providing a graphical interface to display results in real-time.…”

Section: Improving Healthcare Systems and Their Performancementioning

confidence: 99%

Fog computing security: a review of current applications and security solutions

2017

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Fog computing is a new paradigm that extends the Cloud platform model by providing computing resources on the edges of a network. It can be described as a cloud-like platform having similar data, computation, storage and application services, but is fundamentally different in that it is decentralized. In addition, Fog systems are capable of processing large amounts of data locally, operate on-premise, are fully portable, and can be installed on heterogeneous hardware. These features make the Fog platform highly suitable for time and location-sensitive applications. For example, Internet of Things (IoT) devices are required to quickly process a large amount of data. This wide range of functionality driven applications intensifies many security issues regarding data, virtualization, segregation, network, malware and monitoring. This paper surveys existing literature on Fog computing applications to identify common security gaps. Similar technologies like Edge computing, Cloudlets and Micro-data centres have also been included to provide a holistic review process. The majority of Fog applications are motivated by the desire for functionality and end-user requirements, while the security aspects are often ignored or considered as an afterthought. This paper also determines the impact of those security issues and possible solutions, providing future security-relevant directions to those responsible for designing, developing, and maintaining Fog systems.

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Fog Computing in Healthcare Internet of Things: A Case Study on ECG Feature Extraction

Cited by 345 publications

References 13 publications

Fog Computing: An Overview of Big IoT Data Analytics

Fog Computing: An Overview of Big IoT Data Analytics

A Review on Various IOT Analytics Techniques for Bridge Failure Detection in Fog Computing

Fog computing security: a review of current applications and security solutions

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