An Edge-Fog-Cloud Architecture of Streaming Analytics for Internet of Things Applications

Cao, Hung

doi:10.3390/s19163594

Cited by 30 publications

(18 citation statements)

References 15 publications

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“…The goal is to minimize end to end network delay of applications. The RSAbased authentication and authorization for IoV application in distributed edge computing are devised in [9,[11][12][13][14][15][16].…”

Section: Related Delay Optimal Iodv Schemesmentioning

confidence: 99%

SFDWA: Secure and Fault-Tolerant Aware Delay Optimal Workload Assignment Schemes in Edge Computing for Internet of Drone Things Applications

Lakhan

Elhoseny

Mohammed

et al. 2022

Wireless Communications and Mobile Computing

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The number of automobiles has rapidly increased in recent years. To broaden inhabitant’s travel options, push transportation infrastructures to their limitations. With the rapid expansion of vehicles, traffic congestion and car accidents are all common occurrences in the city. The Internet of drone vehicle things (IoDV) has developed a new paradigm for improving traffic situations in urban areas. However, edge computing has the following issues such as fault-tolerant and security-enabled delay optimal workload assignment. The study formulates the workload assignment problem for IoV applications based on linear integer programming. The study devises the fault-tolerant and security delay optimal workload assignment (SFDWA) schemes that determine optimal workload assignment in edge computing. The goal is to minimize average response time, which combines network, computation, security, and fault-tolerant delay. Simulation results show that the proposed schemes gain 15% optimal workload assignment for IoV application compared to existing studies.

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Section: Related Delay Optimal Iodv Schemesmentioning

confidence: 99%

SFDWA: Secure and Fault-Tolerant Aware Delay Optimal Workload Assignment Schemes in Edge Computing for Internet of Drone Things Applications

Lakhan

Elhoseny

Mohammed

et al. 2022

Wireless Communications and Mobile Computing

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“…Many context models will require simple machine learning algorithms such as the linear Spanish inquisition protocol (L-SIP) which has been applied to reduce data transmission; filtered state classification (ClassAct) as a human posture/activity classifier based on decision tree; and time-discounted histogram encoding (Bare Necessities) which is used for summarizing the relative time spent in given contexts [94]; • Mobility and geographic distribution: These are indispensable requirements for context intelligence; however, an anticipatory learning system also requires a rich scenario of communication and interaction between all available computational resources. To achieve this, a priori data pipelines must be designed that will support an analytics everywhere framework [95][96][97]; • Heterogeneity and interoperability: Obviously, terminal devices in the IoMT system can collect data with different timestamps, formats, and locations. Additionally, the edge network computing devices which deploy the IoT gateways could seamlessly support the interoperability between terminal devices.…”

Section: Context Intelligence At the Fog Layer Of A Networkmentioning

confidence: 99%

A Holistic Overview of Anticipatory Learning for the Internet of Moving Things: Research Challenges and Opportunities

Cao

2020

IJGI

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The proliferation of Internet of Things (IoT) systems has received much attention from the research community, and it has brought many innovations to smart cities, particularly through the Internet of Moving Things (IoMT). The dynamic geographic distribution of IoMT devices enables the devices to sense themselves and their surroundings on multiple spatio-temporal scales, interact with each other across a vast geographical area, and perform automated analytical tasks everywhere and anytime. Currently, most of the geospatial applications of IoMT systems are developed for abnormal detection and control monitoring. However, it is expected that, in the near future, optimization and prediction tasks will have a larger impact on the way citizens interact with smart cities. This paper examines the state of the art of IoMT systems and discusses their crucial role in supporting anticipatory learning. The maximum potential of IoMT systems in future smart cities can be fully exploited in terms of proactive decision making and decision delivery via an anticipatory action/feedback loop. We also examine the challenges and opportunities of anticipatory learning for IoMT systems in contrast to GIS. The holistic overview provided in this paper highlights the guidelines and directions for future research on this emerging topic.

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“…As the healthcare data collected by the wearable devices at the bottom layer can be increased in size, there is a need to carry out data mining and analytics on such big data. Fog node at the middle layer can process the raw data collected from the bottom layer and carries out analytics [28].…”

Section: Iot Healthcare Solution and Proposed Frameworkmentioning

confidence: 99%

Towards Energy-Efficient Framework for IoT Big Data Healthcare Solutions

Feng

Adnan

Ahmad

et al. 2020

Scientific Programming

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The aim of the Internet of things (IoT) is to bring every object (wearable sensors, healthcare sensors, cameras, home appliances, smart phones, etc.) online. These different objects generate huge data which consequently lead to the need of requirements of efficient storage and processing. Cloud computing is an emerging technology to overcome this problem. However, there are some applications (healthcare) which need to process data in real time to improve its performance and require low latency and delay. Fog computing is one of the promising solutions which facilitate healthcare domain in terms of reducing the delay multihop data communication, distributing resource demands, and promoting service flexibility. In this study, a fog-based IoT healthcare framework is proposed in order to minimize the energy consumption of the fog nodes. Experimental results reveal that the performance of the proposed framework is efficient in terms of network delay and energy usage. Furthermore, the authors discussed and suggested important services of big data infrastructure which need to be present in fog devices for the analytics of healthcare big data.

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An Edge-Fog-Cloud Architecture of Streaming Analytics for Internet of Things Applications

Cited by 30 publications

References 15 publications

SFDWA: Secure and Fault-Tolerant Aware Delay Optimal Workload Assignment Schemes in Edge Computing for Internet of Drone Things Applications

SFDWA: Secure and Fault-Tolerant Aware Delay Optimal Workload Assignment Schemes in Edge Computing for Internet of Drone Things Applications

A Holistic Overview of Anticipatory Learning for the Internet of Moving Things: Research Challenges and Opportunities

Towards Energy-Efficient Framework for IoT Big Data Healthcare Solutions

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