Data Preservation through Fog-to-Cloud (F2C) Data Management in Smart Cities

Sinaeepourfard, Amir; García, Jordi; Masip-Bruin, X.; Marín‐Tordera, Eva

doi:10.1109/cfec.2018.8358732

Cited by 26 publications

(47 citation statements)

References 8 publications

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“…The combination between both computing paradigms (cloud and fog) can be thus highly beneficial. The advantages that the fog-cloud architecture can provide to smart city [27,28], e-health [29], and IoT [13] scenarios have already been evinced in the available literature.…”

Section: Related Workmentioning

confidence: 99%

Fog to cloud and network coded based architecture: Minimizing data download time for smart mobility

Peralta

Garrido

Bilbao

et al. 2020

Simulation Modelling Practice and Theory

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Industry 4.0 applications foster new business opportunities, but they also pose new and challenging requirements, such as low latency communications and highly reliable systems. They would likely exploit novel wireless technologies (5G), but it would also become crucial using architectures that appropriately support them. In this sense, the combination of fog and cloud computing represents a potential solution, since it can dynamically allocate the workload depending on the specific needs of each application. In this paper, our main goal is to provide a highly reliable and dynamic architecture, which minimizes the time that an end node or user, spends in downloading the required data. In order to achieve this, we have developed an optimal distribution algorithm that decides the amount of information that should be stored at, or retrieved from, each node, to minimize the overall data download time. Our scheme is based on various parameters and it exploits Network Coding (NC) as a tool for data distribution, as a key enabler of the proposed solution. We compare the performance of the proposed scheme with other alternative solutions, and the results show that there is a clear gain in terms of the download time.

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Section: Related Workmentioning

confidence: 99%

Fog to cloud and network coded based architecture: Minimizing data download time for smart mobility

Peralta

Garrido

Bilbao

et al. 2020

Simulation Modelling Practice and Theory

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show abstract

“…In recent years, edge computing has been attracting significant attention from both research and industry due to its promise to reduce latencies and network traffic, improve user experience, and reduce reliance on cloud [3], [5], [12], [13]. Edge computing shares many attributes with fog computing which refers to processing data close to data sources.…”

Section: Related Workmentioning

confidence: 99%

“…The second group of related works presents experiments that demonstrate edge computing capabilities. Sinaeepourfard et al [13] proposed fog to cloud (F2C) data management architecture incorporating the data preservation block with the objective of providing faster data access than the cloud. To illustrate possible benefits of the proposed architecture, they calculated the potential reduction in the data transfer volume and latency decrease taking the city of Barcelona as an example.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning: Edge-Cloud Data Analytics for IoT

Ghosh

Grolinger

2019

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)

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Sensors, wearables, mobile and other Internet of Thing (IoT) devices are becoming increasingly integrated in all aspects of our lives. They are capable of collecting massive quantities of data that are typically transmitted to the cloud for processing. However, this results in increased network traffic and latencies. Edge computing has a potential to remedy these challenges by moving computation physically closer to the network edge where data are generated. However, edge computing does not have sufficient resources for complex data analytics tasks. Consequently, this paper investigates merging cloud and edge computing for IoT data analytics and presents a deep learning-based approach for data reduction on the edge with the machine learning on the cloud. The encoder part of the autoencoder is located on the edge to reduce data dimensions. Reduced data are sent to the cloud where there are used directly for machine learning or expanded to original features using the decoder part of the autoencoder. The proposed approach has been evaluated on the human activity recognition tasks. Results show that 50% data reduction did not have a significant impact on the classification accuracy and 77% reduction only caused 1% change.

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“…As a supplement to cloud computing, fog computing extends cloud computing to the edge of the network, making up for the shortcomings of cloud computing. On the basis of not changing the original network architecture, it provides users with computing, storage, and other services on the edge of the network, reduces the processing delay of user requests, and meets the low delay requirements [1][2][3][4]. Fog computing makes up for the lack of cloud computing and has been widely concerned by experts and scholars.…”

Section: Introductionmentioning

confidence: 99%

Load balancing strategy for medical big data based on low delay cloud network

Yong¹

2020

J. eng.

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Data Preservation through Fog-to-Cloud (F2C) Data Management in Smart Cities

Cited by 26 publications

References 8 publications

Fog to cloud and network coded based architecture: Minimizing data download time for smart mobility

Fog to cloud and network coded based architecture: Minimizing data download time for smart mobility

Deep Learning: Edge-Cloud Data Analytics for IoT

Load balancing strategy for medical big data based on low delay cloud network

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