Toward integrated Cloud–Fog networks for efficient IoT provisioning: Key challenges and solutions

Peng, Limei; Dhaini, Ahmad R.; Ho, Pin–Han

doi:10.1016/j.future.2018.05.015

Cited by 53 publications

(25 citation statements)

References 10 publications

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“…Network overhead affects the links between the things and fog layers and between the fog and cloud layers. Handling network overhead can increase the deployment costs to guarantee adequate resources and is related to network dimensioning and configuration [33]. Thus, the optimal number of fog nodes according to the network demand should be determined.…”

Section: Network Overheadmentioning

confidence: 99%

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Toward Reliable Fog Computing Architecture for Industrial Internet of Things

Ramli¹,

Bhardwaj²,

Kim³

2019

Preprint

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Reliability is essential in industrial networks. In addition, most of the data from nodes of industrial Internet of Things (IIoT) are generated in real time. Thus, those data are mainly used for the time-sensitive applications. Furthermore, device failures should be considered when modeling reliable fog computing for IIoT. In this paper, we provide fundamental aspects to model reliable fog computing for IIoT. First, existing models of fog computing are compared. Then, the most feasible communication type to achieve a reliable system is determined from model analysis. Interaction modes are elaborated to study the advantages and drawbacks when communication is deployed in fog computing for IIoT, and challenges and solutions for reliable fog computing are discussed.

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Section: Network Overheadmentioning

confidence: 99%

“…Figure 6. Tradeoff between latency, network efficiency, and network size [33] The authors in [34] reviewed the scenarios and security issues of fog computing. In [35], the authors showed a subset of possible attack scenarios from real life applications.…”

Section: Security and Data Privacymentioning

confidence: 99%

Toward Reliable Fog Computing Architecture for Industrial Internet of Things

Ramli¹,

Bhardwaj²,

Kim³

2019

Preprint

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“…As a means of addressing this issue, fog computing became an extension to cloud computing [16,17]. Fog computing is a process allocation technique that offloads the different sections of the program from the server to the end devices.…”

Section: Introductionmentioning

confidence: 99%

A scalable IoT-fog framework for urban sound sensing

Baucas

Spachos

2020

Computer Communications

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Internet of Things (IoT) is a system of interrelated devices that can be used to allow large-scale collection and analysis of data. However, as it grew, IoT networks were not capable of managing the data from these services. As a result, cloud computing was introduced to address the need for datacentres for IoT networks. As the technology evolved, the demand for a proper means of supporting and managing crowdsensing and real-time data increased, and cloud servers could no longer keep up with the large volumes of incoming data. This demand brought rise to fog computing. It became an extension to the cloud and allowed resources to be allocated around the network effectively. Its integration to IoT reduced the strain towards the cloud servers. However, issues in high power consumption at the end device and data management constraints surfaced. This paper proposes two approaches to alleviate these issues to keep fog computing remain as a reliable option for IoT-related applications. We created an IoT-based sensing framework that used an urban sound classification model. Through active low and high power states and resource reallocation, we created a network configuration. We tested this configuration against IoT frameworks that use the default fog and cloud setups. The results improved the framework's end device power consumption and server latency. Overall, with the proposed framework, fog computing was proven to be capable of supporting a scalable IoT framework for urban sound sensing.

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“…The emergence of technology has changed the way people work and interact thus linked to the growth of mobiles devices such as wearable devices, tablets, smart phones and etc (Peng, Dhaini, & Ho, 2018). However, mobile computing devices built consist lack of communication capabilities, software and hardware.…”

Section: Introductionmentioning

confidence: 99%

Factors Influencing Fog Computing Adoption Based on Quality of Results (QoR) for Heterogeneous Data Analysis: A Proposed Framework

Malic¹,

Izhar²,

Kadir³

2019

IJRTE

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The rapid increase of data generated has brought challenges on data quality level. Fog computing in general has been supporting the requirements of end user devices that could not be met by cloud computing solution and it is acknowledged to have a major impact on how an organisation decides to adopt for preprocessing a huge amount of data being generated by the devices. Since IoT devices generating very heterogeneous and dynamic data, there are challenges for the level of data quality. The limitation has hindered the development of fog systems framework that capable operating the dynamic execution of edge devices that handling generation and collection large amounts of data on-premise and off-premise. Thus,sufficient operations of identifying Quality of Result enable user to detect any problems when conducting the decision making. The aim of this paper is to address the factors that perceived likely to influence the adoption of fog computing in evaluating the data analysis on data transmitted from the ever increases devices.A conceptual framework has been constructed considering attributes such as heterogeneous data analysis (on-premise and off-premise) and Quality of Results (quality indicators, quality control, validity outcome and reliability outcome).Potential benefits from the implementation of this framework to organisation is it enable to provide greater value and benefits to the business process. The framework of this study could also be influencing and inhibiting the adoption of fog computing.Quality of result has higher chances to satisfy the defined industrial's requirement. In addition, fog-computing adoption is important for serving an environment for industry to execute, monitor, and analyze a large form of data in a fog landscape.

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Toward integrated Cloud–Fog networks for efficient IoT provisioning: Key challenges and solutions

Cited by 53 publications

References 10 publications

Toward Reliable Fog Computing Architecture for Industrial Internet of Things

Toward Reliable Fog Computing Architecture for Industrial Internet of Things

A scalable IoT-fog framework for urban sound sensing

Factors Influencing Fog Computing Adoption Based on Quality of Results (QoR) for Heterogeneous Data Analysis: A Proposed Framework

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