Fog Computing Framework for Smart City Design

Qasem, Mais Haj; Abu-Srhan, Alaa; Natoureah, Hutaf; Alzaghoul, Esra

doi:10.3991/ijim.v14i01.9762

Cited by 12 publications

(11 citation statements)

References 18 publications

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“…Fog Computing (FC) network architecture FC complements the operations of cloud computing to reinforce the Quality of Service (QoS) and Quality of Experience (QoE) of the End-Users (EU) [34]. The FC architecture is versatile; the number of layers between the EU and the cloud provider can vary between a single layer or any hierarchical layers of fog nodes [35], [36] . Figure 1 illustrates the layers of the FC paradigm.…”

Section: 1mentioning

confidence: 99%

Fog Computing Based on Machine Learning: A Review

Samann

Abdulazeez

Askar

2021

Int. J. Interact. Mob. Technol.

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<p>Internet of Things (IoT) systems usually produce massive amounts of data, while the number of devices connected to the internet might reach billions by now. Sending all this data over the internet will overhead the cloud and consume bandwidth. Fog computing's (FC) promising technology can solve the issue of computing and networking bottlenecks in large-scale IoT applications. This technology complements the cloud computing by providing processing power and storage to the edge of the network. However, it still suffers from performance and security issues. Thus, machine learning (ML) attracts attention for enabling FC to settle its issues. Lately, there has been a growing trend in utilizing ML to improve FC applications, like resource management, security, lessen latency and power usage. Also, intelligent FC was studied to address issues in industry 4.0, bioinformatics, blockchain and vehicular communication system. Due to the ML vital role in the FC paradigm, this work will shed light on recent studies utilized ML in a FC environment. Background knowledge about ML and FC also presented. This paper categorized the surveyed studies into three groups according to the aim of ML implementation. These studies were thoroughly reviewed and compared using sum-up tables. The results showed that not all studies used the same performance metric except those worked on security issues. In conclusion, the simulations of proposed ML models are not sufficient due to the heterogeneous nature of the FC paradigm.</p>

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Section: 1mentioning

confidence: 99%

Fog Computing Based on Machine Learning: A Review

Samann

Abdulazeez

Askar

2021

Int. J. Interact. Mob. Technol.

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“…Thus, the data are processed locally and sent to a central server. Using fog computing, application components run in cloud and fog [19].…”

Section: Participant Typementioning

confidence: 99%

Assessment of the Impact of Cloud Technologies on Social Life in the Era of Digitalization

Panfilova

Lukyanova

Pronkin

et al. 2021

Int. J. Interact. Mob. Technol.

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<span lang="EN-US">Due to the expansion of new digital trends in the strategic development of territorial units, it became necessary to structure the socio-economic effects from the introduction of innovative technologies grounded on cloud computing. Realized the identification of cloud technologies impact on the social life in the era of digitalization, an online conference was held between representatives of government, business, science, and education of the Republics of Karelia, Komi, Adygea, Dagestan, Ingushetia, Kalmykia, Karachay-Cherkessia, Krasnodar Krai and Stavropol Krai (Russian Federation). In the context of innovative possibilities of platform economy, in discussion were identified the key effects of the cloud solutions implementation in the Russian Federation regions development prospect strategy. Namely, development of social and human capital, improvement of the quality and standard of living in a region, improvement of social interaction, development of social constructivism, economic potential, and competitiveness of social capital. It has been reported that the positive social effects of cloud technologies at the level of a territorial unit are achieved by increasing digital literacy of the population and opening up new opportunities and platforms for social interaction and constructivism; providing the population with convenient services and open data for participation in active social, economic and political life. Cloud technologies help to form a digital educational environment, modernize educational programs for the preparation of high-quality labor resources, develop digital infrastructure of individual settlements, create user content to preserve socio-cultural identity, and popularize a region in the global information space. Revealed that the possibilities of cloud technologies increase the population quality of living and ensure the growth of the efficiency and productivity indicators of labor resources. The listed above are achieved by expanding the marketing capabilities of business structures, providing them with new business models and digital services; optimization of resource management, digitization of business processes, and platform organization of labor. The capabilities of cloud solutions allow for the formation of flexible and effective interregional and international cooperation in a region, having a positive impact on regional economy and social capital development.</span>

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“…The location of the user in the building is considered for energy management. Qasem et al [17] given a case study of a smart city application. The proposed approach implemented smart parking, traveling, and direction in smart city applications.…”

Section: Related Workmentioning

confidence: 99%

IoT Data Classifications for Smart Home Deployment

Chandak

Ray

2021

SN COMPUT. SCI.

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Gadgets installed in smart home ease human life and can be controlled from remote locations. These gadgets are sensor-based and generate different types of data. These data need to be processed faster to provide a quick response in a smart home application. Edge nodes dwell at the edges of IoT gadgets for faster processing of data. In this paper, we have used a data classifier strategy to overcome a few existing issues in smart home applications. Certain rules are derived based on which the proposed classifier classify the data. Then, data are forwarded to edge nodes for processing. The performance of a proposed data classifier is studied using different parameters. From the simulation study, it is found that the proposed strategy gives better results in terms of average execution time, service latency and resource utilization.

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Fog Computing Framework for Smart City Design

Cited by 12 publications

References 18 publications

Fog Computing Based on Machine Learning: A Review

Fog Computing Based on Machine Learning: A Review

Assessment of the Impact of Cloud Technologies on Social Life in the Era of Digitalization

IoT Data Classifications for Smart Home Deployment

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