Fog Computing for Smart Cities’ Big Data Management and Analytics: A Review

Badidi, Elarbi; Mahrez, Zineb; Sabir, Essaïd

doi:10.3390/fi12110190

Cited by 53 publications

(49 citation statements)

References 88 publications

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“…Thus, big data analytics is a promising solution that processes the humongous big data and transforms it into smart data, imparting actionable insights into making data-driven decisions [ 137 ]. The key feature of fog computing and edge computing models is the potential to quickly store and process data, benefiting real-time applications and playing a crucial part in efficient business operations [ 138 , 139 ].…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Angel

Ravindran

Vincent

et al. 2021

Sensors

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Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical constraints of novel IoT applications generating vast data, which entails a swift response time with improved privacy. The newest drift is moving computational and storage resources to the edge of the network, involving a decentralized distributed architecture. The data processing and analytics perform at proximity to end-users, and overcome the bottleneck of cloud computing. The trend of deploying machine learning (ML) at the network edge to enhance computing applications and services has gained momentum lately, specifically to reduce latency and energy consumed while optimizing the security and management of resources. There is a need for rigorous research efforts oriented towards developing and implementing machine learning algorithms that deliver the best results in terms of speed, accuracy, storage, and security, with low power consumption. This extensive survey presented on the prominent computing paradigms in practice highlights the latest innovations resulting from the fusion between ML and the evolving computing paradigms and discusses the underlying open research challenges and future prospects.

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Section: Challenges and Opportunitiesmentioning

confidence: 99%

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Angel

Ravindran

Vincent

et al. 2021

Sensors

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show abstract

“…This is done by creating multiple attributes and range queries in order to use their storage and computing capabilities in ITS. Furthermore, Badidi et al [60] reviewed current service delivery models in edge and fog computing in smart cities, and proposed a fog-based data pipeline for IoT data management messaging systems. Zhang et al [61] considered developing and deploying data-driven ITS that has the capability of vision, multi-sources, and learning to optimize its performance.…”

Section: Cloud Fog and Edge Computing In Itsmentioning

confidence: 99%

Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

et al. 2021

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With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens’ mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing.These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.

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“…Saugumas ir pažeidžiamumas (Aceto et al, 2018(Aceto et al, , 2020Anawar et al, 2018;Badidi et al, 2020;Basir et al, 2019;da Silva ir de Aquino, 2018;Dang et al, 2019;de Moura Costa et al, 2020;Dong ir Yao, 2021;EscamillaAmbrosio et al, 2018;Gaber et al, 2019;Haouari et al, 2018;Hu et al, 2017;Ibrahim et al, 2018;Javadzadeh ir Rahmani, 2020;Kraemer et al, 2017;Naresh et al, 2020;Nazir et al, 2019;Saheb ir Izadi, 2019;Swamy ir Kota, 2020;Tayeb et al, 2017;Vilela et al, 2020) (Aceto et al, 2018;Anawar et al, 2018;Badidi et al, 2020;Basir et al, 2019;Behmanesh et al, 2020;da Silva ir de Aquino, 2018;Dang et al, 2019;de Moura Costa et al, 2020;de Prado et al, 2020;Dong ir Yao, 2021;EscamillaAmbrosio et al, 2018;Fu et al, 2020;Hartmann et al, 2019;Hu et al, 2017;Younan et al, 2020;Kaur et al, 2020a;Kraemer et al, 2017;McCann et al, 2018;Swamy ir Kota...…”

Section: Duomenų Apdorojimo Uždaviniaiunclassified

“…Naudotojų ar transporto priemonių mobilumo scena rijų įgyvendinimo problemų kyla, kai vykdomos progra mos būsena ar visa programa turi būti perkelta tarp skir tingų rūko kompiuterijos įrenginių (Badidi et al, 2020).…”

Section: Rūko Kompiuterijos E Sveikatos Srityje Problemos Ir Ateities Kryptysunclassified

Fog Computing Technologies for Patient Sensor Networks – Trends, Issues and Future Directions

Kazlauskas

2021

Science - Future of Lithuania

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Advances in sensors and internet of things promise broad opportunities in many areas and one of them is health care. There are many solutions to manage health care data based on cloud computing. However, high response latency, large volumes of data transferred and security are the main issues of such approach. Fog computing provides immediate response and ways to process large amounts of data using real time analytics which includes machine learning and AI. Fog computing has not yet fully matured and there are still many challenges when managing health care data. It was chosen to investigate the most relevant ehealth fog computing topics by analyzing review articles to explain the fog computing model and present the current trends – fog computing ehealth technology application environments, deployment cases, infrastructure technologies, data processing challenges, problems and future directions. 38 scientific review articles published in the last 5 years were selected for analysis, filtering the most significant works with Web of Science article search tool.

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Fog Computing for Smart Cities’ Big Data Management and Analytics: A Review

Cited by 53 publications

References 88 publications

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

Fog Computing Technologies for Patient Sensor Networks – Trends, Issues and Future Directions

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