Fog computing: Data analytics and cloud distributed processing on the network edges

Gonzalez, Nelson Mimura; Goya, Walter Akio; Pereira, Rosangela de Fátima; Langona, Karen; Silva, Erico Augusto; Carvalho, Tereza Cristina de; Miers, Charles Christian; Mångs, Jan-Erik; Sefidcon, Azimeh

doi:10.1109/sccc.2016.7836028

Cited by 43 publications

(15 citation statements)

References 31 publications

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“…Fog is a platform that provides compute, storage and networking services between end devices and cloud computing servers, most commonly, but not exclusively, located at the edge of network [60]. For IoT, this new platform is aimed to decentralize the data processing [288], decrease the latency [289], and bring more reliability for WSN [290]. Finally, Software-Defined Networking (SDN) is a novel concept for IoT, without any growth in the 2011-2012 period, but which was gaining popularity from one publication in 2013, and 2014, to 33 in 2016.…”

Section: Top Trending Topicsmentioning

confidence: 99%

Internet of Things: A Scientometric Review

et al. 2017

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Abstract:Internet of Things (IoT) is connecting billions of devices to the Internet. These IoT devices chain sensing, computation, and communication techniques, which facilitates remote data collection and analysis. wireless sensor networks (WSN) connect sensing devices together on a local network, thereby eliminating wires, which generate a large number of samples, creating a big data challenge. This IoT paradigm has gained traction in recent years, yielding extensive research from an increasing variety of perspectives, including scientific reviews. These reviews cover surveys related to IoT vision, enabling technologies, applications, key features, co-word and cluster analysis, and future directions. Nevertheless, we lack an IoT scientometrics review that uses scientific databases to perform a quantitative analysis. This paper develops a scientometric review about IoT over a data set of 19,035 documents published over a period of 15 years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) in two main scientific databases (Clarivate Web of Science and Scopus). A Python script called ScientoPy was developed to perform quantitative analysis of this data set. This provides insight into research trends by investigating a lead author's country affiliation, most published authors, top research applications, communication protocols, software processing, hardware, operating systems, and trending topics. Furthermore, we evaluate the top trending IoT topics and the popular hardware and software platforms that are used to research these trends.

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Section: Top Trending Topicsmentioning

confidence: 99%

Internet of Things: A Scientometric Review

et al. 2017

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“…Independently of the terminology chosen, which might follow the current naming trend, a common concept here is an intermediate level between the device and the traditional cloud. It is possible to find in the literature numerous surveys about those paradigms in general [6], [10], [21], [22], [23], [24], [25], specific aspects of them such as security [8], [26] or specific techniques such as Software-Defined Networking (SDN) [27]. However, those typically do not consider the resource aspect.…”

Section: Introductionmentioning

confidence: 99%

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Toczé

Nadjm‐Tehrani

2018

Wireless Communications and Mobile Computing

103

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Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices at the edge of the current network. To achieve higher performance in this new paradigm, one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis are used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource and less extensive towards the estimation, discovery, and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of nonfunctional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.

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“…The phrase ‘Fog Computing’ has been propounded by Cisco researchers in 2012 [ 42 , 102 ]. Fog computing and cloudlets are related concepts operating at the edge level, with cloudlets deployed at mobile networks and fog computing dealing with connected things [ 103 ].…”

Section: Evolving Computing Paradigms and Related Conceptsmentioning

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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Fog computing: Data analytics and cloud distributed processing on the network edges

Cited by 43 publications

References 31 publications

Internet of Things: A Scientometric Review

Internet of Things: A Scientometric Review

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

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

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