Fog Computing Realization for Big Data Analytics

Mehdipour, Farhad; Javadi, Bahman; Mahanti, Anirban; Ramirez-Prado, Guillermo

doi:10.1002/9781119525080.ch11

Cited by 26 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, our preprocessing method opens the door to more comprehensive analyses of dietary patterns and their health implications. By efficiently handling diverse and complex dietary data [ 36 , 40 , 41 ], we can explore deeper relationships between food intake and health outcomes across different populations. This capability allows for a broader understanding of how specific dietary components contribute to or mitigate the risk of health conditions such as obesity and dyslipidemia, providing a solid foundation for developing more effective public health guidelines and nutritional therapies.…”

Section: Discussionmentioning

confidence: 99%

An Explainable Prediction for Dietary-Related Diseases via Language Models

Choi,

Kim,

Kim

2024

Nutrients

View full text Add to dashboard Cite

Our study harnesses the power of natural language processing (NLP) to explore the relationship between dietary patterns and metabolic health outcomes among Korean adults using data from the Seventh Korea National Health and Nutrition Examination Survey (KNHANES VII). Using Latent Dirichlet Allocation (LDA) analysis, we identified three distinct dietary patterns: “Traditional and Staple”, “Communal and Festive”, and “Westernized and Convenience-Oriented”. These patterns reflect the diversity of dietary preferences in Korea and reveal the cultural and social dimensions influencing eating habits and their potential implications for public health, particularly concerning obesity and metabolic disorders. Integrating NLP-based indices, including sentiment scores and the identified dietary patterns, into our predictive models significantly enhanced the accuracy of obesity and dyslipidemia predictions. This improvement was consistent across various machine learning techniques—XGBoost, LightGBM, and CatBoost—demonstrating the efficacy of NLP methodologies in refining disease prediction models. Our findings underscore the critical role of dietary patterns as indicators of metabolic diseases. The successful application of NLP techniques offers a novel approach to public health and nutritional epidemiology, providing a deeper understanding of the diet–disease nexus. This study contributes to the evolving field of personalized nutrition and emphasizes the potential of leveraging advanced computational tools to inform targeted nutritional interventions and public health strategies aimed at mitigating the prevalence of metabolic disorders in the Korean population.

show abstract

Section: Discussionmentioning

confidence: 99%

An Explainable Prediction for Dietary-Related Diseases via Language Models

Choi,

Kim,

Kim

2024

Nutrients

View full text Add to dashboard Cite

show abstract

“…Basically, they accomplish a less demanding task and reduce the communication between the cloud and the end user. Fog allows for performing resource-constraints and short-term analytics close to the edge of the network, whereas the cloud accomplishes resource-intensive and longer-term analytics [195].…”

Section: Blockchain Between Fog and Edge Environmentsmentioning

confidence: 99%

Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control

et al. 2021

Self Cite

View full text Add to dashboard Cite

Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed.

show abstract

“…In IoT based edge computing environment, the volume and variety of data can cause consistency and processing issues, and data silos can result from the use of different platforms and data stored in a big data architecture. Big data analytics describes the process of performing sophisticated analytical tasks on data that typically includes grouping, aggregation, or iterative processes [2]. The raw data is generally unformalized that neither has a predefined data model nor is organized in a pre-defined manner.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Big data refers to the large amounts of un-formalized, semi-formalized or formalized data that flows continuously through and around organizations [1]. Big data analysis provides the advantage of reducing costs when storing huge data while performing various analyzes in various and efficient ways [2]. The emerging 5G networks intend to accomplish new opportunities by introducing very high carrier frequencies.…”

Section: Introductionmentioning

confidence: 99%

A Fog Computing Architecture with Multi-Layer for Computing-Intensive IoT Applications

Muneeb

Park

2021

Applied Sciences

View full text Add to dashboard Cite

The emergence of new technologies and the era of IoT which will be based on compute-intensive applications. These applications will increase the traffic volume of today’s network infrastructure and will impact more on emerging Fifth Generation (5G) system. Research is going in many details, such as how to provide automation in managing and configuring data analysis tasks over cloud and edges, and to achieve minimum latency and bandwidth consumption with optimizing task allocation. The major challenge for researchers is to push the artificial intelligence to the edge to fully discover the potential of the fog computing paradigm. There are existing intelligence-based fog computing frameworks for IoT based applications, but research on Edge-Artificial Intelligence (Edge-AI) is still in its initial stage. Therefore, we chose to focus on data analytics and offloading in our proposed architecture. To address these problems, we have proposed a prototype of our architecture, which is a multi-layered architecture for data analysis between cloud and fog computing layers to perform latency- sensitive analysis with low latency. The main goal of this research is to use this multi-layer fog computing platform for enhancement of data analysis system based on IoT devices in real-time. Our research based on the policy of the OpenFog Consortium which will offer the good outcomes, but also surveillance and data analysis functionalities. We presented through case studies that our proposed prototype architecture outperformed the cloud-only environment in delay-time, network usage, and energy consumption.

show abstract

Fog Computing Realization for Big Data Analytics

Cited by 26 publications

References 18 publications

An Explainable Prediction for Dietary-Related Diseases via Language Models

An Explainable Prediction for Dietary-Related Diseases via Language Models

Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control

A Fog Computing Architecture with Multi-Layer for Computing-Intensive IoT Applications

Contact Info

Product

Resources

About