A Comprehensive Analysis of Computing Paradigms Leading to Fog Computing: Simulation Tools, Applications, and Use Cases

Patel, Sandipkumar; Patel, Ritesh

doi:10.1080/08874417.2022.2121782

Cited by 3 publications

(5 citation statements)

References 112 publications

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“…A fog node is placed between the device and application layer to resolve the latency issues. A fog node can be any device that includes processing, storage, and network connectivity element [16] . In most healthcare fog solutions, a three-layer architecture with the bottom layer as the device layer, the middle layer as Fog, and the upper layer as the Cloud layer was proposed [17].…”

Section: Related Workmentioning

confidence: 99%

Enhancement of Semantic Interoperability in Healthcare Systems Using IFCIoT Architecture

2024

KSII TIIS

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Fast decision support systems and accurate diagnosis have become significant in the rapidly growing healthcare sector. As the number of disparate medical IoT devices connected to the human body rises, fast and interrelated healthcare data retrieval gets harder and harder. One of the most important requirements for the Healthcare Internet of Things (HIoT) is semantic interoperability. The state-of-the-art HIoT systems have problems with bandwidth and latency. An extension of cloud computing called fog computing not only solves the latency problem but also provides other benefits including resource mobility and on-demand scalability. The recommended approach helps to lower latency and network bandwidth consumption in a system that provides semantic interoperability in healthcare organizations. To evaluate the system's language processing performance, we simulated it in three different contexts. 1. Polysemy resolution system 2. System for hyponymy -hypernymy resolution with polysemy 3. System for resolving polysemy, hypernymy, hyponymy, meronymy, and holonymy. In comparison to the other two systems, the third system has lower latency and network usage. The proposed framework can reduce the computation overhead of heterogeneous healthcare data. The simulation results show that fog computing can reduce delay, network usage, and energy consumption.

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Section: Related Workmentioning

confidence: 99%

Enhancement of Semantic Interoperability in Healthcare Systems Using IFCIoT Architecture

2024

KSII TIIS

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“…LV technologies have revolutionized software development by offering flexibility in managing and distributing software, making them ideal for edge computing environments where virtualization is required close to the data source and end users. Patel and Patel (2023) conduct a comprehensive analysis of computing paradigms leading to fog computing, which is closely related to edge computing. Their study delves into the evolution of these paradigms, highlighting the role of fog, edge, mist, and dew computing in addressing challenges like bandwidth limitation, high response time, and resource demands of latencysensitive applications.…”

Section: In-depth Analysis Of Edge Computing Technologiesmentioning

confidence: 99%

“…These technologies are compared based on their ability to enable scalability, security, and manageability required by pervasive applications that are increasingly becoming part of everyday life. The research by Patel and Patel (2023) also covers the simulation tools used in fog computing, which are essential for understanding and concluding the results of various scenarios and algorithms. These tools play a crucial role in analyzing and optimizing the performance of fog and edge computing systems.…”

Section: In-depth Analysis Of Edge Computing Technologiesmentioning

confidence: 99%

“…These include challenges related to the integration of edge computing with existing IoT infrastructures and the need for efficient resource management at the edge of the network. Patel and Patel (2023) discuss the major technology areas of fog computing, including case studies on various applications. These case studies provide valuable insights into how fog and edge computing technologies can be effectively applied in different scenarios, from smart cities to industrial applications.…”

Section: In-depth Analysis Of Edge Computing Technologiesmentioning

confidence: 99%

“…These case studies provide valuable insights into how fog and edge computing technologies can be effectively applied in different scenarios, from smart cities to industrial applications. The in-depth analysis of edge computing technologies by Morabito et al (2018), Patel and Patel (2023), and Torres-Carrión et al ( 2022) reveals a dynamic and rapidly evolving field. Edge computing, with its focus on processing data closer to its source, offers significant benefits in terms of reduced latency and improved efficiency.…”

Section: In-depth Analysis Of Edge Computing Technologiesmentioning

confidence: 99%

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Innovation in Data Storage Technologies: From Cloud Computing to Edge Computing

Odunayo Josephine Akindote,

Abimbola Oluwatoyin Adegbite,

Samuel Onimisi Dawodu

et al. 2023

Comput. sci. IT res. j.

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In an era where data is the new gold, understanding the evolution and future trajectory of data storage technologies is crucial. This paper delves into the transformative journey from traditional storage methods to contemporary paradigms like cloud and edge computing, underpinned by the burgeoning influence of Big Data, IoT, AI, and machine learning. The study's aim is to provide a comprehensive analysis of these technologies, assessing their development, efficacy, and the challenges they face in meeting the escalating demands of data storage. The methodology employed is a meticulous synthesis of literature reviews, case studies, and comparative analyses. This approach facilitates an in-depth exploration of the historical evolution of data storage, the paradigm shifts from cloud to edge computing, and the interplay between technological advancements and user demands. The study also scrutinizes the security concerns inherent in these technologies and identifies strategic directions for future research. Key findings reveal that while cloud computing has revolutionized data storage with its scalability and flexibility, edge computing emerges as a vital solution to latency and bandwidth limitations. The integration of AI and machine learning is identified as a pivotal factor in enhancing the efficiency and intelligence of data storage systems. However, this integration presents unique challenges, necessitating innovative solutions. Conclusively, the study recommends a continued focus on innovation in data storage technologies, emphasizing the development of integrated, secure, and efficient solutions. Future research should particularly explore the potential of AI and machine learning in overcoming current limitations. The paper's scope encompasses a comprehensive overview of the current state and future potential of data storage technologies, making it a valuable resource for researchers, technologists, and policymakers in the field. Keywords: Data Storage Technologies, Cloud Computing, Edge Computing, Big Data, Internet of Things (IoT), Artificial Intelligence (AI).

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Mathematical analysis of the pulse coincidence process for applications on frequency sensors after the use of variable references

Murrieta-Rico,

Sergiyenko,

Rodríguez-Quiñonez

et al. 2024

Rev. Ci. Tec.

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In most cases, sensors are the means that enable a computer to get information from a process of interest. This requires that the information generated by the sensor can be processed by the computer in a timely manner. However, if accurate data from the sensor is required, an appropriate transduction process is required. There are sensors that generate a frequency-domain output. Since these sensors typically have a short response time, it is required to get the best approximation to their frequency within the shortest time possible. There are different methods for obtaining the frequency value generated by the sensor. Although such methods can be applied, their functioning characteristics are not suitable for application in sensors. The principle of rational approximations is a method that has proven plenty of improvements in comparison to other frequency measurement methods. In this work, the functioning of the principle of rational approximations is explored when different time references are used. After the computational analysis of the principle of rational approximations, it was found out how the reference frequency value affects the measurement process. It was found that if the magnitude of reference and unknown frequencies have an increment in their difference, then the relative error decreases.

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A Comprehensive Analysis of Computing Paradigms Leading to Fog Computing: Simulation Tools, Applications, and Use Cases

Cited by 3 publications

References 112 publications

Enhancement of Semantic Interoperability in Healthcare Systems Using IFCIoT Architecture

Enhancement of Semantic Interoperability in Healthcare Systems Using IFCIoT Architecture

Innovation in Data Storage Technologies: From Cloud Computing to Edge Computing

Mathematical analysis of the pulse coincidence process for applications on frequency sensors after the use of variable references

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