A Low-Latency and Massive-Connectivity Vehicular Fog Computing Framework for 5G

Integration of Fog and IoT with Multiensor Data Gathering

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d9042.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 11785-11787

Keyword(s):

Cloud Computing ◽

Real Time ◽

Fog Computing ◽

Data Gathering ◽

Low Latency ◽

Computing Paradigm ◽

City System ◽

Computing Framework ◽

The City

In the already existing system number of internet connected devices rapidly increase, this increased demand real-time, for the standard cloud computing framework, low latency services proving to be always a challenge. While In the proposed System, fog computing paradigm serves the demands of the latency sensitive applications in the context of IOT. The IOT is rely on cloud computing by passing information about sensor. This is a decentralized process to gather the information from each and every region of the city. System will check the energy and location of every server. Because whenever server uploads the sensor details it can degrade their energy on every time. So we have to migrate the data by allocating another server which contains the energy to send

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Software Architecture of a Fog Computing Node for Industrial Internet of Things

Sensors ◽

10.3390/s21113715 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3715

Author(s):

Ioan Ungurean ◽

Nicoleta Cristina Gaitan

Keyword(s):

Internet Of Things ◽

Real Time ◽

Development Process ◽

Fog Computing ◽

Low Latency ◽

Practical Implementation ◽

Industrial Internet Of Things ◽

Industrial Internet ◽

Starting Point ◽

Hard Real Time

In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC.

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A scheduling-based dynamic fog computing framework for augmenting resource utilization

Simulation Modelling Practice and Theory ◽

10.1016/j.simpat.2021.102336 ◽

2021 ◽

Vol 111 ◽

pp. 102336

Author(s):

Md Razon Hossain ◽

Md Whaiduzzaman ◽

Alistair Barros ◽

Shelia Rahman Tuly ◽

Md. Julkar Nayeen Mahi ◽

...

Keyword(s):

Resource Utilization ◽

Fog Computing ◽

Computing Framework

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A Fog Computing Framework for Quality of Service Optimisation in the Internet of Things (IoT) Ecosystem

2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC) ◽

10.1109/imitec50163.2020.9334083 ◽

2020 ◽

Author(s):

William Tichaona Vambe ◽

Khulumani Sibanda

Keyword(s):

Quality Of Service ◽

Internet Of Things ◽

Fog Computing ◽

The Internet ◽

Computing Framework ◽

The Internet Of Things

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INTELLIGENT DISTRIBUTED DYNAMIC FOG COMPUTING FRAMEWORK

Электросвязь ◽

10.34832/elsv.2021.20.7.004 ◽

2021 ◽

Author(s):

А.Н. ВОЛКОВ

Keyword(s):

Fog Computing ◽

Computing System ◽

Limited Resources ◽

Swarm Optimization ◽

Communications Networks ◽

Software Migration ◽

Distributed Structures ◽

Computing Framework ◽

Distributed Computing Infrastructure ◽

New Framework

Одним из направлений развития сетей связи 5G и сетей связи 2030 является интегрирование в сеть распределенных вычислительных структур, таких как системы пограничных и туманных вычислений (Fog), которые призваны выполнить децентрализацию вычислительной части сетей. В связи с этим необходимо исследовать и определить принципы предоставления услуг на основе распределенной вычислительной инфраструктуры, в том числе в условиях ограниченности ресурсов отдельно взятых составных частей (Fog-устройства). Предлагается новый фреймворк распределенной динамической вычислительной системы туманных вычислений на основе микросервисного архитектурного подхода к реализации, развертыванию и миграции программного обеспечения предоставляемых услуг. Исследуется типовая архитектура микросервисного подхода и ее имплементация в туманные вычисления, а также рассматриваются два алгоритма: алгоритм K-средних для нахождения центра пользовательской нагрузки и алгоритм роевой оптимизации для определения устройства тумана с необходимыми характеристиками для последующей миграции микросервиса. One of the directions of 5G and 2030 communications networks development is the network-integrated distributed structures, such as edge computing (MEC) and Fog computing, which are designed to decentralize the computing part of networks. In this regard, it is necessary to investigate and determine the principles of providing services based on a distributed computing infrastructure, including in conditions of limited resources of individual components (Fog devices). This article proposes a new framework for a distributed dynamic computing system of fog computing based on a microservice architectural approach to the implementation, deployment, and software migration of the services. The article examines the typical architecture of the microservice approach and its implementation in fog computing, and also investigates two algorithms: K-means for finding the center of user load, swarm optimization (PSO) to determine the fog device with the necessary characteristics for the subsequent migration of the microservice.

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