Survey of applications, challenges and opportunities in fog computing

Large and complex ring forgings are key structural parts of the aerospace field, and their quality is closely related to the reliability of aerospace vehicles. However, high-quality production of aerospace ring forgings faces many problems, such as the long process design cycle and impoverished consistency, the difficulties of real-time detection under the severe time-varying state of the deformation process, the complexity of high-quality non-destructive testing under multitudinous defects, and the cumbersome management control of the multi-source and multi-dimensional heterogeneous data. Considering the current situation of multi-variety and multi-batch production for aerospace ring forgings, establishing an intelligent production line is a crucial means to solving the above problems and realizing the standardization and premiumization of key aerospace components. Therefore, management control and integration technology of the intelligent production line play a crucial role. An analysis, including the research progress of the intelligent computer-aided process planning (CAPP) system, the real-time detection and control system, the product quality testing system, and the intelligent management control and integration system, is systematically reviewed in this work. Through intelligently managing and controlling the integrated systems of the production line, the production efficiency of ring forgings can be effectively improved, and the production energy consumption can be remarkably reduced, which is of great significance for enhancing the manufacturing technology level of aerospace products.

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“…Edge Computing [98][99][100][101][102][103] Lower latency and power consumption; higher reliability.…”

Section: Technology Opportunities Challengesmentioning

confidence: 99%

Management Control and Integration Technology of Intelligent Production Line for Multi-Variety and Complex Aerospace Ring Forgings: A Review

Huang

Cui

et al. 2022

Metals

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“…In other way, fog computing reduces the burden of traditional cloud computing data centers [20,21] and work on the basis of decentralized computing concept to improve the computational speed. Nowadays, fog computing is performing a significant role in the area of agriculture, healthcare, construction, industry, smart cities, smart home, energy-grid, transportation, environment monitoring and climate change monitoring [22,23,24,25]. Fig.…”

Section: A Contextmentioning

confidence: 99%

Fog computing: A taxonomy, systematic review, current trends and research challenges

Singh

Gill

2021

Journal of Parallel and Distributed Computing

105

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“…Such a situation needs data caching and offloading capabilities for proper distribution to users Shi et al (2016). These capabilities also need to be optimized due to several constraints the experience, such as data priority determination and limited storage and execution time Bala & Chishti (2019).…”

Section: Introductionmentioning

confidence: 99%

GenACO a multi-objective cached data offloading optimization based on genetic algorithm and ant colony optimization

Zulfa

Hartanto

Permanasari

et al. 2021

PeerJ Computer Science

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Background Data exchange and management have been observed to be improving with the rapid growth of 5G technology, edge computing, and the Internet of Things (IoT). Moreover, edge computing is expected to quickly serve extensive and massive data requests despite its limited storage capacity. Such a situation needs data caching and offloading capabilities for proper distribution to users. These capabilities also need to be optimized due to the experience constraints, such as data priority determination, limited storage, and execution time. Methods We proposed a novel framework called Genetic and Ant Colony Optimization (GenACO) to improve the performance of the cached data optimization implemented in previous research by providing a more optimum objective function value. GenACO improves the solution selection probability mechanism to ensure a more reliable balancing of the exploration and exploitation process involved in finding solutions. Moreover, the GenACO has two modes: cyclic and non-cyclic, confirmed to have the ability to increase the optimal cached data solution, improve average solution quality, and reduce the total time consumption from the previous research results. Result The experimental results demonstrated that the proposed GenACO outperformed the previous work by minimizing the objective function of cached data optimization from 0.4374 to 0.4350 and reducing the time consumption by up to 47%.

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Survey of applications, challenges and opportunities in fog computing

Cited by 21 publications

References 59 publications

Management Control and Integration Technology of Intelligent Production Line for Multi-Variety and Complex Aerospace Ring Forgings: A Review

Management Control and Integration Technology of Intelligent Production Line for Multi-Variety and Complex Aerospace Ring Forgings: A Review

Fog computing: A taxonomy, systematic review, current trends and research challenges

GenACO a multi-objective cached data offloading optimization based on genetic algorithm and ant colony optimization

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