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Digital manufacturing and urban conservation based on the Internet of Things and 5 G technology in the context of economic growth

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  • Zhou, Yang
  • Tang, Zhen
  • Qian, Xiaoyan
  • Mardani, Abbas

Abstract

The increasing economic level impacts the urban structure. Therefore, based on the continuous improvement of the level of science and technology, it is necessary to conduct green city innovation and development planning in combination with economic development. This article combines IoT technology and 5 G technology to build an intelligent planning model under the current economic level background. Moreover, based on network dual connectivity research, our work discusses and analyzes the application of dual connectivity technology and specific architecture selection in the early stage of 5 G deployment and 4 G LTE network collaborative networking. Simultaneously, this paper studies and analyzes related protocols and proposals that were proposed and frozen, researches and improves the hybrid networking technology in 5 G heterogeneous networks, and constructs the functional structure model. In addition, this paper designs a systematic planning method based on the demands of innovative green city planning. Finally, this paper analyzes the performance of this method through simulation experiments. The research results show that the model constructed in this paper has certain effects.

Suggested Citation

  • Zhou, Yang & Tang, Zhen & Qian, Xiaoyan & Mardani, Abbas, 2021. "Digital manufacturing and urban conservation based on the Internet of Things and 5 G technology in the context of economic growth," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    • Handle: RePEc:eee:tefoso:v:170:y:2021:i:c:s0040162521003383
    DOI: 10.1016/j.techfore.2021.120906
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    Cited by:

    1. Kumar, Anil & Agrawal, Rohit & Wankhede, Vishal A & Sharma, Manu & Mulat-weldemeskel, Eyob, 2022. "A framework for assessing social acceptability of industry 4.0 technologies for the development of digital manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    2. Nadeem, Kashif & Wong, Sut I. & Za, Stefano & Venditti, Michelina, 2024. "Digital transformation and industry 4.0 employees: Empirical evidence from top digital nations," Technology in Society, Elsevier, vol. 76(C).
    3. Yogesh K. Dwivedi & A. Sharma & Nripendra P. Rana & M. Giannakis & P. Goel & Vincent Dutot, 2023. "Evolution of Artificial Intelligence Research in Technological Forecasting and Social Change: Research Topics, Trends, and Future Directions," Post-Print hal-04292607, HAL.
    4. Ávila-Robinson, Alfonso & Islam, Nazrul & Sengoku, Shintaro, 2022. "Exploring the knowledge base of innovation research: Towards an emerging innovation model," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    5. Khan, Syed Abdul Rehman & Ponce, Pablo & Yu, Zhang & Ponce, Katerine, 2022. "Investigating economic growth and natural resource dependence: An asymmetric approach in developed and developing economies," Resources Policy, Elsevier, vol. 77(C).

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