Abstract
With the rapid development of Internet of things, the traditional city model is no longer applicable. Therefore, the emerging concept of smart city meets the needs of users. Smart medical system needs to meet the service needs of different priority users in emergency. In order to improve the service quality of users, we apply mobile edge computing technology to the smart medical system. In this article, we consider the priority of the offloading tasks and establish a priority-mobile edge cloud ecosystem (priority-MECE). The system taked into account user priorities, the combined cost value genereted by the delay and energy consumption was reduced. In the priority-MECE computing offloading system, an optimization problem is established with the task offloading cost as the optimization goal. The result of this optimization problem can provide an optimal offloading scheme for priority-MECE. In order to solve this optimization problem, we propose a priority constraint optimal offloading algorithm(priority-COFA) based on dynamic programming. In order to prove that the performance of this algorithm is better than random selection, we design a simulation experiment based on previous studies. Finally, the simulation results show that the proposed algorithm is superior to random offloading and no priority offloading.
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21 June 2022
A Correction to this paper has been published: https://doi.org/10.1007/s11036-022-01980-0
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Acknowledgements
This work was supported by the China National Science Foundation under Grant 62172079. This work was also funded by Natural Science Foundation of Hubei Province NO. 2020CFB697.
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The original version of this article, published on 26 February 2022 needs modification on first author updated affiliation. Since the first author's work unit has changed, the information of "Hubei University of Education" should be removed. This is as per Author‘s preference.
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Li, M., Xiong, N., Zhang, Y. et al. Priority-MECE: A Mobile Edge Cloud Ecosystem Based on Priority Tasks Offloading. Mobile Netw Appl 27, 1768–1777 (2022). https://doi.org/10.1007/s11036-022-01930-w
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DOI: https://doi.org/10.1007/s11036-022-01930-w