Green city: An efficient task joint execution strategy for mobile micro-learning
Mobile micro-learning has received extensive attention in the research of smart cities because it is a novel fusion service mode of the mobile Internet, cloud computing, and micro-learning. However, due to the explosively increased applications of the mobile micro-learning and the limited resources...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2018-06-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147718780933 |
| _version_ | 1797712021198733312 |
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| author | Li Yang Ruijuan Zheng Junlong Zhu Mingchuan Zhang Ruoshui Liu Qingtao Wu |
| author_facet | Li Yang Ruijuan Zheng Junlong Zhu Mingchuan Zhang Ruoshui Liu Qingtao Wu |
| author_sort | Li Yang |
| collection | DOAJ |
| description | Mobile micro-learning has received extensive attention in the research of smart cities because it is a novel fusion service mode of the mobile Internet, cloud computing, and micro-learning. However, due to the explosively increased applications of the mobile micro-learning and the limited resources of mobile terminals, an effective energy saving approach for mobile micro-learning is urgently required. For this end, this article proposes an efficient task joint execution strategy to reduce energy consumption. First, a new matching method of time series is proposed to obtain the latest requested record, which can provide guidance for the selection of a future service mode. Second, a mapping-level service mode and a cloud-level service mode are proposed to achieve seamless switching. Finally, the genetic algorithm is used to find the optimal executive strategy. In addition, the experimental results show that the proposed method can effectively realize the target of energy saving by using real data set. |
| first_indexed | 2024-03-12T07:15:39Z |
| format | Article |
| id | doaj.art-c8d57d7bc2824383ae27e064b17fc573 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T07:15:39Z |
| publishDate | 2018-06-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-c8d57d7bc2824383ae27e064b17fc5732023-09-02T22:49:18ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772018-06-011410.1177/1550147718780933Green city: An efficient task joint execution strategy for mobile micro-learningLi YangRuijuan ZhengJunlong ZhuMingchuan ZhangRuoshui LiuQingtao WuMobile micro-learning has received extensive attention in the research of smart cities because it is a novel fusion service mode of the mobile Internet, cloud computing, and micro-learning. However, due to the explosively increased applications of the mobile micro-learning and the limited resources of mobile terminals, an effective energy saving approach for mobile micro-learning is urgently required. For this end, this article proposes an efficient task joint execution strategy to reduce energy consumption. First, a new matching method of time series is proposed to obtain the latest requested record, which can provide guidance for the selection of a future service mode. Second, a mapping-level service mode and a cloud-level service mode are proposed to achieve seamless switching. Finally, the genetic algorithm is used to find the optimal executive strategy. In addition, the experimental results show that the proposed method can effectively realize the target of energy saving by using real data set.https://doi.org/10.1177/1550147718780933 |
| spellingShingle | Li Yang Ruijuan Zheng Junlong Zhu Mingchuan Zhang Ruoshui Liu Qingtao Wu Green city: An efficient task joint execution strategy for mobile micro-learning International Journal of Distributed Sensor Networks |
| title | Green city: An efficient task joint execution strategy for mobile micro-learning |
| title_full | Green city: An efficient task joint execution strategy for mobile micro-learning |
| title_fullStr | Green city: An efficient task joint execution strategy for mobile micro-learning |
| title_full_unstemmed | Green city: An efficient task joint execution strategy for mobile micro-learning |
| title_short | Green city: An efficient task joint execution strategy for mobile micro-learning |
| title_sort | green city an efficient task joint execution strategy for mobile micro learning |
| url | https://doi.org/10.1177/1550147718780933 |
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