Green city: An efficient task joint execution strategy for mobile micro-learning

Li Yang, Ruijuan Zheng, Junlong Zhu, Mingchuan Zhang, Ruoshui Liu and Qingtao Wu

International Journal of Distributed Sensor Networks, 2018, vol. 14, issue 6, 1550147718780933

Abstract: 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.

Keywords: Green city; mobile micro-learning; Cloudlet; time-series matching; genetic algorithm (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:14:y:2018:i:6:p:1550147718780933

DOI: 10.1177/1550147718780933

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