Personal and Ubiquitous Computing

, Volume 22, Issue 1, pp 121–134 | Cite as

Hybrid computation offloading for smart home automation in mobile cloud computing

  • Jie Zhang
  • Zhili Zhou
  • Shu Li
  • Leilei Gan
  • Xuyun Zhang
  • Lianyong Qi
  • Xiaolong Xu
  • Wanchun DouEmail author
Original Article


Smart home automation enables the users to realize the access control of the in-home appliances by the mobile devices. With the rapid development of mobile cloud computing, offloading computation workloads of the home automation applications to nearby cloudlets has been treated as a promising approach to overcoming inherent flaws of portable devices, such as low battery capacity. The computing capacity of cloudlet is limited compared with the distant public cloud whose elastic computation resources are almost infinite. Therefore, some mobile services should wait for the occupied computation resources in the cloudlet to get released, which is less energy-efficient. In view of this challenge, we model the waiting time spending in the cloudlet as a M/M/m/ queue and propose a hybrid computation offloading algorithm for home automation applications to minimize the total energy consumption of the mobile devices within a given constant deadline. The proposed algorithm combines cloudlet with public clouds, providing a more energy-efficient offloading strategy for home automation applications. Technically, a particle swarm optimization (PSO)-based heuristic algorithm is implemented to schedule mobile services. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of our proposed algorithm.


Home automation Mobile cloud computing Cloudlet Mobile service Energy consumption 


Funding information

This research is supported by the National Key R&D Program of China (no. 2017YFB1001800); the National Science Foundation of China under grant no. 61672276, no. 61702277, no. 61402258, and no. 61602253; and the Key Research and Development Project of Jiangsu Province under grant nos. BE2015154 and BE2016120. Besides, this work is also supported by the Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University, and the open project from State Key Laboratory for Novel Software Technology, Nanjing University under grant no. KFKT2017B04.


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Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  2. 2.Jiangsu Engineering Centre of Network Monitoring, School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  3. 3.Department of Electrical and Computer EngineeringThe University of AucklandAucklandNew Zealand
  4. 4.School of Information Science and Engineering, Chinese Academy of Education Big DataQufu Normal UniversityRizhaoChina

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