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Volume 29 Issue 2
March  2014
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Hai-Long Shi, Dong Li, Jie-Fan Qiu, Chen-Da Hou, Li Cui. A Task Execution Framework for Cloud-Assisted Sensor Networks[J]. Journal of Computer Science and Technology, 2014, 29(2): 216-226. DOI: 10.1007/s11390-014-1424-y
Citation: Hai-Long Shi, Dong Li, Jie-Fan Qiu, Chen-Da Hou, Li Cui. A Task Execution Framework for Cloud-Assisted Sensor Networks[J]. Journal of Computer Science and Technology, 2014, 29(2): 216-226. DOI: 10.1007/s11390-014-1424-y
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A Task Execution Framework for Cloud-Assisted Sensor Networks

  • 1 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China

Funds: This paper is supported in part by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No. XDA06010403, the International Science and Technology Cooperation Program of China under Grant No. 2013DFA10690, the National Natural Science Foundation of China under Grant No. 61003293, and the Beijing Natural Science Foundation under Grant No. 4112054.
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  • Received Date: November 17, 2013
  • Revised Date: January 06, 2014
  • Published Date: March 04, 2014
    Abstract
  • As sensor networks are increasingly being deployed, there will be more sensors available in the same region, making it strategic to select the suitable ones to execute users' applications. We propose a task execution framework, named sTaskAlloc, to execute application energy effciently by two main parts. First, considering that the energy consumption of an application is inversely proportional to the utilization rate of sensors, we present a hot sensor selection algorithm, HotTasking, to minimize the energy consumption of new added applications by selecting the most suitable sensor. Second, when a sensor is shared by multiple applications, proposed MergeOPT (a concurrent tasks optimization algorithm) is used to optimize energy consumption further by eliminating redundant sampling tasks. Experimental results show that sTaskAlloc can save more than 76% of energy for new added applications compared with existing methods and reduce up to 72% of sampling tasks when a sensor is shared by more than 10 applications.
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    • References (15)
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