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Sensors 2011, 11(7), 6533-6554; doi:10.3390/s110706533
Article

Design and Analysis of Self-Adapted Task Scheduling Strategies in Wireless Sensor Networks

1
,
2,* , 3
,
4
 and
1
1 College of Mathematics and Computer Science, Fuzhou University, Fujian 350108, China 2 Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA 3 Institute of Computer Science & Information Engineering, National Ilan University, Taiwan 4 Computer Science, Fisk University, Nashville, TN 37208, USA
* Author to whom correspondence should be addressed.
Received: 8 May 2011 / Revised: 9 June 2011 / Accepted: 20 June 2011 / Published: 27 June 2011
(This article belongs to the Special Issue Selected Papers from FGIT 2010)
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Abstract

In a wireless sensor network (WSN), the usage of resources is usually highly related to the execution of tasks which consume a certain amount of computing and communication bandwidth. Parallel processing among sensors is a promising solution to provide the demanded computation capacity in WSNs. Task allocation and  scheduling is a typical problem in the area of high performance computing. Although task allocation and scheduling in wired processor networks has been well studied in the past, their counterparts for WSNs remain largely unexplored. Existing traditional high performance computing solutions cannot be directly implemented in WSNs due to the limitations of WSNs such as limited resource availability and the shared communication medium. In this paper, a self-adapted task scheduling strategy for WSNs is presented. First, a multi-agent-based architecture for WSNs is proposed and a mathematical model of dynamic alliance is constructed for the task allocation problem. Then an effective discrete particle swarm optimization (PSO) algorithm for the dynamic alliance (DPSO-DA) with a well-designed particle position code and fitness function is proposed. A mutation operator which can effectively improve the algorithm’s ability of global search and population diversity is also introduced in this algorithm. Finally, the simulation results show that the proposed solution can achieve significant better performance than other algorithms.
Keywords: wireless sensor networks; task scheduling; particle swarm optimization; dynamic alliance wireless sensor networks; task scheduling; particle swarm optimization; dynamic alliance
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Guo, W.; Xiong, N.; Chao, H.-C.; Hussain, S.; Chen, G. Design and Analysis of Self-Adapted Task Scheduling Strategies in Wireless Sensor Networks. Sensors 2011, 11, 6533-6554.

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