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

Sensors

Wenzhong Guo¹  Naixue Xiong⁴  Han-Chieh Chao³  Sajid Hussain² 

[1] College of Mathematics and Computer Science, Fuzhou University, Fujian 350108, China; E-Mails:;Computer Science, Fisk University, Nashville, TN 37208, USA; E-Mail:;Institute of Computer Science & Information Engineering, National Ilan University, Taiwan; E-Mail:;Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA

关键词: wireless sensor networks;    task scheduling;    particle swarm optimization;    dynamic alliance;   

DOI  :  10.3390/s110706533

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.