College of Mathematics and Computer Science, Fuzhou University, Fujian 350108, China.
Sensors (Basel). 2011;11(7):6533-54. doi: 10.3390/s110706533. Epub 2011 Jun 27.
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.
在无线传感器网络（WSN）中，资源的使用通常与执行任务高度相关，这些任务会消耗一定的计算和通信带宽。传感器之间的并行处理是在 WSN 中提供所需计算能力的一种有前途的解决方案。任务分配和调度是高性能计算领域的一个典型问题。尽管过去已经对有线处理器网络中的任务分配和调度进行了深入研究，但针对 WSN 的对应方案仍在很大程度上尚未得到探索。由于 WSN 的限制，如有限的资源可用性和共享通信介质，现有的传统高性能计算解决方案不能直接在 WSN 中实现。本文提出了一种用于 WSN 的自适应任务调度策略。首先，提出了一种基于多代理的 WSN 架构，并为任务分配问题构建了动态联盟的数学模型。然后，提出了一种有效的用于动态联盟的离散粒子群优化（PSO）算法（DPSO-DA），该算法具有精心设计的粒子位置代码和适应度函数。该算法还引入了一种突变算子，可以有效地提高算法的全局搜索能力和种群多样性。最后，仿真结果表明，所提出的解决方案可以实现比其他算法显著更好的性能。
