以智能服装为背景,研究基于无线生理传感器网络任务调度模型。首先对数据融合任务进行子任务划分,并通过基于全局遗传模拟退火算法的任务调度算法对子任务进行优化调度,实现计算的并行化和分布化。由于各个传感器功能固定,导致子任务执行顺序也是固定的,因此任务调度优化算法可以事先运行。仿真结果表明,其加速比可以达到4倍以上,充分利用了网络中节点数量多的优势,有效地解决了单个节点有限的运算、存储资源与较高的整体计算性能需求之间的矛盾。
This paper researched the task scheduling of wireless physiological sensor network based on the background of smart clothing. According to the characteristics of wireless sensor networks, the nodes are always designed to be low power consumption, low speed and low computing performance. Thus the process of data fusion has to be ranged to the remote back-end. Apparently it’s a waste of time and computing resource. The work based on intelligent garment divides the task into small tasks and schedules the small tasks by the method of Global Genetic Simulated Annealing Algorithm (GGSAA). This method makes full use of network nodes in a large number resource and high performance requirement. to resolve the contradiction between limited computing