基于旋转门算法的自适应变频数据采集策略

Strategy of self-adaptive frequency conversion data acquisition based on swing door trending algorithm

针对无线传感器网络在监测类似室内或大棚等微气候环境数据时,传统的等间隔时间数据采集方法存在数据大量冗余、浪费网络带宽的问题,以及现有自适应变频数据采集策略仍然非常复杂的问题,提出一种基于旋转门算法的自适应变频数据采集策略,该策略根据旋转门算法能否"套住"数据以及能连续"套住"数据的次数,自适应地调整数据采集的间隔时间。仿真和实验结果证明,该策略与传统的等间隔时间数据采集方法相比,可以降低数据采集量76%以上,减少数据传输量90%以上。该策略具有创新性,实现简单,不仅可行,而且高效。

For the wireless sensor network monitoring the micro-climate environmental data such as indoor or greenhouses,the traditional equal interval time data acquisition method had a large amount of data redundancy,waste of network bandwidth and the existing self-adaptive frequency conversion data acquisition strategies were still very complex.This paper proposed a new self-adaptive frequency conversion data acquisition strategy based on the swing door trending(SDT)algorithm,which adjusted the interval of data acquisition adaptively according to whether the SDT algorithm could“trap”data and the number of times it could“trap”data consecutively.Simulation and experimental results show that the proposed strategy can lower the volume of data acquisition more than 76%and reduce capacity of data transmission more than 90%compared with the traditional data acquisition method with equal interval time.The strategy is innovative,simple to implement,not only feasible,but also efficient.