Persistent short time series data acquisition algorithm for wireless smart sensor networks

Abstract

Most scientific, business and industrial workflows depend on receiving and collecting data from disturbed sensors and sensor networks. Wireless sensor technology helps to increase the number of disturbed sensors and sensor services and provides embedded mechanisms to access, analyze and visualize important sensed data. Wireless communication causes the largest part of energy consumption and, therefore, reducing the amount of data being sent by sensors is widely concerned. In this work, we developed an algorithm to suppress sensed data transmissions by computing prediction models to represent the measured data. The algorithm is based on the Improved Brown's method applying fractal dimension to forecast short time series