Dispersion-based prediction framework for estimating missing values in wireless sensor networks
Wireless Sensor Networks (WSNs) have attracted many researchers in the past few years due to their applicability for a wide-range of applications. WSNs rely on unreliable sensing schemes in which a sensor might lose some data due to the inherent characteristics of such networks. Estimating missing values that cope with other collected ones is crucial for some applications. In this paper, we introduced a framework dedicated to predicting missing values in WSNs. The key idea is to estimate missing values according to the natural spread (i.e. dispersion) of the guilty sensors. The framework considers cases in which distance and time play a significant role in estimating missing values. Thus, accurate values might be generated as compared to state-of-the-art central tendency measurements such as mean, median, mode, and midrange.
dispersion estimation wireless data networks data mining