Missing data is an inevitable problem in wireless sensor network and the way missing values are handled can significantly affect the analysis results involving such data. To address data missing issues, spatial correlation and temporal correlation modeling can be applied. This paper aims at reviewing some popular spatial and temporal correlation based methods. The proposed review includes a critical overview through a summary of pros and cons of these methods and a comparison between them based on simulation results. To our best knowledge, there is no such comparative benchmarking study in the current literature.
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.
With the accelerated process of economic globalization, inter-regional linkages are becoming closer, exchanges become more frequent, spill over effects of public services are increasing day by day. In this paper, the author analyzes the level of public services in the provinces spatial correlation by using the theory of spatial economics. The results indicate that the level of the supply of public service has a strong spatial correlation.