Data Reconstruction for Cyber-Physical Landslide Detection System

Abstract

Wireless Sensor Network (WSN) systems are often used to collect data from the environment for predicting landslide occurrence. However, due to the instability of wireless communications in WSN systems, data loss could occur often. To reconstruct the missing soil moisture data, heterogeneous data with spatio-temporal relations are used, such as soil moisture and rainfall intensity. In the spatial reconstruction method, we take into consideration not only the distance but also the difference of rainfall intensity between two locations. In the temporal reconstruction method, soil moisture will decrease because of evaporation if there is no rainfall. We use historical evaporation rate to reconstruct missing soil moisture data when there is no rainfall. If there is rain, we take a period of sensed soil moisture data to reconstruct any missing data. As a result, we can improve the accuracy of data reconstruction by selecting either spatial or temporal reconstruction results that have a smaller estimation error. The Root Mean Squared Error of spatiotemporal reconstruction is below 2%, even when there is 80% random missing soil moisture data. This is due to both spatiotemporal considerations, as well as, the use of heterogeneous data (rainfall intensity).