Data Loss and Reconstruction for Wireless Environmental Sensor Networks

Environmental Sensor Networks (ESNs) in forests facilitate the study of fundamental processes, and the development of wireless communication makes ESNs into 'intelligent' sensor network, named as Wireless Environmental Sensor Networks (WESNs). However, data loss is prevalent in wireless transmission, which may result in incompletion of sensory datasets. Thus, if we want to achieve a satisfactory accuracy in a WESNs system, the task of recovering data from achieved sensory datasets is unavoidable. Previous works provide many approaches to solve the data missing problem. Compared with other methods, Compressing Sensing (CS) is powerful technique for estimating data, which can utilize a small fraction of data to reconstruct the entire dataset. In real forests, because of the complicated geographic conditions and deployment of sensors, sensory data will largely loss during the wireless transmission. Despite CS technique is a better choice, it cannot be directly applied for the data missing problem. In this paper, we will present a reliable WESNs system and a better approach based on CS to reconstruct sensory datasets.