One of the applications as a source of big data, there is a sensor network for--the environmental monitoring that is designed to detect the deterioration of the infrastructure, erosion control and so on. The specific targets are bridges, buildings, slopes and embankments due to the natural disasters or aging. Basic requirement of this monitoring system is to collect data over a long period of time from a large number of nodes that installed in a wide area. However, in order to apply a wireless sensor network (WSN), using wireless communication and energy harvesting, there are not many cases in the actual monitoring system design. Because of the system must satisfy various conditions; measurement location and time specified by the civil engineering; communication quality and topology obtained from the network technology; the electrical engineering to solve the balance of weather environment and power consumption that depends on the above-mentioned conditions. We propose the whole WSN design methodology especially for the electrical architecture that is affected by the network behavior and the environmental disturbance. It is characterized by determining recursively mutual trade-off of a wireless simulation and a power architecture simulation of the node devices. Furthermore, the system allows the redundancy of the design. In addition, we deployed the actual slope monitoring WSN that is designed by the proposed method to the snow-covered area. A conventional similar monitoring WSN, with 7 Ah Li-battery, it worked only 129 days in a mild climate area. On the other hand, our proposed system, deployed in the heavy snow area has been working more than 6 months (still working) with 3.2 Ah batteries. Finally, it made a contribution to the civil engineering succeeded in the real time observation of the groundwater level displacement at the time of melting snow in the spring season.
['Koichiro Yamashita', 'Takahisa Suzuki', 'Hongchun Li', 'Chen Ao', 'Yi Xu', 'Jun Tian', 'Keiji Kimura', 'Hironori Kasahara']
Architecture Design for the Environmental Monitoring System over the Winter Season