Designing Communications and Power for an Instrumentation System for Natural Resources Research in a Remote Mountainous Location
Author(s) -
Herbert Hess
Publication year - 2016
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/p.26703
Subject(s) - instrumentation (computer programming) , router , terrain , computer science , data acquisition , remote sensing , environmental science , engineering , computer network , operating system , ecology , biology , geology
To investigate important aspects of a mountainous ecosystem, a group of students designed and built an instrumentation array. The goal was to monitor effects of climate change. The pristine environment provided a unique and valuable place for establishing a baseline of data. In all, 78 sensors were divided into three wired micro-networks at altitudes of 1200m, 1800m, and 2400m. Among these sensors were CO2 sensors, precipitation, temperature, soil moisture, leaf wetness, animal traffic, etc. Important challenges for power and communication included three-dimensional terrain, forest canopy, and creative sensor placement. The three networks were linked wirelessly to each other and to an Internet router to storage and presentation two thousand kilometers distant. The networks provided communications through self-healing mesh topologies. Experience soon showed that the current state of technology in not as well developed as one might expect for reliable communications in this challenging environment. Power was available from combinations of wind, photovoltaic, and hydroelectric sources with lithium battery storage. Providing small amounts of power in the remote location with solely renewable sources encountered proved to be quite difficult. To maintain the pristine nature of the environment and test conditions, power apparatus had to be carefully placed and concealed. This paper describes the design and installation of this instrumentation system in the remote mountainous environment. Two years after installation was completed, student learning is assessed by improvements quality of data collection over the course of the project and by the quality of data presented since the project.
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