Development of a wireless sensor based monitoring system for urban 'greening' technologies

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SAP is a global software and data management company with strengths in the areas of data acquisition, data management, cloud computing and the development of intelligent decision making systems. The recent emergence of SAP's 'Urban Matters' program that focuses on developing technology to help urban centers improve their environmental footprint, has led SAP's newest R&D laboratory located in Waterloo Ontario to explore the opportunity to use wireless sensor networks to collect large amounts of data to be analyzed via unique software in order to automate 'smart' decisions. Dr. Youbin Zheng of the University of Guelph has been developing expertise in the creation of affordable sensor networks for various applications including green roof monitoring and irrigation, urban tree planting monitoring and irrigation, and urban agriculture applications. This project will prototype the concept and develop a flexible framework for both the sensor networks and the software/application requirements to acquire and process data to improve the performance and function of urban green technologies. The prototype will focus initially on green roofs. Because the green roof R&D program is already at a stage of pilot scale installations, it is an ideal system to test the ability of such a sensor network to be installed and to monitor things like moisture, light levels, substrate nutrient retention, water runoff quantities and qualities, temperatures at numerous levels as well as fire monitoring and prevention. These data are important to monitor and make real-time adjustments decisions for the maximum efficacy and performance of the green roof system. This accumulated data will subsequently be processed with analytics to continually aid in Canada's development of green roof technologies, with recommendations being generated for plant and substrate selection, rain water retention characteristics, and water runoff chemistry. Further benefits include the production of an accurate predictive model, which will aid a building owner's decision making process on assessing the proper materials, plants and systems to maximize the efficiency and ROI of a green roof installation.