Embedded Databases for Agricultural and Environmental Applications

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The rapid growth of data collected by sensor devices for environmental, agricultural, and industrial applications is challenging existing approaches to data processing. Although data collection and analysis provide improved efficiency and sustainability, there are also associated costs including energy use, electronic and battery waste, and deployment and maintenance time. My long-term research goal is to improve the efficiency of database software on low-power embedded sensor devices resulting in reduced energy use, lower maintenance costs, and increased device lifetime. My research group has produced software for SQL and key-value embedded databases, and efficient techniques for data indexing, sorting, and storage. This software runs on devices with as little as 4 KB of memory and provides unique support for embedded applications where there is no standard database library. The diverse group of students involved in the last five years includes four graduates now working as college professors, four NSERC USRA recipients, and over twenty undergraduates with software engineering roles in industry. The research objective for the next five years is to adapt data management algorithms to the latest hardware and network technologies and create techniques that simplify the deployment of sensor systems for environmental and agricultural users. Database algorithms require optimization for devices with limited memory, and there is a need for more efficient algorithms for non-volatile memory devices such as NOR flash, which have unique storage properties. The latest, low-energy network protocols, such as LoRa, allow for innovative techniques for data management, as local data processing on the sensor can replace transmitting data over the network. We will build on our previously developed software and create new research to reduce the amount of data wirelessly transmitted by utilizing data compression, adaptive sampling, and machine learning techniques. These techniques will improve sensor efficiency by adaptively determining what data to store, transmit, and discard for best performance. This program will make fundamental contributions to understanding data processing with limited resources, release open source software to improve ease of use, and train a diverse group of five graduate and ten undergraduate HQP with skills in embedded software engineering. A primary use case is supporting time series data collected in agricultural applications. With increasing impacts of climate change and water scarcity, intelligent use of technology to improve sustainability, reduce water and fertilizer usage, and increase harvests is critical. A specific focus is continuing and expanding collaborations with the City of Kelowna, vineyard and agricultural producers in the Okanagan, and environmental agencies to deploy our research results to tackle key challenges of water management, climate monitoring, and agricultural productivity.