Managing Data for the Internet of Things: Storage, Updates, and Transactions

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Consider the following real-world use case presented in The Economist in June of 2016. Tom Rogers, an almond farmer in California Central Valley, must pay high costs by constantly monitoring many variables such as moisture, nutrient content, health threats, and temperature, and by taking appropriate actions to control the levels of the values of these variables. Tom wants to reduce operating costs by improving overall utilization of water and fertilizers needed to grow the water-thirsty almond nuts. Tom also wants to access real-time information about the state of his farm that he may use in making decisions to achieve the desired cost reductions. Tom's problem is a perfect example for using a so-called smart farming application, an instance of a growing number of applications spanning domains as diverse as agriculture, transportation (asset management), housing (smart buildings), and infrastructure (smart power grids). What is common to these applications is the increasing use of the Internet of Things (IoT) to monitor operations in order to achieve the goal of reducing overall operating costs and making accessible real-time data about the state of the monitored entities. IoT is a paradigm of networking where the networked points (called “things”) are uniquely identifiable devices equipped with embedded connectivity. To achieve his stated goals, Tom installed wireless sensors throughout his nut plantation to measure the aforementioned variables. The measurements are then periodically sent to dedicated third party cloud-servers which compute the appropriate levels of water and fertilizer that should be applied to the plantation. Finally, the results are used to automatically drive the farm's irrigation system by delivering precise amounts of water and fertilizers to the nut groves. The data archived on the cloud database is also processed by analytics applications. Our proposed research focuses on the design and implementation of storage, update, and transaction models for managing IoT data that flows from sensors to a cloud gateway, to a cloud database, to applications, and back. In the short term, we will investigate the architectural choices for the management of IoT data, and study data management (e.g., stream cleaning and fusion, and complex event detection) at the cloud gateway level by considering the IoT data requirements of gigantic volume, velocity and variety. In a longer term, we will study architectures for storage models of the cloud IoT distributed databases, as well as appropriate update and transactions models for IoT data management, and develop a prototype system. We anticipate that this research, which will enjoy the collaboration of a fortune 500 company as well as several renowned database and machine learning experts, will be significant with respect to new directions in the database industry, to IoT applications, as well as to the improvement of the IoT technology.