CAREER: Towards Spatial Data Systems Support for the Internet of Things

点击翻译按钮获取中文摘要

The Internet of Things (IoT) represents a network of devices, each equipped with a variety of sensors that collect data about the environment. The IoT has been growing rapidly since 2012 with about 9 billion devices, including around 20 billion devices in 2018 and reaching more than 30 billion devices connected to the IoT by 2020. An IoT device can be installed in a static location such as a building or a traffic intersection to collect data about air pollution, water quality, or traffic in a city. A device, such as a smart watch, can also be attached to a moving object (human) to monitor a person's movement pattern, heart rate, or calories burned. In both cases, data generated by an IoT device possess spatial and temporal attributes that represent the geospatial location and time of the sensed observation. Furthermore, various devices measure different observations (e.g., temperature, sound, speed, etc.). The aim of this project is to build innovative scalable technologies that seamlessly connect data collected from various geographically distributed IoT devices that can effectively manage and analyze the ever growing IoT data at various spatial and temporal scales. The results of the project will provide a tool for policy makers, scientists, businesses, and citizens to better utilize and extract value from IoT data in a variety of applications, such as transportation, environmental economics, public safety and health. To integrate research and education and achieve societal impact, the project team will also kick off the "Data Science for Cities Initiative" with a pilot chapter in the city of Tempe, Arizona. The initiative aims to engage local communities and local students in working on solutions to many urban problems by analyzing data collected from IoT devices.The overarching goal of the project is to is to develop graph database systems technology that can efficiently store, manage, and execute real-time spatial / spatio-temporal graph queries on linked IoT data and support scalable processing of large-scale IoT data. To achieve that, the research effort in this project includes the design and development of novel spatial data storage, indexing, processing and management techniques that scale to the ever-increasing volume and fast rate of data collected from IoT devices. As opposed to existing big spatial data systems and numerical frameworks, a novel IoT data abstraction method will extend recently developed big spatial data systems to provide an Application Programming Interface (API) for development of IoT applications. The newly developed method takes into account not only the spatial distribution of the data, but also the physical and mathematical characteristics of signals generated by each sensor. Furthermore, the new IoT abstraction method includes novel query operators as well as query optimization strategies, which can efficiently evaluate a hybrid workload that involves classic spatial and spatio-temporal data processing and digital signal processing operations on IoT data. The new method also bridges the gap between IoT devices and the spatial data system by designing a middleware that integrates the IoT devices streaming data to the central system with the requirements of applications accessing such IoT data. Another outcome of the project is a graph query processor that can optimize and evaluate general-purpose spatial predicates such as spatial range, join and K-Nearest Neighbors (KNN) as well as temporal predicates in a graph query issued on linked IoT data in real time even with new things and observations being regularly added to the IoT graph.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.