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

职业：为物联网提供空间数据系统支持

• 批准号: 1845789
• 负责人: Mohamed Sarwat
• 金额: $ 55万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845789&HistoricalAwards=false
• 关键词: CAREER; Towards; Spatial; Data; Systems

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.

物联网(IoT)代表着一个设备网络，每个设备都配备了各种传感器，可以收集有关环境的数据。自2012年以来，物联网一直在快速增长，目前约有90亿台设备，其中2018年约有200亿台设备，到2020年连接到物联网的设备超过300亿台。物联网设备可以安装在建筑物或交通路口等静态位置，以收集有关城市空气污染、水质或交通的数据。智能手表等设备也可以连接到移动对象(人)上，以监测人的运动模式、心率或燃烧的卡路里。在这两种情况下，物联网设备生成的数据都具有表示所感知观测的地理空间位置和时间的空间和时间属性。此外，不同的设备测量不同的观测结果(例如，温度、声音、速度等)。该项目的目标是构建创新的可扩展技术，无缝连接从各种地理分布的物联网设备收集的数据，这些设备可以有效地管理和分析各种空间和时间尺度上不断增长的物联网数据。该项目的成果将为政策制定者、科学家、企业和公民提供一个工具，以便在交通、环境经济、公共安全和健康等各种应用中更好地利用物联网数据并从中提取价值。为了融合研究和教育，并实现社会影响，项目组还将在亚利桑那州坦佩市开设一个试点分会，启动《城市数据科学倡议》。该倡议旨在通过分析从物联网设备收集的数据，让当地社区和当地学生参与到解决许多城市问题的工作中。该项目的总体目标是开发图形数据库系统技术，能够高效地存储、管理和执行对链接的物联网数据的实时空间/时空图形查询，并支持大规模物联网数据的可扩展处理。为了实现这一目标，该项目的研究工作包括设计和开发新的空间数据存储、索引、处理和管理技术，以适应从物联网设备收集的不断增长的数据量和快速增长的数据。相对于现有的大型空间数据系统和数值框架，一种新的物联网数据抽象方法将扩展新近发展的大型空间数据系统，为物联网应用的开发提供应用编程接口(API)。新开发的方法不仅考虑了数据的空间分布，还考虑了每个传感器产生的信号的物理和数学特性。此外，新的物联网抽象方法包括新颖的查询算子以及查询优化策略，可以有效地评估涉及传统时空数据处理和物联网数据上的数字信号处理操作的混合工作负载。新方法还通过设计一个中间件来弥合物联网设备和空间数据系统之间的差距，该中间件将向中央系统传输数据的物联网设备与访问此类物联网数据的应用程序的需求相结合。该项目的另一个成果是图形查询处理器，它可以优化和评估通用空间谓词，如空间范围、连接和K最近邻居(KNN)，以及对链接的物联网数据发出的图形查询中的时间谓词，即使新事物和观察定期添加到物联网图形中也是如此。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。