CNS Core: Small: Toward Real-Time Stream Processing in Edge Devices

CNS 核心：小型：迈向边缘设备中的实时流处理

基本信息

批准号：
2007854
负责人：
Kyoung-Don Kang
金额：
$ 50万
依托单位：
SUNY at Binghamton
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007854&HistoricalAwards=false
关键词：
CNS Core Small Toward Real

项目摘要

Timely analysis of real-time sensor data streams is essential to key applications in the Internet of Things (IoT), such as smart health, transportation, and energy. Although advanced stream processing engines (SPEs), such as Apache Storm, Flink, and Spark Streaming, provide powerful stream processing frameworks in a cloud, sending sensor data to the SPE for analysis over the wide area network may incur many deadline misses and create bottlenecks in the core Internet. A viable alternative is real-time sensor data stream processing in edge devices; however, it is challenging to support timing constraints using limited resources available in such devices. Real-time scheduling theory is not directly applicable, since it is agnostic to data semantics and usually based on worst-case assumptions for predictability that would be too pessimistic and resource inefficient in edge devices. The problem is becoming increasingly serious as the number of IoT devices and data volume increases rapidly. The proposed work aims to bridge the widening gap by investigating cost-efficient approaches for soft real-time stream processing at the edge. This project explores novel approaches to scheduling, sensor stream processing, and load sharing to significantly decrease deadline misses and communicational as well as computational resource consumptions, while enhancing the reliability of real-time stream processing. The research is expected to provide an enabling technology for important IoT applications with great societal impacts, such as those in healthcare, transportation, and energy that produce immense real-time sensor data streams, by substantially improving the timeliness and reliability of real-time stream processing with less resource consumptions compared to state-of-the-art SPEs. The investigator will use select research results to continue education and outreach efforts that include broadly disseminating publications and code that will be produced by this project, developing new courses and teaching materials on real-time stream processing, recruiting underrepresented groups of students to work on the project, and encouraging the younger generation to study computer science and pursue careers in industry and academia.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.

对实时传感器数据流的及时分析对于物联网（IoT）（例如智能健康，运输和能源）中的关键应用程序至关重要。尽管高级流处理引擎（SPE），例如Apache Storm，Flink和Spark流媒体，在云中提供强大的流处理框架，将传感器数据发送到SPE中以进行大区域网络分析，可能会导致许多截止日期失误并在核心Internet中创建瓶颈。可行的替代方案是边缘设备中的实时传感器数据流处理。但是，使用此类设备中可用的有限资源来支持正时约束是一个挑战。实时调度理论不是直接适用的，因为它不可或缺地适用于数据语义，并且通常基于最坏情况的假设的可预测性假设，而这种可预测性在边缘设备中过于悲观和资源效率低下。随着物联网设备的数量和数据量迅速增加，问题变得越来越严重。拟议的工作旨在通过调查边缘软实时流处理的成本效益方法来弥合扩大差距。该项目探讨了调度，传感器流处理和负载共享的新颖方法，以大大减少截止日期的错过和通信以及计算资源消耗，同时提高实时流处理的可靠性。预计该研究将为重要的物联网应用提供具有巨大社会影响的能力技术，例如医疗保健，运输和能源的能源，这些技术通过实质上提高实时流处理的及时性和资源消费较少的及时性和可靠性，而资源消耗较少，而资源消耗较少，而与较少的SPE相比，该技术会产生巨大的实时传感器数据流。研究者将使用精选的研究结果继续教育和宣传工作，包括广泛传播该项目将制作的出版物和守则，开发有关实时流程处理的新课程和新课程和教学材料，招募缺乏代表性的学生团体从事该项目的工作，并鼓励年轻一代通过研究计算机科学来促进宣传和学院的奖励。基金会的智力优点和更广泛的影响审查标准。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Smartphone Thermal Temperature Analysis for Virtual and Augmented Reality

适用于虚拟和增强现实的智能手机热温度分析

DOI：
10.1109/aivr50618.2020.00061
发表时间：
2020
期刊：
2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR
影响因子：
0
作者：
Zhang, Xiaoyang;Vadodaria, Harshit;Li, Na;Kang, Kyoung-Don;Liu, Yao
通讯作者：
Liu, Yao

Preprocessing via Deep Learning for Enhancing Real-Time Performance of Object Detection