BIGDATA: F: Collaborative Research: Acquisition, Collection and Computation of Dynamic Big Sensory Data in Smart Cities

BIGDATA：F：协作研究：智慧城市动态大传感数据的采集、收集和计算

• 批准号: 1741338
• 负责人: Xiang Chen
• 金额: $ 28.25万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1741338&HistoricalAwards=false
• 关键词: BIGDATA; Collaborative; Research; Acquisition; Collection

The ubiquity of information-sensing devices has opened up abundant sources for Big Sensory Data (BSD), which span over Internet of Things, wireless sensor networks, RFID, cyber physical systems, to name a few. Such diverse BSD-rich systems are the building blocks for smart cities where smart devices are deployed in every corner of a city. The analytical use of BSD is essential to smart cities in managing a city's assets and monitoring air conditions, pollution, climate change, traffic, security and safety, etc. The high demand for smart cities and the pivotal role of sensing devices in smart cities accelerate the explosion of BSD. Unfortunately, the size and dynamic nature of BSD overwhelm current capability to capture, store, search, mine and visualize BSD, and hence have become a major hindrance to the widespread development of smart city applications. To tackle these challenges, this project will investigate fundamental issues regarding acquisition, collection and computation of BSD with principled quality control. The goal is to cost-effectively collect and manage BSD for efficient utilization in smart city applications. A set of foundational principles, algorithms and tools for BSD management will be developed in response to the four challenging characteristics of BSD, which are large scale, correlated dynamics, mode diversity and low quality. The outcomes of this research will contribute to the vision of smart and resilient cities, which broadly impact the nation's emerging smart city infrastructure and citizens' mobile quality of life. This project also offers an opportunity to collaborate with the Government of the District of Columbia on the Smarter DC Initiative, and hence impacts not only the research community but also the society at large. This project aims at tackling major challenges in task-cognizant BSD management at the critical phase of data acquisition, collection and computation. The overarching goal is to alleviate the high computational cost and improve the utilization efficiency of BSD in smart city applications. First, approximate BSD acquisition methods will be developed that automatically adjust the sensing frequency based on the changing trend of the physical world. Such acquisition methods can effectively reduce the data volume at an early stage during periodic and long-term monitoring of smart cities. Second, approximate sampling algorithms, knowledge discovery methods, and integration methods will be developed for task-specific multimodal BSD, in order to reduce the transmission cost associated with delivering otherwise raw and redundant BSD from sensing devices to end users. Finally, new metrics for evaluating BSD quality will be investigated and then applied to properly assess the tolerance of low-quality BSD and provide deep understanding of the fundamental impact of data quality on various design aspects of BSD acquisition, collection and computation. Besides theoretical analysis, simulation and experimental studies will be carried out on real BSD, including experimentation on real-world Smart City projects at Washington DC. The corresponding code, datasets, and educational materials will be released via a dedicated project website.

信息感应设备的无处不在为大型感觉数据（BSD）打开了跨越物联网，无线传感器网络，RFID，网络物理系统的大量资源，以等。如此多样化的BSD富裕系统是智能城市的基础，这些城市在城市的每个角落都部署了智能设备。 BSD的分析用途对于智能城市管理城市的资产和监测空气条件，污染，气候变化，交通，安全和安全性等至关重要。对智能城市的高需求以及智能城市中传感设备在智能城市中的关键作用加速了BSD的爆炸。不幸的是，BSD淹没捕获，存储，搜索，矿山和可视化BSD的当前功能的大小和动态性质已成为对智能城市应用程序广泛发展的主要障碍。为了应对这些挑战，该项目将调查有关通过原则质量控制的BSD获取，收集和计算的基本问题。目的是成本效益收集和管理BSD，以在智能城市应用中有效利用。将开发出一系列BSD管理的基本原理，算法和工具，以应对BSD的四个具有挑战性的特征，BSD的四个具有挑战性的特征是大规模的，相关的动态，模式多样性和低质量。这项研究的结果将有助于智能和韧性城市的愿景，这广泛影响了该国新兴的智慧城市基础设施和公民的移动生活质量。该项目还提供了与哥伦比亚特区政府合作就智能DC计划进行合作的机会，因此不仅会影响研究界，而且会影响整个社会。该项目旨在应对数据获取，收集和计算的关键阶段的任务认知BSD管理方面的重大挑战。总体目标是减轻高计算成本并提高BSD在智能城市应用中的利用效率。首先，将开发近似BSD采集方法，该方法会根据物理世界的变化自动调整传感频率。这种采集方法可以在定期和长期监测智能城市的早期阶段有效地减少数据量。其次，将为特定于特定于任务的多模式BSD开发近似采样算法，知识发现方法和集成方法，以减少与将原始和冗余BSD从传感设备传递到最终用户相关的传输成本。最后，将研究用于评估BSD质量的新指标，然后应用以正确评估低质量BSD的容忍度，并深入了解数据质量对BSD获取，收集和计算各个设计方面的基本影响。除了理论分析外，还将对实际BSD进行仿真和实验研究，包括对华盛顿特区现实世界中智能城市项目的实验。相应的代码，数据集和教育材料将通过专用项目网站发布。

项目成果

Quantum Analysis on Task Allocation and Quality Control for Crowdsourcing With Homogeneous Workers

• DOI: 10.1109/tnse.2020.2997716
• 发表时间: 2020-10
• 期刊: IEEE Transactions on Network Science and Engineering
• 影响因子: 6.6
• 作者: Minghui Xu;Shengling Wang;Qin Hu;Hao Sheng;Xiuzhen Cheng

Count Sketch with Zero Checking: Efficient Recovery of Heavy Components

• DOI: 10.1109/icassp39728.2021.9413853
• 发表时间: 2021-06
• 期刊: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Guanqiang Zhou;Zhi Tian

Solving the Crowdsourcing Dilemma Using the Zero-Determinant Strategies

使用零决定性策略解决众包困境

• DOI: 10.1109/tifs.2019.2949440
• 发表时间: 2020
• 期刊: IEEE Transactions on Information Forensics and Security
• 影响因子: 6.8
• 作者: Hu Qin;Wang Shengling;Cheng Xiuzhen;Ma Liran;Bie Rongfang
• 通讯作者: Bie Rongfang

A class of event-triggered coordination algorithms for multi-agent systems on weight-balanced digraphs

• DOI: 10.23919/acc.2018.8431314
• 发表时间: 2018-06
• 期刊: 2018 Annual American Control Conference (ACC)
• 作者: Ping Xu;Cameron Nowzari;Z. Tian

Latency-and-Coverage Aware Data Aggregation Scheduling for Multihop Battery-Free Wireless Networks

• DOI: 10.1109/twc.2020.3036408
• 发表时间: 2021-03-01
• 期刊: IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
• 影响因子: 10.4
• 作者: Cai, Zhipeng;Chen, Quan
• 通讯作者: Chen, Quan