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.

信息传感设备的无处不在为大传感数据(BSD)开辟了丰富的来源，大传感数据横跨物联网、无线传感器网络、RFID、网络物理系统等。这种多样化的BSD丰富的系统是智能城市的基石，智能设备部署在城市的每一个角落。BSD的分析使用对于智能城市在管理城市资产和监测空气状况、污染、气候变化、交通、安全和安全等方面至关重要。对智能城市的高需求和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的大小和动态特性压倒了当前捕获、存储、搜索、挖掘和可视化BSD的能力，从而成为智能城市应用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BSD的获取、收集和计算的基本问题，并进行原则性的质量控制。其目标是以经济高效的方式收集和管理BSD，以便在智能城市应用程序中高效利用。针对BSD的规模大、动态相关、模式多样性和质量低等四个具有挑战性的特点，提出了一套BSD管理的基本原则、算法和工具。这项研究的成果将有助于实现智能和弹性城市的愿景，这将广泛影响国家新兴的智能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就更聪明的DC倡议进行合作的机会，因此不仅影响了研究界，也影响了整个社会。该项目的目的是在数据采集、收集和计算的关键阶段解决任务认知的业务可持续发展管理方面的重大挑战。总的目标是缓解BSD在智慧城市应用中的高昂计算成本，提高BSD的利用效率。首先，将开发近似的BSD捕获方法，根据物理世界的变化趋势自动调整传感频率。这样的采集方式可以在智慧城市的周期性和长期监测中，有效地减少前期的数据量。其次，将为特定任务的多模式BSD开发近似采样算法、知识发现方法和集成方法，以降低与将其他原始和冗余的BSD从传感设备传输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，然后将其应用于适当地评估低质量BSD的容忍度，并提供对数据质量对BSD采集、收集和计算的各个设计方面的基本影响的深刻理解。除了理论分析外，还将在真实的BSD上进行模拟和实验研究，包括在华盛顿特区的真实世界智慧城市项目上进行实验。相应的代码、数据集和教育材料将通过专门的项目网站发布。

项目成果

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

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

Decentralized Dynamic ADMM with Quantized and Censored Communications

• DOI: 10.1109/ieeeconf44664.2019.9048719
• 发表时间: 2019-11
• 期刊: 2019 53rd Asilomar Conference on Signals, Systems, and Computers
• 作者: Yaohua Liu;K. Yuan;Gang Wu;Z. Tian;Qing Ling

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