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: 1741279
• 负责人: Xiuzhen Cheng
• 金额: $ 27.91万
• 依托单位: George Washington 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 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1741279&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.

信息传感设备的普遍存在为大传感数据（BSD）开辟了丰富的来源，其涵盖物联网、无线传感器网络、RFID、网络物理系统等。这种富含 BSD 的多样化系统是智能城市的构建块，智能设备部署在城市的每个角落。 BSD 的分析使用对于智慧城市管理城市资产以及监测空气状况、污染、气候变化、交通、安保和安全等至关重要。对智慧城市的高需求以及传感设备在智慧城市中的关键作用加速BSD 的爆发。不幸的是，BSD 的规模和动态性质压倒了当前捕获、存储、搜索、挖掘和可视化 BSD 的能力，因此成为智慧城市应用广泛发展的主要障碍。为了应对这些挑战，该项目将研究有关 BSD 获取、收集和计算的基本问题，并进行原则性的质量控制。目标是经济高效地收集和管理 BSD，以便在智慧城市应用中高效利用。针对BSD的大规模、动态关联性、模式多样性和低质量这四个具有挑战性的特征，将开发一套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慧和弹性城市的愿景，这将广泛影响国家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开展智慧特区计划的机会，因此不仅影响研究界，而且影响整个社会。该项目旨在解决数据采集、收集和计算关键阶段任务认知 BSD 管理的主要挑战。总体目标是缓解智慧城市应用中高昂的计算成本并提高 BSD 的利用效率。首先，将开发近似BSD采集方法，根据物理世界的变化趋势自动调整传感频率。这种采集方式可以有效减少智慧城市定期和长期监测的早期数据量。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，以降低与从传感设备向最终用户提供原始和冗余 BSD 相关的传输成本。最后，将研究评估 BSD 质量的新指标，然后应用于正确评估低质量 BSD 的容忍度，并深入了解数据质量对 BSD 采集、收集和计算的各个设计方面的根本影响。除了理论分析之外，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能城市项目上进行实验。相应的代码、数据集和教材将通过专门的项目网站发布。

项目成果

A model for integrating heterogeneous sensory data in IoT systems

在物联网系统中集成异构传感数据的模型

• DOI: 10.1016/j.comnet.2018.11.032
• 发表时间: 2019-02
• 期刊: Computer Networks
• 影响因子: 5.6
• 作者: Cheng, Siyao;Li, Yingshu;Tian, Zhi;Cheng, Wei;Cheng, Xiuzhen
• 通讯作者: Cheng, Xiuzhen

Sampling Based δ -Approximate Data Aggregation in Sensor Equipped IoT Networks

配备传感器的物联网网络中基于采样的近似数据聚合

• 发表时间: 2018-06
• 期刊: and Applications
• 作者: Li, Ji;Siddula, Madhuri;Cheng, Xiuzhen;Cheng, Wei;Tian, Zhi;Li, Yingshu.
• 通讯作者: Li, Yingshu.

A game theoretic analysis on block withholding attacks using the zero-determinant strategy

采用零行列式策略的扣块攻击博弈论分析

• DOI: 10.1145/3326285.3329076
• 发表时间: 2019-06
• 期刊: IEEE/ACM International Symposium on Quality of Service (IWQoS ’19
• 作者: Hu, Qin;Wang, Shengling;Cheng, Xiuzhen
• 通讯作者: Cheng, Xiuzhen

Wide and Recurrent Neural Networks for Detection of False Data Injection in Smart Grids

用于检测智能电网中的虚假数据注入的宽循环神经网络

• 发表时间: 2019-06
• 期刊: and Applications
• 作者: Wang, Yawei;Chen, Donghui;Zhang, Cheng;Chen, Xi;Huang, Baogui;Cheng, Xiuzhen
• 通讯作者: Cheng, Xiuzhen

Quantum Game Analysis on Extrinsic Incentive Mechanisms for P2P Services

P2P服务外在激励机制的量子博弈分析

• DOI: 10.1109/tpds.2019.2933416
• 发表时间: 2020-01-01
• 期刊: IEEE Transactions on Parallel and Distributed Systems
• 影响因子: 5.3
• 作者: Shengling Wang;Weiman Sun;Liran Ma;Weifeng Lv;Xiuzhen Cheng
• 通讯作者: Xiuzhen Cheng