BIGDATA: Collaborative Research: IA: Big Data Analytics for Optimized Planning of Smart, Sustainable, and Connected Communities

将村庄、城镇和城市转变为智能、互联和可持续的社区是未来十年最关键的技术挑战之一。实现这一愿景取决于现有的社区基础设施，如交通、通信和能源系统，能够无缝地整合可再生能源、智能传感器和电动汽车等可持续组件。这样的整合将确保未来的社区是真正可持续的，并通过展示理想的品质来连接，包括：a)零能源，因为它们在能源生产上是自给自足的；b)零中断，因为整个社区的通信链路是超可靠的，并且经历了显著的低中断；c)零拥堵，因为整个社区的交通拥堵最小化。基于这一总体愿景，该项目的目标是为智能、互联和可持续社区开发一个新的规划框架，通过优化决定如何、何时、何地部署或升级社区基础设施，实现零能耗、零停电和零拥堵的目标。这些决策将由大量的社区数据驱动，这些数据来自多个来源，包括交通、能源、交通、通信需求和其他社会技术信息，以便就如何逐步和有机地将社区转变为完全可持续和真正连接的环境做出明智的决策。该问题的规模和异构性要求对用于处理、分析和可视化异构数据的工具进行创新，以及用于监控该社区基础设施性能的数据感知度量。这项研究的一个关键要素是创建一个虚拟试验台，通过利用来自弗吉尼亚理工大学和佛罗里达州零能耗社区以及其他来源（如美国能源部）的真实大数据集，该试验台可以准确地重建、模拟和评估理论框架。该试验台旨在开放访问，并将能够支持宿主机构的研究以及其他需要非专有多领域开放数据集的用户。因此，这项研究的整体性有望促进可持续和互联社区的全球部署。拟议的研究将由一个智能社区大数据挑战活动补充，这将使社区广泛参与。该教育计划包括新的以大数据为中心的课程，以及研究生和本科生大规模参与大数据和智能社区研究。通过开源软件和定期研讨会和教程确保广泛传播。将组织K-12外展活动，以吸引代表性不足的学生群体参与大数据研究。这一变革性研究将通过开发首个大数据驱动的整体方法，为通信、能源和交通网络等至关重要的系统共同规划、优化和部署社区基础设施，为智能、互联和可持续社区奠定理论和实践基础。通过汇集来自数据科学、电气工程、土木和建筑工程等跨学科领域的专家，这项研究将产生以下几个创新：1)新颖的大数据技术，忠实地为智能社区创建时空模型，整合来自异质来源的数据，揭示给定智能社区的组成和运行；2)新颖的数据驱动性能指标，从随机几何中推进强大的数学工具，通过易于处理的零能耗、零停电和零拥堵概念，明确量化智能社区的健康状况；3)先进的分析工具，从优化理论中提出新颖的想法，为部署、升级和运行各种社区基础设施节点设计最有效的策略，考虑到数据和社区的规模、动态和结构；4)虚拟智能社区测试平台，可以利用开放的非专有现实世界大数据集准确地重建、模拟和评估理论框架。

Transforming villages, towns, and cities into smart, connected, and sustainable communities is one of the most critical technological challenges of the coming decade. Realizing this vision is contingent upon enabling existing community infrastructure such as transportation, communications, and energy systems, to seamlessly integrate sustainable components such as renewable sources, smart sensors, and electric vehicles. Such an integration will ensure that tomorrow's communities are truly sustainable and connected by exhibiting desirable qualities that include: a) zero energy, in that they are self-sufficient in their energy production, b) zero outage, in that communication links across the community are ultra-reliable and experience significantly low interruption, and c) zero congestion, in that the traffic congestion is minimized across the community. With this overarching vision, the goal of this project is to develop a new planning framework for smart, connected and sustainable communities that allows meeting such zero-energy, zero-outage, and zero-congestions goals by optimally deciding on how, when, and where to deploy or upgrade a community's infrastructure. These decisions will be driven by massive volumes of community data, stemming from multiple sources that can include mobility, energy, traffic, communication demands, and other socio-technological information, to make informed decisions on how to gradually and organically transform a community into a fully sustainable and truly connected environment. The scale and heterogeneity of this problem necessitates the need for innovation in the tools used to process, analyze, and visualize heterogeneous data, as well as the data-aware metrics used to monitor the performance of this community infrastructure. One key element of this research is creation of a virtual testbed that can accurately reconstruct, simulate, and evaluate the theoretical framework by leveraging real-world big data sets from Virginia Tech and a zero-energy community in Florida as well as other sources, such as the DOE. The testbed is intended to be open access and will be able to support both research at host institution as well as other users requiring non-proprietary multi-domain open-data sets. The holistic nature of this research is thus expected to catalyze the global deployment of sustainable and connected communities. The proposed research will be complemented by a smart community big data challenge event that will enable broad community participation. The educational plan includes new big data-centric courses, as well as a large-scale involvement of graduate and undergraduate students in big data and smart communities research. Broad dissemination is ensured via open-source software and periodic workshops and tutorials. K-12 outreach events will be organized to attract under-represented student groups to big data research.This transformative research will lay the theoretical and practical foundations of smart, connected, and sustainable communities by developing the first big data-driven holistic approach to joint planning, optimization, and deployment of community infrastructure for systems of critical importance, such as communication, energy, and transportation networks. By bringing together interdisciplinary domain experts from data science, electrical engineering, and civil and architectural engineering, this research will yield several innovations: 1) Novel big data techniques for faithfully creating spatio-temporal models for smart communities that integrate data from heterogeneous sources and shed light on the composition and operation of a given smart community, 2) Novel, data-driven performance metrics that advance powerful mathematical tools from stochastic geometry to explicitly quantify the health of smart communities via tractable notions of zero energy, zero outage, and zero congestion, 3) Advanced analytical tools that bring forward novel ideas from optimization theory to devise the most effective strategies for deploying, upgrading, and operating various community infrastructure nodes, given the scale, dynamics, and structure of both the data and the community, and 4) A virtual smart community testbed that can accurately reconstruct, simulate, and evaluate the theoretical framework by leveraging open non-proprietary real-world big data sets.