Center for Research on Complex Networks

德克萨斯南方大学(TSU)将建立一个复杂网络研究中心，以解决网络科学、网络优化和网络控制方面一些最具挑战性的问题。该中心的三个研究子项目领域是：(1)节能无线传感器网络，旨在通过集成安全的数据聚合和动态功率管理来制定节能方案，研究针对不确定性感知的传感器网络的高效多重适配算法，并探索动态控制和优化策略；(2)城市交通环境网络，用于开发和实施车辆排放模型和实时智能网络控制方法，以减少温室气体和污染物排放，从而改善城市空气质量；(3)分布式计算网络，用于在资源分配不均衡的网络上开发和实施计算算法。该中心的研究活动将涉及计算机科学、数学、物理、化学、工程技术、环境科学和交通运输等学科。它们将与南德克萨斯大学的科学、技术、工程和数学(STEM)教育项目紧密结合。该中心的一个关键成果将是增加攻读高级研究生学位的少数族裔学生的数量，有助于满足国家未来在STEM领域的关键劳动力需求。智力价值该中心的智力优势将是开发新的理论模型、先进的模拟工具和高效的物理试验台，用于通过多学科方法研究复杂网络。此外，研究成果将促进复杂网络的知识；整合来自不同科学领域的知识，专注于对复杂网络的理解；并针对其在现实世界复杂网络中的实际应用。该中心将对德克萨斯州立大学成为历史上黑人学院和大学中顶尖研究型大学的努力产生革命性的影响，并将促进该大学与来自学术界、产业界和政府的合作伙伴的合作。更广泛的影响中心的活动将产生的更广泛的影响将是代表不足的少数族裔本科生和研究生的数量增加，包括博士生，他们将受到激励，在STEM领域从事尖端跨学科研究。将发展TSU与该区域主要研究型大学以及国家实验室之间的伙伴关系。该中心的活动将加强台州大学科学与技术学院现有的环境毒理学博士课程，并将在台州大学开发一个新的多学科计算科学与工程(CSE)硕士学位课程。除了这些对当地更广泛的影响之外，该中心的研究还将为理解复杂的网络提供一个知识基础，从而能够制定和执行全球环境可持续性政策。

Texas Southern University (TSU) will establish a CREST Center for Research on Complex Networks to address some of the most challenging problems of network science, network optimization and network control. The three research sub-project areas of the Center are (1) Energy Efficient Wireless Sensor Networks, which is to develop energy efficient schemes by integrating secured data aggregation and dynamic power management, investigating efficient multiple adaptation algorithms for uncertainty-aware sensor networks, and exploring dynamic control and optimization strategies; (2) Urban Transportation Environmental Networks, which is to develop and implement vehicle emission models and real-time intelligent network control methods to reduce greenhouse gas and pollutant emissions, thereby improving urban air quality; and (3) Distributed Computational Networks, which is to develop and implement algorithms for computing on networks for which the resource allocation is uneven. The research activities in the Center will involve the disciplines of computer science, mathematics, physics, chemistry, engineering technology, environmental science, and transportation. They will be strongly integrated with the science, technology, engineering and mathematics (STEM) education programs at Texas Southern. An critical outcome of the Center will be to increase the number of under-represented minority students who pursue advanced graduate degrees, contributing to meeting the future critical workforce needs of the Nation in STEM fields.Intellectual MeritThe intellectual merit of the Center will be the development of novel theoretical models, advanced simulation tools, and efficient physical test-beds for the study of complex networks through a multi-disciplinary approach. Moreover, the outcomes of the research will advance the knowledge of complex networks; integrate knowledge from diverse scientific areas to focus on the understanding of complex networks; and target their practical applications in real-world complex networks. The Center will have a transformative impact on TSU's effort to become one of the top research universities among Historically Black Colleges and Universities and will promote the University's collaboration with its partners from academia, industry, and government. Broader Impacts The broader impacts that will result from the Center's activities will be an increase of the number of under-represented minority undergraduate and graduate students, including Ph.D. students, who will be motivated to pursue cutting-edge interdisciplinary research in STEM fields. Partnerships between TSU and leading research universities in the region as well as with national laboratories will be developed. The activities in the Center will enhance the existing Ph.D. program in Environmental Toxicology in the College of Science and Technology at TSU, and will develop a new Master's degree program in the multi-disciplinary Computational Science and Engineering (CSE) at TSU. Beyond these local broader impacts, the research of the Center will provide a knowledge base for the understanding of complex networks that could allow for the development and implementation of policies for global environmental sustainability.