CAREER: Information-driven distributed coordination of mobile sensor networks in dynamic scenarios

职业：动态场景下信息驱动的移动传感器网络分布式协调

• 批准号: 0830601
• 负责人: Jorge Cortes
• 金额: $ 28.37万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-01-01 至 2012-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0830601&HistoricalAwards=false
• 关键词: CAREER; Information; driven; distributed; coordination

Project SummaryThe emergence of low-cost, highly-autonomous vehicles equipped with control, communication, sensingand computing capabilities is paving the way for the deployment of mobile sensor networks in a wide rangeof applications. Examples include environmental monitoring, oceanographic research, high-stress, rapid de-ployment operations, and health monitoring of civil infrastructure. In these envisioned applications, manycritical processes occur at temporal and spatial scales that cannot be effectively sampled with current ap-proaches. Mobile sensor networks hold the promise to provide the rich, in-situ spatio-temporal data needed torevolutionize the detection, estimation, and monitoring of dynamic natural phenomena. Controlled mobilityintegrated with distributed data fusion capabilities will enable sensor networks to provide broad spatial cov-erage, react to short-lived events in real time, and track key processes that occur away from fixed sites.The state of the art in distributed data fusion only considers static networks, and therefore is not directlyapplicable to ad-hoc, dynamically changing mobile networks. The state of the art in motion coordinationof networked systems has only developed centralized approaches to estimation and signal processing. As aresult of these limitations, current mobile networks are too rigid to cope with the small-scale features andthe rapid evolution characteristic of many key physical processes.Intellectual Merit. The major objective of this project is the synthesis of scalable coordination algorithmsfor mobile networks performing spatially-distributed sensing tasks. Distributed strategies that maximize theinformation content of collected data will allow future sensor networks to adapt to changing conditions in arapid, autonomous and optimal fashion. To make this vision a reality, this project will address the distributed,in-situ aggregation of data collected by mobile networks in dynamic scenarios, and the information-driven,scalable coordination of the network mobility to optimally perform the required sensing tasks.The research plan will adopt an ambitious integrative approach composed of three thrusts: (i) a sound,unifying framework where different cooperative strategies can be rigorously formalized and compared. Thiseffort will facilitate the modular design of cooperative strategies for complex sensing tasks via the combinationof simpler algorithms performing more basic objectives; (ii) system-theoretic tools to evaluate the correctness,robustness and scalability properties of coordination algorithms. To assess the optimal trade-offs betweenperformance and energy allocation in combined communication, motion, and sensing scenarios, this researchwill evaluate complexity measures for cooperative strategies and spatially-distributed tasks; and (iii) novel,systematic design methodologies that allow to break down global sensing tasks into local objectives forindividual agents. This effort seeks to synthesize fault tolerant, scalable algorithms that sit at the limits onthe achievable performance, operational time and energy consumption of mobile networks conducting datafusion and estimation tasks. The innovative technical approach builds on a set of very promising results bythe PI and collaborators, hinging upon disciplines such as cooperative and topology control, automata andhybrid systems theory, robotics, wireless communications, and operations research.Broader Impacts. The techniques developed in this work will help design autonomous and efficient mobilenetworks performing critical tasks in homeland security, industrial processes, health care and the environment.The proposed research will lead to crosscutting and synergistic technologies applicable to a wide rangeof scenarios where real-time information gathering and data exploitation are important. The results willbe transferred to oceanographic and disaster management applications in collaboration with the MontereyBay Aquarium Research Institute, and NASA Ames, respectively.The proposed educational activities are integrated into the research plan and consist of the followinginitiatives: (i) involvement of undergraduate students in research via design projects, summer internshipsand engineering research demonstrations in a newly-created laboratory environment; (ii) development of anundergraduate course on \Motion Coordination" and a graduate course on \Cooperative Mobile Networks";(iii) offering of a course on control and robotics in the California State Summer School for Mathematics andScience for high-school students, and expository and research talks at community colleges near UCSC. Re-search and educational materials will be made available to high-school teachers, the scientific community andthe general public via an interactive website. Regular activities for broad dissemination (journal publications,conference presentations, lecture notes) will also be pursued. The evaluation of the educational activities,based on the educational process and the students' outcomes, will be done in collaboration with the UCSCCenter for Teaching Excellence. Special attention will be paid to integrate inclusive teaching practices intothe daily educational activity to address retention issues concerning underrepresented students.

项目总结，配备控制，通信，传感和计算功能的低成本，高度自治的车辆的出现为在广泛的应用程序中部署移动传感器网络铺平了道路。例子包括环境监测，海洋研究，高压力，快速雇用操作以及对民用基础设施的健康监测。在这些设想的应用中，在时间和空间尺度上发生许多临界过程，这些过程无法用当前的AP填充物有效地采样。移动传感器网络有望提供所需的丰富，原地时空数据所需的扭转趋势，使动态自然现象的检测，估计和监测。具有分布式数据融合功能的受控迁移率集成将使传感器网络能够提供广泛的空间COV-erage，实时对短寿命事件做出反应，并跟踪从固定站点出发的关键过程。分布式数据融合中的最新技术仅适用于静态网络，因此对静态网络不可直接适用于Ad-Ad-hoc，并且不可直接更改的移动网络，动态更改移动网络。网络系统的运动协调状态仅开发了估计和信号处理的集中式方法。作为这些局限性的库存，当前的移动网络太僵化了，无法应对许多关键物理过程的小规模特征和快速进化特征。该项目的主要目的是综合移动网络的可扩展协调算法，这些算法执行了空间分布的传感任务。最大化收集数据的信息信息的分布式策略将使未来的传感器网络适应不断变化的ARAPID，自主和最佳方式。为了使这一愿景成为现实，该项目将解决移动网络在动态场景中收集的数据的分布，原位汇总，以及信息驱动的，可扩展的网络移动性协调以最佳执行所需的感应任务。研究计划将采用雄心勃勃的综合方法，由三个强度构成：（i）形式化的框架，统一的策略是不同的。 ThisFort将通过更简单的算法的组合来促进合作策略的模块化设计，以实现更基本的目标。 （ii）评估协调算法的正确性，鲁棒性和可扩展性的系统理论工具。为了评估在沟通，运动和感应场景中的表现和能源分配之间的最佳权衡，该研究将评估合作策略的复杂性度量以及空间分布的任务； （iii）新颖的系统设计方法，可以将全球传感任务分解为本地目标的本地目标。这种努力旨在综合容忍，可扩展的算法，这些算法位于可实现的性能，运营时间和能源消耗的移动网络，这些移动网络进行数据融资和估计任务。创新的技术方法基于PI和合作者的一系列非常有希望的结果，取决于合作和拓扑控制，自动机和Hybrid Systems理论，机器人技术，无线通信和操作研究等学科。这项工作中开发的技术将有助于设计自主和高效的MobilEnetworks在国土安全，工业过程，医疗保健和环境中执行关键任务。拟议的研究将导致适用于广泛范围的方案，实时信息收集和数据开发很重要。结果将与蒙特利贝水族馆研究所合作转移到海洋和灾难管理应用程序中，分别将NASA AMES合作。拟议的教育活动被整合到研究计划中，并包括以下教育效应：（i）通过设计项目，夏季工程研究项目，夏季工程学研究示范，在新的研究项目中参与了本科生在研究项目中的参与。 （ii）开发有关运动协调的Anundergradula课程“和合作移动网络的研究生课程”;（iii）在加利福尼亚州立大学的数学和高中生，以及UCSC附近社区学院的扩建和研究谈判的加利福尼亚州立暑期学院的控制和机器人学课程中。重新搜索和教育材料将通过互动网站向高中教师，科学界和公众提供。还将进行广泛传播的定期活动（期刊出版物，会议演讲，讲义）也将进行。基于教育过程和学生成果的教育活动的评估将与UCSCCENTER合作进行，以进行卓越的教学。将特别注意将包容性的教学实践整合到日常的教育活动中，以解决有关代表性不足的学生的保留问题。