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: 0546871
• 负责人: Jorge Cortes
• 金额: $ 40万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2008-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0546871&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.

项目概述低成本、高度自动化的车辆的出现，配备了控制、通信、传感和计算能力，为移动的传感器网络在广泛应用中的部署铺平了道路。例子包括环境监测、海洋学研究、高压力、快速部署行动和民用基础设施的健康监测。在这些设想的应用中，许多关键的过程发生在时间和空间尺度上，不能有效地采样与当前的ap-proaches。移动的传感器网络有望提供丰富的、原位的时空数据，从而彻底改变动态自然现象的检测、估计和监测。受控移动性与分布式数据融合能力的结合将使传感器网络能够提供广阔的空间覆盖范围，真实的响应短暂的事件，并跟踪发生在固定地点以外的关键过程。分布式数据融合的最新技术只考虑静态网络，因此不直接适用于ad-hoc动态变化的移动的网络。网络系统运动协调的最新技术水平只开发了集中的估计和信号处理方法。由于这些局限性，当前的移动的网络过于僵化，无法科普许多关键物理过程的小规模特征和快速演化特征。该项目的主要目标是综合可扩展的协调算法，用于执行空间分布式传感任务的移动的网络。最大化收集数据的信息内容的分布式策略将允许未来的传感器网络以快速、自主和最佳的方式适应不断变化的条件。为了实现这一愿景，该项目将解决动态场景中移动的网络收集的数据的分布式、原位聚合，以及网络移动性的信息驱动、可扩展协调，以最佳地执行所需的传感任务。该研究计划将采用一种雄心勃勃的综合方法，包括三个方面：（i）一个健全、统一的框架，使不同的合作战略能够严格正规化并加以比较。Thisefort将有利于复杂的传感任务的合作策略的模块化设计，通过combinationof更简单的算法执行更基本的目标;（ii）系统理论的工具，以评估协调算法的正确性，鲁棒性和可扩展性。为了评估最佳的权衡betweenperformance和能源分配相结合的通信，运动和传感方案，本researchwill评估合作策略和空间分布的任务的复杂性措施;（iii）新的，系统的设计方法，允许分解成局部目标为个别代理的全球传感任务。这一努力旨在综合容错，可扩展的算法，坐在限制上可实现的性能，操作时间和能量消耗的移动的网络进行搜索和估计任务。创新的技术方法建立在PI和合作者的一系列非常有前途的成果的基础上，依赖于协作和拓扑控制、自动机和混合系统理论、机器人技术、无线通信和运营研究等学科。更广泛的影响。在这项工作中开发的技术将有助于设计自主和高效的mobilenetworks执行国土安全，工业流程，医疗保健和environment.The拟议的研究将导致适用于广泛的场景中的交叉和协同技术的关键任务实时信息收集和数据开发是重要的。研究结果将分别与蒙特雷湾水族馆研究所和美国航天局艾姆斯合作，应用于海洋学和灾害管理。拟议的教育活动已纳入研究计划，包括以下举措：（一）通过设计项目、暑期实习和在新创建的实验室环境中进行工程研究演示，让本科生参与研究;（ii）开设一门关于“运动协调”的本科课程和一门关于“合作移动的网络”的研究生课程;（iii）在加州州立数学和科学暑期学校为高中生开设一门关于控制和机器人技术的课程，并在加州大学旧金山分校附近的社区学院开设短期和研究讲座。研究和教育材料将通过一个互动网站提供给高中教师、科学界和公众。还将定期开展广泛传播活动（期刊出版物、会议介绍、演讲稿）。教育活动的评估，基于教育过程和学生的成果，将与UCSC卓越教学中心合作完成。将特别注意将包容性教学做法纳入日常教育活动，以解决代表性不足的学生的保留问题。