Exploiting Mobility-assisted Collaboration for Adaptive Aquatic Sensor Networks

利用移动辅助协作实现自适应水生传感器网络

• 批准号: 1029683
• 负责人: Guoliang Xing
• 金额: $ 36万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1029683&HistoricalAwards=false
• 关键词: Exploiting; Mobility; assisted; Collaboration; Adaptive

The objective of this research is to establish a principled framework for the design and operation of aquatic sensor networks consisting of resource-limited nodes. The proposed approach is to exploit adaptation and collaboration among nodes to holistically deal with or even leverage uncertainties in sensing, communication, and mobility. Main research thrusts include online sensor and fusion calibration for dynamic environments, model-driven radio power adaptation to achieve assured communication performance, and exploitation of node mobility and fluid motion in the joint optimization of sensing, networking, and control to realize efficient coverage and tracking. The proposed methodology will be validated in detection and tracking of harmful algal blooms at the MSU Kellogg Biological Station using networks of robotic fish.The project will result in a unifying design framework for aquatic sensor networks to achieve energy-efficient operation with assured spatiotemporal sensing performance. Some methodologies developed in this project, e.g., exploiting the seemingly undesirable environmental disturbances, could apply to aerial and terrestrial sensor networks and thus benefit those fields as well.The project is expected to bring aquatic sensor networks much closer to their envisioned applications, and positively impact monitoring of lakes and other ecosystems, tracking of oil spills and pollutants, and surveillance of ports and rivers. The project will enrich two graduate-level courses and provide interdisciplinary training for graduate and undergraduate students. The project will also offer an excellent opportunity to reach out to K-12 students and schools through interactive lectures, robotic fish competitions, and participation in a teacher training program at MSU.

本研究的目的是建立一个原则性的框架，由资源有限的节点组成的水生传感器网络的设计和操作。所提出的方法是利用节点之间的适应和协作，以整体处理，甚至利用传感，通信和移动性的不确定性。主要研究方向包括动态环境下的在线传感器和融合校准、模型驱动的无线电功率自适应以实现有保证的通信性能，以及在传感、网络和控制的联合优化中利用节点移动性和流体运动以实现高效的覆盖和跟踪。拟议的方法将在密歇根州立大学凯洛格生物站使用机器鱼网络检测和跟踪有害藻华的过程中得到验证，该项目将为水生传感器网络提供一个统一的设计框架，以实现节能运行，同时确保时空传感性能。本项目开发的一些方法，例如，利用看似不受欢迎的环境干扰，可以应用于空中和陆地传感器网络，从而使这些领域受益，该项目预计将使水生传感器网络更接近其预期的应用，并积极影响湖泊和其他生态系统的监测，石油泄漏和污染物的跟踪，以及港口和河流的监视。该项目将丰富两门研究生课程，并为研究生和本科生提供跨学科培训。该项目还将提供一个很好的机会，通过互动讲座，机器鱼比赛，并在密歇根州立大学教师培训计划的参与，接触到K-12学生和学校。