NeTS-NOSS: Collaborative Research: Doing More with Less: Tracking Movements Using a Sparse Sensor Network

NetS-NOSS：协作研究：用更少的资源做更多的事情：使用稀疏传感器网络跟踪运动

• 批准号: 0721817
• 负责人: Prasun Sinha
• 金额: $ 20.4万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0721817&HistoricalAwards=false
• 关键词: NeTS; NOSS; Collaborative; Research; Doing

The full coverage model, where every point in the deployment region must be covered by at least one sensor, is pervasive in the wireless sensor network community. For applications that involve tracking movements at large scale such as tracking of thieves and robbers fleeing with stolen objects, tracking of animals in forests, and tracking the spread of forest fire, using the full coverage model makes sensor deployment prohibitively expensive. No sound model currently exists that can be used for systematic deployment of such large scale applications.This project proposes a novel model of coverage called Trap Coverage that can be used for systematic deployment of sparse sensor networks, while ensuring frequent tracking of movements of interest. Most existing theoretical and systems work are not applicable to this new model because of the inherent sparsity of the network implied by the trap coverage model. The overall goal of this project is to establish a strong foundation for all large scale movement tracking applications and address the key systems issues faced in such applications. The project applies rigorous mathematical analysis, experimentation on a large scale sensor network testbed, and real-life deployment of a campus-wide object tracking system called AutoWitness to design, develop, and evaluate the algorithms and protocols developed in this project. In addition to providing hands-on research experience to undergraduate and graduate students in building a real wireless sensor network, the AutoWitness system is expected to help reduce property thefts in a university campus.

在无线传感器网络社区中，整个部署区域中的每个点都必须由至少一个传感器覆盖，其中部署区域中的每个点都必须覆盖。对于涉及大规模跟踪运动的应用，例如跟踪盗窃物体的盗贼和强盗，在森林中跟踪动物的跟踪以及使用完整的覆盖型模型跟踪森林火灾的传播，使传感器部署变得非常昂贵。目前没有可用于系统地部署如此大规模应用程序的声音模型。本项目提出了一种新型的覆盖范围模型，称为陷阱覆盖范围，可用于系统地部署稀疏传感器网络，同时确保频繁地跟踪感兴趣的运动。大多数现有的理论和系统工作不适用于该新模型，因为陷阱覆盖模型暗示的网络的固有稀疏性。该项目的总体目标是为所有大规模移动跟踪应用程序建立强大的基础，并解决此类应用程序中所面临的关键系统问题。该项目应用了严格的数学分析，对大型传感器网络测试的实验以及校园范围内的对象跟踪系统的现实部署，称为自动化，以设计，开发和评估该项目中开发的算法和协议。除了为建立真正的无线传感器网络的本科生和研究生提供动手研究经验外，自动化系统还有望帮助减少大学校园中的财产盗窃。