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.

全覆盖模式，即部署区域的每个点都必须被至少一个传感器覆盖，在无线传感器网络社区中非常普遍。对于涉及大规模跟踪运动的应用程序，例如跟踪带着被盗物品逃跑的小偷和强盗，跟踪森林中的动物，以及跟踪森林火灾的蔓延，使用全覆盖模型使传感器部署成本过高。目前还没有一个可靠的模型可以用于系统地部署如此大规模的应用程序。该项目提出了一种新的覆盖模型，称为陷阱覆盖，可用于稀疏传感器网络的系统部署，同时确保对感兴趣的运动进行频繁跟踪。由于陷阱覆盖模型所隐含的网络固有的稀疏性，大多数现有的理论和系统工作都不适用于这个新模型。该项目的总体目标是为所有大规模运动跟踪应用建立坚实的基础，并解决此类应用中面临的关键系统问题。该项目采用严格的数学分析，在大规模传感器网络测试平台上进行实验，并在校园范围内部署名为AutoWitness的目标跟踪系统，以设计、开发和评估该项目中开发的算法和协议。除了为本科生和研究生提供构建真正的无线传感器网络的实践研究经验外，AutoWitness系统还有望帮助减少大学校园内的财产盗窃。