NeTS: Small: Target Monitoring with Low-Cost Sensor Networks

NeTS：小型：利用低成本传感器网络进行目标监控

• 批准号: 1116430
• 负责人: Duc Tran
• 金额: $ 38.39万
• 依托单位: University of Massachusetts Boston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116430&HistoricalAwards=false
• 关键词: NeTS; Small; Target; Monitoring; Low

Most research on target monitoring using sensor networks requires the geographic locations of the sensors to be known. However, in practice, it is not always possible for every sensor node to have a built-in GPS-like capability. On the other hand, applying a localization procedure to all the sensor nodes can be expensive for large networks. Even if the location information of many sensors can be computed, it may never be used because targets usually appear very sparse. This project, therefore, tackles the target monitoring problem in the context of low-cost sensor networks, where a sensor node does not need built-in capability to determine its location. Instead, hopcount information serves as the primary source of information to track the target. The challenges result from the unknown mobility and nature of the target and the coarseness of the hopcount data. The matter is more sophisticated if there are multiple moving targets and it is not known whether they move in groups or independently.The research objective of the project is to design and implement a novel solution framework to address these challenges. This framework, based on a machine-learning approach, can be applied to a wide range of sensor networks which can have modest resource capacities or be deployed in non-conventional physical settings such as under the water or on wet ground. Building on the research, an education plan is proposed that seeks to transfer knowledge to younger generations, improve student enrollment and retention, and encourage active participation from students of under-represented groups.

大多数使用传感器网络进行目标监测的研究都需要知道传感器的地理位置。然而，在实践中，并非每个传感器节点都具有内置的类似GPS的能力。另一方面，对所有传感器节点应用定位过程对于大型网络来说可能是昂贵的。即使许多传感器的位置信息可以计算，它可能永远不会被使用，因为目标通常显得非常稀疏。因此，该项目，解决了目标监测问题的背景下，低成本的传感器网络，传感器节点不需要内置的能力，以确定其位置。相反，跳数信息作为跟踪目标的主要信息源。这些挑战来自于目标的未知移动性和性质以及跳数数据的粗糙度。如果存在多个移动目标，并且不知道它们是成组移动还是独立移动，则问题更加复杂。该项目的研究目标是设计和实现一种新颖的解决方案框架，以应对这些挑战。这个框架，基于机器学习的方法，可以应用于广泛的传感器网络，可以有适度的资源能力或部署在非传统的物理环境，如水下或湿地。在研究的基础上，提出了一项教育计划，旨在向年轻一代传授知识，提高学生入学率和保留率，并鼓励代表性不足群体的学生积极参与。