ITR: Distributed Learning in Sensor Networks

ITR：传感器网络中的分布式学习

• 批准号: 0312413
• 负责人: Sanjeev Kulkarni
• 金额: $ 24万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0312413&HistoricalAwards=false
• 关键词: ITR; Distributed; Learning; Sensor; Networks

The possibility of deploying a large number of networked sensors presents great opportunities for a host of commercial, military, and homeland security applications, but also presents enormous technical challenges. To fully utilize the potential of suchnetworks, advances will be required on a number of fronts and through all layers of the network. However, in addition to dealing with most of the difficult issues of wireless communication networks, sensor networks give rise to a number of additionalissues as well.Sensor networks must do a great deal more than just support communication. Transporting bits does not automatically lead to intelligent decision-making, and connectivity does not automatically result in coordination. In a sensor network there is a joint purpose to be accomplished by the entire network as a whole. There are fundamental questions about which bits to transmit, where to send them, and how to utilize them. Moreover, there are issues of whether to do computations locally, or to pass information to higher layers and perform partially centralized computations. These tasks must be accomplished in the face of scarce resources, notably limited time, bandwidth, and power.One key area where breakthroughs are needed, and which is the focus of this project, concerns how to learn, adapt, and make decisions to carry out the goals of the sensor network in a complex and distributed environment. Accomplishing a jointgoal for the entire network poses significant information processing challenges. Myopic or local information gathered by the individual sensors must be fused for globaldecision-making. These tasks are complicated by the large number of sensors, each of which may be heterogeneous, multimodal, possibly dynamic, and uncalibrated. In some applications the scene-sensor geometry may be unknown or only partially known, andthe environment itself will typically be complex and dynamic. The key element that distinguishes learning, adaptation, and decision-making in sensor networks with most previous work in these areas is the distributed and local nature of the information gathering together with the rich and varied structure in the information and decisions to be made in the face of limited resources. A degenerate appeal to existing methods by sending all the data to a centralized node may be impossible or infeasible in many situations, and certainly will not be the most effective way to utilize limited resources. In this project, we address key problems in distributed learning in sensor networks, to begin to develop an understanding of the fundamental capabilities and limitations of learning and decision-making in sensor networks.

部署大量网络传感器的可能性为许多商业、军事和国土安全应用带来了巨大的机遇，但也带来了巨大的技术挑战。 为了充分利用此类网络的潜力，需要在多个方面和网络的各个层面取得进展。 然而，除了处理无线通信网络的大多数难题之外，传感器网络还引发了许多其他问题。传感器网络必须做的不仅仅是支持通信。 传输比特不会自动导致智能决策，连接也不会自动导致协调。在传感器网络中，整个网络作为一个整体需要实现一个共同的目标。 存在关于传输哪些位、将它们发送到哪里以及如何使用它们的基本问题。 此外，还存在是在本地进行计算，还是将信息传递到更高层并执行部分集中计算的问题。 这些任务必须在资源稀缺的情况下完成，尤其是有限的时间、带宽和功率。需要突破的一个关键领域也是本项目的重点，涉及如何学习、适应和做出决策，以在复杂的分布式环境中实现传感器网络的目标。 实现整个网络的共同目标带来了巨大的信息处理挑战。 必须融合各个传感器收集的短视或局部信息以进行全局决策。 这些任务因大量传感器而变得复杂，每个传感器都可能是异构的、多模式的、可能是动态的且未经校准。 在某些应用中，场景传感器几何形状可能未知或仅部分已知，并且环境本身通常是复杂且动态的。将传感器网络中的学习、适应和决策与这些领域的大多数先前工作区分开来的关键要素是信息收集的分布式和局部性质以及信息的丰富多样的结构以及面对有限资源时要做出的决策。 在许多情况下，通过将所有数据发送到集中节点来退化地诉诸现有方法可能是不可能或不可行的，并且肯定不会是利用有限资源的最有效方法。 在这个项目中，我们解决传感器网络分布式学习的关键问题，以开始了解传感器网络学习和决策的基本功能和局限性。