ITR: Distributed Learning in Sensor Networks

部署大量联网传感器的可能性为许多商业、军事和国土安全应用提供了巨大的机会，但也带来了巨大的技术挑战。 为了充分利用这种网络的潜力，需要在若干方面和网络的所有层面取得进展。 然而，传感器网络除了解决无线通信网络中的大部分难题外，还带来了许多其他问题，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，连接性不会自动导致协调。在传感器网络中，存在由整个网络作为整体来实现的联合目的。 有一些基本的问题是关于传输哪些比特，将它们发送到哪里以及如何利用它们。 此外，还存在是否在本地进行计算的问题，或者将信息传递到更高层并执行部分集中式计算。 这些任务必须在有限的资源，特别是有限的时间、带宽和功率的情况下完成，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。 实现整个网络的联合目标带来了重大的信息处理挑战。 由单个传感器收集的近视或局部信息必须被融合用于全局决策。 这些任务由于大量的传感器而变得复杂，每个传感器可能是异构的、多模态的、可能是动态的和未校准的。 在某些应用中，场景传感器的几何形状可能是未知的或仅部分已知的，并且环境本身通常是复杂和动态的。区分传感器网络中的学习、适应和决策与这些领域中大多数先前工作的关键要素是信息收集的分布式和本地性质，以及面对有限资源要做出的信息和决策的丰富多样的结构。 在许多情况下，通过将所有数据发送到集中式节点来对现有方法的退化呼吁可能是不可能的或不可行的，并且肯定不会是利用有限资源的最有效方式。 在这个项目中，我们解决传感器网络中分布式学习的关键问题，开始发展的基本能力和传感器网络中的学习和决策的限制的理解。

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.