CAREER: Unified Probabilistic Localization for Sensor Networks: Theoretic and Practical Foundations

空间定位是一系列传感器网络任务的先决条件，如监控、跟踪、路由和安全服务。目前，用于向传感器节点提供关键定位信息的方法多种多样。虽然这些方法为创新提供了丰富的环境，但其多样化的策略带来了标准化和资源利用效率低下的问题。例如，许多技术需要独特的基础设施，在不同的定位技术中，在存在系统失真和噪声的情况下，人们对成本性能的权衡知之甚少。本研究解决了在各种传感器节点类型上构建可扩展的、统一的定位服务家族的需求。算法方法以贝叶斯网络为中心。这些网络允许系统地管理信号失真和噪声，并使当前传感器网络定位模式的频谱集成：信号强度与距离、到达角度、到达时差和指纹。这项研究将产生一种通用的定位基础设施，能够将位置信息集成到任何计算设备中，并提供新的基准，描述存在失真和噪声时定位性能的限制。这些发现将允许设计人员在成本限制的情况下选择最大化应用性能目标的定位模式，并将促进低成本传感器节点的普遍使用。通过在档案出版物和新课程中传播结果，该项目将通过为各种传感器网络提供可扩展的本地化服务来推进传感器应用的发展。

Spatial localization is a prerequisite for a range of sensor network tasks, such as monitoring, tracking, routing and security services. Currently, a variety of approaches are employed in providing critical positioning information to sensor nodes. Although these approaches provide a rich environment for innovation, their diversified strategies introduce problems of standardization and inefficient resource use. For example, many techniques require unique infrastructures, and among different localization technologies, little is understood about cost performance tradeoffs in the presence of systematic distortions and noise. This research addresses the need to construct a scalable, unified family of localization services over a variety of sensor node types. The algorithmic methodology centers on Bayesian networks. These networks allow for a systematic way to manage signal distortion and noise and enable the integration of the spectrum of current sensor network positioning modalities: signal strength to distance, angle of arrival, time difference of arrival, and fingerprinting. This research will result in a universal localization infrastructure capable of integrating location information into any computing device and provide new benchmarks that describe the limits of localization performance in the presence of distortion and noise.These findings will allow designers to select the localization modalities that maximize their application's performance goals given their cost constraints and will promote the ubiquitous use of low-cost sensor nodes. Through dissemination of the results in both archival publications and new curricula, this project will advance the development of sensor applications by enabling scalable localization services for a diverse range of sensor networks.