SST: Reliable, Accurate, and Long Term Environmental Monitoring Using Networks of Sensors

传感器提供了计算和物理领域之间的基本联系。尽管传感器和测量设备在科学早期就已经存在，但小型化、集成化、无线技术和计算技术的进步正在为开发非常大规模的传感设备网络铺平道路，这些网络悄悄地嵌入到广泛的环境中。在这一新出现的范例下，可靠性、准确性和长期自主运行成为实际系统设计和使用中的重要因素。拟议活动的智力优点：传感系统的目标通常是检测、观察和了解关于环境的事情，涉及诸如信号检测、估计、分类或其他数据分析操作等任务。这些任务对联网的传感器系统尤其具有挑战性，原因如下：首先，根据需要在任意的时间点和空间点收集数据是困难的。测量过程的受限性质可能会对推理方案的可实现精度造成严重影响。其次，由于传感器网络系统的分散性，校准传感器网络系统是非常困难的。第三，由于采样和校准的限制，传感器网络数据分析任务可能是不合适的。第四，电力、带宽和计算设备等关键资源往往非常有限。这严重限制了传感器网络中的通信量和计算量。在大规模传感器网络的背景下，检测、估计、分类、校准和通信各自独立地提出了每一个具有挑战性的新问题。此外，作为提出的四个问题的结果，分布式传感、处理和通信从根本上是相互关联的。我们对这一具有挑战性的新领域的方法依赖于研究以下关键原则：分布式算法、复杂性管理、感觉反馈以及感知、处理和通信的无缝集成。网络内数据处理和通信的分布式算法消除了将原始传感器数据传输到中心点的需要。它们可以显著减少通信量和获得准确估计所需的能量。此外，分布式算法在存在通信错误、网络中断和组件降级的情况下更加健壮和可靠。复杂性规则化为处理模型复杂性/灵活性和对有限数据的过度依赖之间的贸易提供了理论和实践方法。传感器反馈允许动态资源分配，从而在电力、带宽和数据处理器等宝贵资源的使用方面实现潜在的显著收益。传感、处理和通信操作的集成对于使用传感器网络进行可靠、准确和长期的环境监测至关重要。本项目将为实际传感器网络系统开发一套新的理论和一套算法。这些算法将进行理论分析和实验测试。试验台将由光、声、热和其他源和传感器组成，使用符合IEEE 802.15.4标准的节点联网。这些传感器的组合将使人们能够深入探索复杂问题，包括时空环境变化的特征、多模式传感以及传感、数据处理和通信的集成。广泛的影响：该项目将为培训研究生和本科生提供一个独特的平台，使他们在设计网络化传感器系统时从实验和理论角度面临各种挑战。拟议研究的主题是威斯康星大学麦迪逊分校电气和计算机工程系教授的一门新的研究生水平课程的组成部分，该课程涉及网络嵌入式系统(欧洲经委会902)。此外，将通过威斯康星大学现有的各种项目鼓励少数族裔和妇女参与该项目。目前，三名女研究生正由其中一名PI指导。该项目还将促进与当地行业的合作，这些行业表达了强烈的兴趣，并致力于提供建立实验试验台所需的硬件和软件组件。

Sensors provide the fundamental link between the computation and physical domains. Although sensorsand measuring devices have existed since the early days of science, miniaturization, integration, wirelesstechnologies, and advances in computing are paving the way for developments of very large scalenetworks of sensing devices, embedded unobtrusively in a wide array of environments. Under this newemerging paradigm, reliability, accuracy, and long term autonomous operation become vital factors inthe design and use of practical systems.The intellectual merit of the proposed activity: The goal of a sensing system is often todetect, observe, and learn things about the environment, involving tasks such as signal detection,estimation, classi.cation, or other data analysis operations. These tasks are especially challenging innetworked sensor systems for the following reasons: First, it is di.cult to collect data at arbitrarypoints in time and space as needed. The restricted nature of the measurement process can place severelimitations on the achievable accuracy of inference schemes. Second, calibrating sensor network systemsis extremely di.cult due to their decentralized nature. Third, sensor network data analysis tasks can beill-posed because of the limitations on sampling and calibration. Fourth, key resources such as power,bandwidth, and computing devices are often very limited. This places severe restrictions on the amountof communication and computation that can take place in a sensor network.Detection, estimation, classi.cation, calibration, and communication, each, in their own right, posevery challenging new problems in the context of large-scale sensor networks. Moreover, as a consequenceof the four issues raised, distributed sensing, processing, and communication are fundamentally interconnected. Our approach to this challenging new domain rests on investigating the following keyprinciples: distributed algorithms, complexity management, sensory feedback, and the seamless integrationof sensing, processing, and communications. Distributed Algorithms for in-network dataprocessing and communications eliminate the need to transmit raw sensor data to a central point.They can provide signi.cant reductions in the amount of communication and energy required to obtainan accurate estimate. Furthermore, distributed algorithms are much more robust and reliablein the presence of communication errors, network outages, and component degradation. ComplexityRegularization provides theory and practical methodologies for coping with trade-o.s betweenmodel complexity/.exibility and over.tting to limited data. Sensor Feedback allows for dynamicresource allocation, enabling potentially dramatic gains in the use of precious resources such as power,bandwidth, and data processors. Integration of sensing, processing and communication operations iscritical for reliable, accurate, and long-term environmental monitoring using networks of sensors.This project will develop a new theory and a suite of algorithms for practical sensor network systems.The algorithms will be analyzed theoretically and tested experimentally. The testbed will consist ofoptic, acoustic, thermal and other sources and sensors, networked using IEEE 802.15.4 compliant nodes.The combination of these sensors will enable in depth exploration of complex issues including thecharacterization of spatio-temporal environmental variation, multimodal sensing, and the integration ofsensing, data processing, and communication.Broader Impact: This project will provide a unique platform for training graduate and undergraduatestudents by exposing them to a broad variety of challenges in designing networked sensor systems from both experimental and theoretical perspectives. The topics of the proposed research are an integral part of a new graduate level course o.ering by the PI at the University of Wisconsin Madison Electrical and Computer Engineering Department dealing with networked embedded systems (ECE 902). Furthermore, involvement of minorities and women in the project will be encouraged via various existing programs at the University of Wisconsin. Currently three female graduate students are being supervised by one of the PI's. This project will also facilitate collaborations with local industry who have expressed strong interest and committed to furnishing the hardware and software components necessary in build the experimental testbed.