CPS: Small: Sensor Lattices

该项目利用新引入的传感器网格的概念，对可以感知世界的“粒度”以及这如何影响利用网络物理系统完成共同任务的能力进行了系统研究。 传感器被视为一种设备，它将物理世界的状态划分为测量不变的等价类，传感器格表示所有传感器之间的关系。 所追求的方法的几个显著特征是：1）开发虚拟传感器模型，其对应于常见任务的最小信息需求并且独立于特定物理传感器实现。 2)不确定性被解耦为扰动和原像，后者产生测量不变等价类和传感器格。 3)基于任务的最小信息需求的特定空间和时间过滤器的开发。4)正式建立条件，使传感器在CPS互换，然后确定相对的复杂性权衡。智力的优点是理解从物理世界的传感器输出的映射如何影响常见的任务的可解性和复杂性。 这是数学和计算CPS基础发展的关键一步。 通过改进CPS解决社会问题的设计方法，如辅助生活，环境监测和自动化农业，预计会产生更广泛的影响。 传感器网格方法是变革性的，因为它代表了一种新的范式，可以解决基本的基于传感器的推理问题，这些问题远远超出了传统的学术界限。

Using the newly introduced idea of a sensor lattice, this project conducts a systematic study of the ``granularity'' at which the world can be sensed and how that affects the ability to accomplish common tasks with cyberphysical systems (CPSs). A sensor is viewed as a device that partitions the physical world states into measurement-invariant equivalence classes, and the sensor lattice indicates how all sensors are related. Several distinctive characteristics of the pursued approach are: 1) Virtual sensor models are developed, which correspond to minimal information requirements of common tasks and are independent of particular physical sensor implementations. 2) Uncertainty is decoupled into disturbances and preimages, the latter of which yields the measurement-invariant equivalence classes and sensor lattice. 3) The development of particular spatial and temporal filters that are based on minimal information requirements of a task. 4) Formally establishing the conditions that enable sensors in a CPS to be interchanged, and then determining the relative complexity tradeoffs.The intellectual merit is to understand how mappings from the physical world to sensor outputs affect the solvability and complexity of commonly occurring tasks. This is a critical step in the development of mathematical and computational CPS foundations. Broader impact is expected by improving design methodologies for CPS solutions to societal problems such as assisted living, environmental monitoring, and automated agriculture. The sensor lattice approach is transformative because it represents a new paradigm with which to address basic sensor-based inference issues, which extend well beyond the traditional academic boundaries.