CIF: Small: Ensuring robustness of communication between networked energy harvesting nodes

太阳能、环境射频能量和机械振动等为从环境中获取能量提供了机会。通常，将收获的部分能量存储在可充电的本地电池中以供将来使用是可行的。因此，预计能源收集设备将很快发挥重要作用，成为监测关键基础设施的网络组件，如交通网络和智能电网。这一发展与物联网的增长也是协同的，连接到网络的设备数量比我们今天拥有的多两个数量级。在关键基础设施技术背景下发展能源收集对国家利益至关重要，在物联网背景下发展能源收集可为社会带来深远利益。该项目的教育部分包括培养研究生，让本科生参与研究，并将研究成果转化为核心研究生和本科生课程。该项目的研究解决了在监控关键基础设施和物联网的背景下，能量收集传感网络带来的基本通信挑战。这项研究开发了系统的技术来设计感知能量收集节点的通信网络，该网络对能量收集过程中的变化具有健壮性。合适的模型取决于能量采集的时间尺度与被感知、估计和控制的物理系统所固有的时间尺度之间的关系。本项目同时研究信息论和分组丢弃模型，分别对应于相对于物理系统动态的能量收集的快或慢时间尺度。

Solar energy, ambient radio frequency energy, and mechanical vibrations, among others, provide opportunities for energy harvesting from the environment. Typically, it is feasible to store some of the harvested energy in a rechargeable local battery for future use. Energy harvesting devices are therefore expected to soon play a major role as components of networks monitoring critical infrastructure, such as transportation networks and the smart grid. This development is also synergistic with the growth of the Internet of Things, with the number of devices connected to the network being two orders of magnitude more than what we have today. The development of energy harvesting in the context of critical infrastructure technologies is of central importance to the national interest and its development in the context of the Internet of Things offers profound benefits to society. The educational component of this project includes training graduate students, exposing undergraduate students to research, and transitioning the results of the research to core graduate and undergraduate courses.The research in this project addresses the fundamental communications challenges posed by energy harvesting sensing networks in both the context of monitoring critical infrastructure and the Internet of Things. The research develops systematic techniques to design communication networks of sensing energy harvesting nodes that are robust to the variations in the energy harvesting process. The appropriate models to use depend on the relation between the time scale of the energy harvesting and the time scales inherent to the physical system being sensed, estimated, and controlled. This project studies both information theoretic and packet-drop models, corresponding respectively to fast or slow time scales for energy harvesting relative to the dynamics of the physical system.