Collaborative Research ITR:Energy-efficiency and reliability in dense sensor networks

密歇根州的SanDeep Sandanandarao U这项提案解决了使低功耗、无处不在的传感器网络成为现实所需的理论和算法信号处理机制中的一些重要组件。这些低功率、低成本传感器的物理和硬件属性以及计算和通信能力，特别是那些基于高密度MEMS器件的传感器，具有彻底改变下一代信息技术的潜力。事实上，下一代MEMS传感器预计将非常便宜，非常小(大约1毫米立方体)，通信范围为数百米，带宽为每秒数万到数万比特。挑战在于从这些低成本、无处不在的设备中构建一个无处不在的、可靠的、大规模分布的、动态自聚集的密集传感器网络系统。虽然硬件和计算基础设施遵循摩尔定律，但可悲的是，充分利用这一基础设施的潜力所需的理论和算法知识库方面的系统级专业知识落后了。这些网络带来的挑战远远超出了现有的理论和算法，在许多情况下，需要从集中式体系结构到分布式体系结构的基本范式转变。因此，这项提议的目的是开发实现这一梦想所需的信号处理和通信系统机制的一些重要组成部分。

Sandeep SandanandaraoU of MichiganThis proposal addresses some important components in the theoretical and algorithmic signal processing machinery needed to make low-power, ubiquitous sensor networks a reality. The physical and hardware attributes as well as the computing and communication capabilities of these low-power, low-cost sensors, particularly those based on high-density MEMS devices, have the potential to revolutionize next-generation information technology. Indeed, next-generation MEMs sensors are expected to be very cheap and very small (of the order of one millimeter cube) with a communication range of several hundred meters and a bandwidth of tens to hundreds of kilobits per second. The challenge is to build a pervasive, reliable, mas-sively distributed, dynamically self-conguring dense sensor network system out of these low-cost, ubiquitous devices. While the hardware and computational infrastructure are following Moore's law, the system-level expertise in terms of both theoretical and algorithmic knowledge-base needed to fully harness the potential of this infrastructure is sadly lagging behind. The challenges presented by these networks are far beyond existing theories and algorithms, and in many cases require a fundamental paradigm shift from centralized to distributed architectures. This proposal is accordingly motivated at developing some important components of the signal processing and communication system machinery needed to make this dream a reality.