SENSORS: Towards a system theory for the robust design of large-scale

当前围绕传感器网络的许多讨论都是由传感器设备技术的最新进展所推动的。 尽管值得注意的努力，以建立基础设施和聪明的协议，以网络这些功率有限的设备，以推动他们的操作信封为特定的应用程序，一个基本的，而不是一个渐进的理解大规模的强大的网络的系统性能限制仍然远远不够成熟。 推动传感器网络的基本前沿是一个艰巨的系统理论挑战。 正是这一重大挑战是本研究的重点。 这项研究解决了缩放定律和鲁棒性问题，但更重要的是，旨在具体的设计准则和算法处方。 简而言之，这一努力提供了急需的系统理论专业知识，使大规模的强大的传感器网络成为现实。 总体主题是（i）大规模，（ii）传感器网络的鲁棒性。 传感器网络的独特之处在于，信道物理和传感器源模型从根本上支持传感器信号的采集、处理和分布，以进行决策和控制。 相应地，通道物理驱动缩放定律，并通过所有功能任务进行缩放。逾渗理论用于研究缩放律，并指示鲁棒网络连接的设计。 采样理论，研究了一个基本的方法来解决鲁棒性与性能之间的权衡传感器过采样密度和每个传感器的A/D精度在本地通信约束下。 最后，本研究通过研究基于部分数据的分布式推理和鲁棒控制，利用混合系统理论，围绕传感器网络“闭合回路”。 这种合作研究工作被组织成三个高度耦合的功能类别：（i）大规模传感器网络设计准则：信道物理和渗流理论。 (ii)传感器场表示和数据采集：分布采样理论。 (iii)通信约束下的分布式推理与鲁棒自适应控制。

ABSTRACT0330514Kannan RamchandranU of Cal BerkeleyMuch of the current buzz around sensor networks has been driven by dramatic recent advances in sensor device technologies. Despite noteworthy efforts to build infrastructure and clever protocols to network these power-limited devices in order to push their operational envelopes for specific applications, a fundamental rather than an incremental understanding of the system performance limits of large-scale robust networks remains far from mature. It is a daunting system theory challenge to push the fundamental frontiers of sensor networks. It is this grand challenge that is the focus of this research. This study addresses scaling laws and robustness issues, but more importantly, aims at concrete design guidelines and algorithmic prescriptions. In short, this effort provides the much-needed systems theory expertise to make large-scale robust sensor networks a reality. The overarching themes are (i) large scale, and (ii) robustness of sensor networks. Sensor networks are unique in that channel physics and sensor source models fundamentally underpin the sensor signals being acquired, processed, and distributed across the network for decision-making and control. Accordingly, channel physicsdrives scaling laws and percolates through all functional tasks. Percolation theory is used to study scaling laws and dictate designs for robust network connectivity. Sampling theory, is studied in a fundamental way to to address robustness versus performance tradeoffs between sensor oversampling density and per-sensor A/D precision under local communication constraints. Finally, this research ``closes the loop'' around sensor networks by studying distributed inference and robust control based on partial data using hybrid systems theory. This collaborative research effort is organized into three highly coupled functional categories: (i) Large-scale sensor network design guidelines: channel physics and percolation theory. (ii) Sensor field representation and data acquisition: distributed sampling theory. (iii) Distributed inference under communication constraints and robust adaptive control.