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

【摘要】加利福尼亚大学伯克利分校的kannan ramchandranan：最近传感器设备技术的巨大进步推动了当前围绕传感器网络的许多讨论。尽管值得注意的努力是建立基础设施和智能协议来将这些功率有限的设备联网，以便将其操作信封推向特定的应用程序，但对大规模健壮网络的系统性能限制的基本理解而不是增量理解仍然远未成熟。推动传感器网络的基本前沿是一项艰巨的系统理论挑战。这一重大挑战正是本研究的重点。本研究解决了比例定律和鲁棒性问题，但更重要的是，针对具体的设计指南和算法处方。简而言之，这项工作提供了急需的系统理论专业知识，使大规模鲁棒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，通道物理和传感器源模型从根本上支持传感器信号在整个网络中被获取、处理和分发，以进行决策和控制。因此，通道物理驱动缩放定律并渗透到所有功能任务中。渗透理论用于研究标度定律，并指导稳健网络连接的设计。采样理论，研究了在本地通信约束下传感器过采样密度和每个传感器a /D精度之间的鲁棒性与性能权衡的基本方法。最后，本研究利用混合系统理论研究了基于部分数据的分布式推理和鲁棒控制，从而实现了传感器网络的“闭环”。这项合作研究工作分为三个高度耦合的功能类别：(i)大规模传感器网络设计指南：通道物理和渗透理论。（ii）传感器现场表示和数据采集：分布式采样理论。（iii）通信约束下的分布式推理和鲁棒自适应控制。

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.