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

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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.