Distributed Control Applications Within Sensor Networks Bruno Sinopoli, Sourtney Sharp, Luca Schenato, Shawn Schaffery, S. Shankar Sastry Robotics and Intelligent Machines Laboratory / UC Berkeley Proceedings of the IEEE, VOL. 91, No.8, August 2003 Seo, Dongmahn

Contents Introduction PEGs (pursuit-evasion game) Implementation Methodology Conclusion December 1, 2005

Introduction Embedded computers Sensor Networks Crossbow, Millennial, Sensoria, Smart Dust various fields of research extensive experimentation of structural response to earthquakes habitat monitoring intelligent transportation systems home and building automation military applications research community time services, localization services, routing services, tracking services system design and implementations longevity, self configuration, self upgrade, adaptation to changing environmental conditions control applications location determination, time synchronization, reliable communication, power consumption management, cooperation and coordination, and security December 1, 2005

The goal of our research to design robust controllers for distributed systems evaluation on a distributed control application testbed a pursuit-evasion game (PEG) application research problems tracking, control design, security, robustness multiple-vehicle tracking distinguish pursuers from evaders dynamic routing structure to deliver information to pursuers in minimal time security features graceful performance degradation SN can fail December 1, 2005

PEGs December 1, 2005

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Distributed PEG (DPEG) scenario issues Time Communication Location Cooperation Power Security December 1, 2005

Implementation Hardware December 1, 2005

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NesC, TinyOS Time Communication two time management protocols global Network Time Protocol (NTP)-like synchronization protocol local time protocol with the means to transform time readings between individual motes Communication propose a general routing framework that supports a number of routing methodologies routing to geographic regions routing based on geographic direction routing to symbolic network identifiers for dynamically routing to physically moving destinations within the network December 1, 2005

Localization Coordination Power Security top-to-bottom localization framework Coordination application-specific grouping algorithms general-purpose grouping services Power Security OS level December 1, 2005

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Indoor miniature car SN remotely controlled remotely controlled a pan-tilt-zoom camera to track the car uniform grid of 25motes detects local magnetic field shared positioning information December 1, 2005

Outdoor December 1, 2005

Methodology Scalability and Distributed Control Nature AI ants searching for food, bacteria foraging, and flight formations of some birds schooling in fish & cooperation in insect societies food search, predator avoidance, colony survival for the species AI distributed agents free market systems continuous control community process control, distributed systems, jitter compensation, scheduling December 1, 2005

Models of Computation (MOC) Continuous time dynamical systems stability and reachability for distributed control applications in SNs not able to capture communication delays, time skew between clocks or discrete decision making discrete time dynamical systems does not directly address sensing and actuation jitter can be taken into account by augmenting with time delay between the plant and the controller hybrid automaton continuous flow and discrete jumps December 1, 2005

discrete event systems work well for mode changes or task scheduling and characterizes hardware platform allow for system to be event-triggered not support continuous variables, not correlate time steps of the model with real time dataflow MOCs useful for characterizing several communicating processes awkward for control synchronous reactive languages support a broad range of formal verification tools to aid in debugging possible to generate code for platform directly from the synchronous reactive language no relation between time steps of the language and real time December 1, 2005

Design Approaches a hierarchical system representation assume sensor reading come with an accurate time stamp sensors know their location in space December 1, 2005

Low-level controller time based December 1, 2005

The proposed design methodology (high-level) event based December 1, 2005

Conclusion overview of research activities dealing with distributed control in SNs SNs and related research issues hardware and software platforms SNs for distributed control applications suggested a general approach to control design using a hierarchical model composed of continuous time-triggered components at the low level discrete event-triggered components at the high level future work will focus on implementation, verification, and testing of our methodologies in distributed control systems on our proposed DPEG testbed December 1, 2005

Thank you! December 1, 2005