Distributed Control Applications Within Sensor Networks
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Distributed Control Applications
Within Sensor NetworksBruno 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
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ContentsIntroduction
PEGs (pursuit-evasion game)
Implementation
Methodology
Conclusion
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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
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The goal of our research to design robust controllers for distributed systemsevaluation on a distributed control application testbeda 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 featuresgraceful performance degradation
SN can fail
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PEGs
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Distributed PEG (DPEG) scenario issuesTimeCommunicationLocationCooperationPowerSecurity
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Implementation Hardware
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NesC, TinyOS Time
two time management protocols global Network Time Protocol (NTP)-like synchronization protocol local time protocol with the means to transform time readings between i
ndividual 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
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Localization top-to-bottom localization framework
Coordinationapplication-specific grouping algorithmsgeneral-purpose grouping services
Power Security
OS level
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Indoorminiature car
remotely controlledSN
remotely controlled a pan-tilt-zoomcamera
to track the caruniform grid of 25motesdetects local magnetic fieldshared positioning information
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Outdoor
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Methodology Scalability and Distributed Control
Natureants searching for food, bacteria foraging, and flight
formations of some birdsschooling in fish & cooperation in insect societies
food search, predator avoidance, colony survival for the species
AIdistributed agentsfree market systemscontinuous control community
process control, distributed systems, jitter compensation, scheduling
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Models of Computation (MOC)Continuous time dynamical systems
stability and reachabilityfor distributed control applications in SNs
not able to capture communication delays, time skew between clocks or discrete de
cision making
discrete time dynamical systemsdoes not directly address sensing and actuation jittercan be taken into account by augmenting with time delay bet
ween the plant and the controllerhybrid automaton
continuous flow and discrete jumps
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discrete event systems work well for mode changes or task scheduling and characterizes hardw
are platform allow for system to be event-triggered not support continuous variables, not correlate time steps of the model w
ith 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 rea
ctive language no relation between time steps of the language and real time
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Design Approachesa hierarchical system representation
assume sensor
reading come with an accurate time stamp
sensors know their location in space
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Low-level controllertime based
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The proposed design methodology (high-level)event based
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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 m
ethodologies in distributed control systems on our proposed DPEG testbed
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