Distributed Control Applications Within Sensor Networks

1. 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

2. Contents • Introduction • PEGs (pursuit-evasion game) • Implementation • Methodology • Conclusion

3. 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

4. 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

5. PEGs

7. Distributed PEG (DPEG) scenario issues • Time • Communication • Location • Cooperation • Power • Security

8. Implementation • Hardware

13. 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 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

14. Localization • top-to-bottom localization framework • Coordination • application-specific grouping algorithms • general-purpose grouping services • Power • Security • OS level

17. Indoor • miniature car • remotely controlled • SN • remotely controlled a pan-tilt-zoomcamera • to track the car • uniform grid of 25motes • detects local magnetic field • shared positioning information

18. Outdoor

19. Methodology • Scalability and Distributed Control • Nature • 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

20. 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

21. 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

22. Design Approaches a hierarchical system representation assume sensor reading come with an accurate time stamp sensors know their location in space

23. Low-level controller • time based

24. The proposed design methodology (high-level) • event based

25. 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

26. Thank you!