Interconnecting CITRIS and the Physical World

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Interconnecting CITRIS and the Physical World

• David Culler
• Computer Science Division
• U.C. Berkeley
• www.cs.berkeley.edu/culler
• Intel Research
• Berkeley

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Breakthough Technology and Applns
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Bridging the Technology-Application Gap

• Power-aware, communication-centric node
  architecture
• Tiny Operating System for Range of
  Highly-Constrained Application-specific
  environments
• Network Architecture for vast, self-organized
  collections
• Programming Environments for aggregate
  applications in a noisy world
• Distributed Middleware Services (time, trigger,
  routing, allocation)
• Techniques for Fine-grain distributed control
• Demonstration Applications

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Critical issues

• Highly constrained devices
• power, storage, bandwidth, energy, visibility
• primitive I/O hierarchy
• Observation and action inherently distributed
• many small nodes coordinate and cooperate on
  overall task
• The structure of the SYSTEM changes
• Devices ARE the infrastructure
• ad hoc, self-organized network of sensors
• Highly dynamic
• passive vigilance most of the time
• concurrency-intensive bursts
• highly correlated behavior
• variation in connectivity over time
• failure is common

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The de facto platform for EmNets

• Developed a series of wireless sensor devices
• TinyOS concurrency framework
• Messaging Model
• Networking stacks (RF and Serial)
• Multihop routing
• Key components
• sensing, logging, data filters, broadcast
• Simulation tools
• Database Support

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Application Graph of Components
Route map
router
sensor appln
application
Active Messages
Radio Packet
Serial Packet
packet
Temp
photo
SW
Example ad hoc, multi-hop routing of photo
sensor readings
HW
UART
Radio byte
ADC
byte
3450 B code 226 B data
clocks
RFM
bit
Graph of cooperating state machines on shared
stack
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Many Research Groups using it

• UCB
• NEST
• SensorWeb
• Blackout
• Glaser structures
• CBE
• BFD
• BRWC
• UCLA
• USC, ISI
• Rutgers winlab
• Intel
• Bosch
• Crossbow

• U Wash
• Rutgers
• UIUC
• NCSA
• U Virginia, Notre Dame
• Ohio State, Wash. Univ.
• UCSD
• Dartmouth
• MIT
• UT Austin, ASU, Iowa
• Accenture
• Honeywell
• and many more (100)

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DARPA NEST Open Experimental Platform

• 1,000 Micas Delivered to NEST Feb 02
• 500 to UCB (Nest, Millennium), 250 to Intel
• Crossbow continues to manufacture
• new radio
• new microcontroller
• 14 Demos in July

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The MICA architecture

• Atmel ATMEGA103
• 4 Mhz 8-bit CPU
• 128KB Instruction Memory
• 4KB RAM
• 4 Mbit flash (AT45DB041B)
• SPI interface
• 1-4 uj/bit r/w
• RFM TR1000 radio
• 50 kb/s ASK
• Focused hardware acceleration
• Network programming
• Rich Expansion connector
• i2c, SPI, GIO, 1-wire
• Analog compare interrupts
• TinyOS tool chain
• sub microsecond RF synchronization primitive
• 10 mW active, 40 uW passive

51-Pin I/O Expansion Connector
8 Analog I/O
8 Programming Lines
Digital I/O
Atmega103 Microcontroller
DS2401 Unique ID
Coprocessor
Transmission Power Control
Hardware Accelerators
SPI Bus
TR 1000 Radio Transceiver
4Mbit External Flash
Power Regulation MAX1678 (3V)
2xAA form factor
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Rich Sensor board
Environment Ranging Detection Movement
Microphone
Sounder
Magnetometer
1.25 in
Temperature Sensor
Light Sensor
2.25 in
Accelerometer
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Discrete event simulation for large sensor
networks

• Re-implemensts hardware abstractions
• Individual rf modulation events, sensor events,
  clock events
• existing applications work
• Exploits TinyOS event driven structure
• host emulation down to HW abstraction
• redefine TOS macros and scheduler
• Allows debugging of distributed algorithms
• up to 1000s motes
• 100 in real time
• Variety of network models

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Controlled Test Bench

• Well-defined position
• Parallel access to nodes
• Integrated with MatLab

in situ programming Localization (RF,
TOF) Distributed Algorithms Distributed
Control Auto Calibration !!!!
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Tokachi Port, Hokkaido - Liquifaction
Virtual data logger High Confidence in Passive
state Dense Distributed Data Analysis Reclamation
Rapid, cheap, installation of vertical array
post-blast
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Getting Ready for the great Outdoors
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in situ habitat monitoring
Packaging Sensor Suite Longevity Power
Management Predictability Long-term Data
Analysis Remote Management delay-tolerant
network energy-based exp. design
Alan Mainwaring _at_ Intel Research
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with UCLA CENS
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Energy Monitoring/Mgmt Kits
Interactive Streaming Data Access Ubicomp /
HCI Networking Management Privacy / Security

• Cory Hall last summer
• Intel Smart Lab
• Cory Environment and asset tracking
• Etch. Smart-Alarm

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Distributed Query Processing over Low-Power
Sensor Network

• Focus Hierarchical Aggregation
• TinyDB Software On Motes

Hellerstein, Hong, Madden
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Intel Demonstrating the Technology

• Intel Research Impact
• Intel Sales and Marketing (Jan 2002)
• Intel Developers Forum (Feb 2002)
• 100 nodes in audience of 2000
• Network Discovery
• Power-aware routing
• In-Network aggregation
• Silly voting demo

Network in Marconi Center
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Meeting Social Network
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Smart Fire Helmet
Dick White

• CO sensor interfaced to MICA
• Intended to provide chemical sensing in helmet

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Cots Bots and nanobots (Pister)

• Fleet of 50 Low-cost Robots
• toy chassis mote stack
• Motor-Servo board interfaces any combination of
  two motors, servos, and solenoids to a toy car
  platform
• whisker board for obstacle detection
• digital accelerometer (ADXL202) board for crude
  odometry
• Rene gt Mica

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Self-propagating Programs?

• TinyOS components support class of applns.
• Tiny virtual machine adds layer of interpretation
  for specific coordination
• Primitives for sensing and communication
• Small capsules (24 bytes)
• Propagate themselves through network

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NEST Challenge Appln

• level field (400-2500 m2) with 5-15 tree-like
  obstacles
• Pursuers team
• 400-1000 nodes
• 3-5 ground pursuers,
• 1-2 aerial pursuers
• Evaders team
• 1-3 ground evaders
• Self organization of motes
• Localization of evaders
• Evaders position and velocity estimation by
  sensor network
• Communication of sensors estimates to ground
  pursuers
• Design of a pursuit strategy
• Minimize capture time and energy
• accuracy of localization synch
• stability of network and dist. alg

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Strategy Planner
Map Builder
Vehicle coordination layer
Pursuers communication infrastructure
Tactical Planner Regulation
Vehicle-level sensor fusion
Control Signals to pursuer
Single vehicle estimation and control layer
Nest Sensorweb
vision
GPS
Sensorial Information
Physical Platform
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Wealth of Research Challenges

• Large numbers of highly constrained (energy
  capability), connected devices
• able to be casually deployed in infrastructure
  (existing or in design)
• imperfect operation and reliability
• operating in aggregate
• New family of issues across all the layers
• Several viable testbeds and study applications

application
service
prog / data model
network
mgmt / diag / debug
algorithm / theory
system
architecture
technology
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The Nodes are the Infrastucture

• Simple Epidemic Algorithm Schema
• if (new mcast) then
• take local action
• retransmit modified request
• Examples Network wakeup, command propagation
• Build spanning tree
• record parent
• Naturally adapts to available connectivity
• Minimal state and protocol overhead
• gt surprising complexity in this simple mechanism

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Network Discovery
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Example epidemic tree formation
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Understanding Connectivity

• 16 transmit power settings
• For each transmit power setting, each node
  transmits 20 packets.
• Receivers log successfully received packets.
• Nodes transmit one after the other in a
  token-ring fashion
• No collisions.
• Define range radius where 75 of enclosed
  nodes receive 75 of packets
• Often good nodes at a distance

probability of reception from center node vs xmit
strength
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A First Generation PicoNode

• Flexible platform for experimentation on
  networking and protocol strategies
• Size 3x4x2
• Power dissipation lt 1 W (peak)
• Multiple radio modules Bluetooth, Proxim,
• Collection of sensor and monitor cards

Building sensor (light, sound, Temp, humidity)
Connectors for sensor boards
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BWRCs First Sub-Milliwatt Node
2 chip-set (overall silicon area 5mm2) In fab
by mid-fall
256 DATA
2 mm
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TinyOS on a chip

• TinyOS network stack in silicon
• synthesized from VHDL via BSAC National bus
• MICA on a chip
• open AVR core
• TinyOS stack
• 32 KB of memory
• 5 MM2
• Taped out May 20
• Due in July
• Contains Pister/Warneke ADC

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Practical Means of Energy Scavenging
Piezoelectric bi-morphs
PVDF
PZT
90 mW/cm3
Photovoltaic
10-1500 mW/cm2
Capacitive converter using MEMS micro-vibrator
30 mW/cm3 (on microwave oven)
K. Pister (UCB)
Shad Roundy (IML,UCB)
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Energy-Conscious Networking
Simulated Energy Dissipationin Sensor Networks
(BWRC)
Power Consumption
Performance-oriented
Energy-Conscious
Source R. Shah (UCB)
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Areas for Help

• Quality sensor subsystem design is surprisingly
  hard
• digital sensors, where art thou?
• so many interfaces
• calibration
• Application studies are more demanding than
  anyone allocates
• planning
• manpower
• programming, programming, programming
• maintenance
• adapter to thing X
• mechanical engineering
• Platform needs to spin off non-research support
  element

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Where to go for more?

• http//www.tinyos.net/tos/
• Jason Hill, Robert Szewczyk, Alec Woo, Seth
  Hollar, David Culler, Kristofer Pister. System
  architecture directions for network sensors.
  ASPLOS 2000.
• David E. Culler, Jason Hill, Philip Buonadonna,
  Robert Szewczyk, and Alec Woo. A Network-Centric
  Approach to Embedded Software for Tiny Devices.
  EMSOFT 2001.