Smart Dust and TinyOS: Hardware and Software for Network Sensors - the software part

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Smart Dust and TinyOS Hardware and Software for
Network Sensors- the software part

• David E. Culler
• Kris Pister
• University of California, Berkeley
• Intel Research Berkeley

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A New Computer Class Emerging
log (people per computer)
year
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CMOS Trends miniaturization and
more
nearly a thousand 8086s fit in a modern
microproc.
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New Role for Computing
Number Crunching Data Storage
log (people per computer)
productivity interactive
streaming information to/from physical world
year
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Example Habitat Monitoring
gt 1000 ft
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Current State of the Art
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Sensor Network Solution
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Typical Applications

• nodes gtgt people
• sensor/actuator data stream
• unattended
• inaccessible
• prolonged deployment
• energy constrained
• operate in aggregate
• in-network processing necessary
• what they do changes over time
• gt must be programmed in situ

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the Technology-Application Gap
mgmt / diag / debug
algorithm / theory
technology
MEMS
Power
Comm.
uRobots
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Open Experimental Platform to Catalyze a Community
Services
Networking
TinyOS
Rene 00
Dot 01
Mica 02
WeC 99
Small microcontroller - 8 kb code, 512 B data
Simple, low-power radio - 10 kb EEPROM
storage (32 KB) Simple sensors
Demonstrate scale

• Designed for experimentation
• sensor boards
• power boards

NEST open exp. platform 128 KB code, 4 KB data 50
KB radio 512 KB Flash comm accelerators
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An Operating System for Tiny Devices embedded in
the Physical World
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Traditional Systems

• Well established layers of abstractions
• Strict boundaries
• Ample resources
• Independent Applications at endpoints communicate
  pt-pt through routers
• Well attended

Application
Application
User
System
Network Stack
Transport
Threads
Network
Address Space
Data Link
Files
Physical Layer
Drivers
Routers
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by comparison ...

• Highly Constrained resources
• processing, storage, bandwidth, power
• Applications spread over many small nodes
• self-organizing Collectives
• highly integrated with environment and network
• communication is fundamental
• Concurrency intensive in bursts
• streams of sensor data and network traffic
• Robust
• inaccessible, critical operation
• Unclear where the boundaries belong

• gt Provide a framework for
• Resource-constrained concurrency
• Defining boundaries
• Appln-specific processing
• allow abstractions to emerge

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Tiny OS Concepts

• Scheduler Graph of Components
• constrained two-level scheduling model threads
  events
• Component
• Commands,
• Event Handlers
• Frame (storage)
• Tasks (concurrency)
• Constrained Storage Model
• frame per component, shared stack, no heap
• Very lean multithreading
• Efficient Layering

Events
Commands
send_msg(addr, type, data)
power(mode)
init
Messaging Component
Internal State
internal thread
TX_packet(buf)
Power(mode)
TX_packet_done (success)
init
RX_packet_done (buffer)
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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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Example TinyOS study

• UAV drops 10 nodes along road,
• hot-water pipe insulation for package
• Nodes self-configure into linear network
• Synchronize (to 1/32 s)
• Calibrate magnetometers
• Each detects passing vehicle
• Share filtered sensor data with 5 neighbors
• Each calculates estimated direction velocity
• Share results
• As plane passes by,
• joins network
• upload as much of missing dataset as possible
  from each node when in range
• 7.5 KB of code!
• While servicing the radio in SW every 50 us!

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Vast Networks of Tiny Devices

• Past 25 years of internet technology built up
  around powerful dedicated devices that are
  carefully configured and very stable
• local high-power wireless subnets at the edges
• 1-1 communication between named computers
• Here, ...
• every little node is potentially a router
• work together in application specific ways
• collections of data defined by attributes
• connectivity is highly variable
• must self-organize to manage topology, routing,
  etc
• and for power savings, radios may be off 99 of
  the time

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Directed Diffusion Concept

• Nodes express interest in data with certain
  attributes (sinks)
• Establishes gradient from sources

Estrin, Govindan, Heideman
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Directed Diffusion Concept

• Nodes express interest in data with certain
  attributes (sinks)
• Establishes gradient from sources
• Sources generate data
• Useful paths reinforced, others suppressed
• in-network aggregation
• nested queries

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Challenges

• Probabilistic Connectivity
• Contention creates interference
• Mobility
• Discovery
• Flooding does not scale
• Mechanism for reinforcement and suppression
• Naming, operators

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Common Special Case Data Gather

• Collection of nodes take periodic samples
• Stream data towards a root node
• Root announces interest
• depth 0
• Nodes listen to neighbors
• When hear neighbor with smaller depth
• start transmitting data to good neighbor with
  smallest depth
• set own depth to one greater and include with
  data
• Data transmission continuously reinforces /
  adjusts routes

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Network Discovery Radio Cells
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Network Discovery
0
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Surge Demo
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Local Operations gt Global Behavior

• Packets directed to a parent neighbor
• all other neighbors hear too
• carry additional organizational information
• Each nodes builds estimate of neighborhood
• adjusted with every packet and with time
• Interactively selects parent
• Routes traffic upward
• Collectively they build and maintain a stable
  spanning tree
• takes energy to maintain structure

node depth child? parent? link goodness
17 1 yes 90 .7
6 3 yes 75 .6
...


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Programming Challenges

• thousands of constrained nodes,
• interacting in real-time with physical world,
• where you cannot touch them,
• and what you want them to do changes with time...
• How do you program the network?
• How do you specify what you want it to do?

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Programmable network fabric

• Architectural approach
• new code image pushed through the network as
  packets
• assembled and verified in local flash
• second watch-dog processor reprograms main
  controller
• Viral code approach
• each node runs a tiny virtual machine interpreter
• captures the high-level behavior of application
  domain as individual instructions
• packets are capsule sequence of high-level
  instructions
• capsules can forward capsules
• Rich challenges
• security
• energy trade-offs
• DOS

pushc 1 Light is sensor 1 sense Push
light reading pushm Push message
buffer clear Clear message buffer add
Append val to buffer send Send message
using AHR forw Forward capsule halt
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Higher-level Programming?

• Ideally, would specify the desired global
  behavior
• Compilers would translate this into local
  operations
• High-Performance Fortran (HPF) analog
• program is sequence of parallel operations on
  large matrices
• each of the matrices are spread over many
  processors on a parallel machine
• compiler translates from global view to local
  view
• local operations message passing
• highly structured and regular
• We need a much richer suite of operations on
  unstructured aggregates on irregular, changing
  networks

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Sensor Databases a start

• Relational databases rich queries described by
  declarative queries over tables of data
• select, join, count, sum, ...
• user dictates what should be computed
• query optimizer determines how
• assumes data is presented in complete, tabular
  form
• First step database operations over streams of
  data
• incremental query processing
• Big step process the query in the sensor net
• query processing content-based routing?
• energy savings, bandwidth, reliability

SELECT AVG(light) GROUP BY roomNo
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TinyDB Demo
Joe Hellerstein, Sam Madden, Wei Hong, Michael
Franklin
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New Architectures?
Embedded Network Arch.
Typical Wireless Arch. (cellphone)
audio
kbd / display
Sensor / Actuators
Codec
Application Controller
CoProc
Multi-Purpose Controller
DSP
protocol accelerators
Protocol Processor
RF Transceiver
RF Transceiver

• Traditional approach is to partition design into
  specialized subsystems with rigid interfaces.
• TinyOS allows low and high-level processing to be
  interleaved.
• rich physical information can be exposed
• specialized hardware to accelerate primitives
• Enables cross-layer optimizations

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Example monitoring and alarm

• Monitoring
• sample every 4 seconds, aggregate over 5 minutes,
  transmit statistical summary
• 20,000 samples, 300 reports per day per node
• aggregate statistics up the routing tree
• schedule rendezvous, so radio mostly off
• Alarm
• upon detection of dramatic environmental change
• routes alarm through parent at any time
• Where the energy goes
• sleeping
• sensing processing
• communication
• listening for communication to start
• listening for an alarm message

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Cross-Layer optimization

• Sensing Processing
• 15 mw 17 mJ per day
• Sleeping
• 45 uw 5038 mJ per day
• Communination
• hardware accelerators for edge capture and
  serialization
• 10 kbps gt 50 kbps 2262 gt 452 mJ/day 5x
• Rendezvous 2x time-synchronization
• time-stamp packets - 100 ms
• radio bit edge detection - 2 us
• radio-level timesynch 669 gt 33 mj/day 20x
• Wake-up
• packet listen 108 ms (21 ms) 54,000 gt 25
  mj/day 2000x
• sample radio channel for energy 50 us
• Combined 2AA lifetime grows from 1 year to 9
  years
• dominated by sleep energy

receiver-based alternative (Elison)
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A rich growing research agenda

• Localization
• High-fidelity Time-Synch
• Collaborative Processing
• Multi-layered storage hierarchy
• Simulation environments
• Distributed Algorithms

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CENS
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Tiny Stuff
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Example Distributed Control

• Sensor field quietly monitors
• net env. data for health monitoring
• off-line geographic localization
• Event detected locally by small group
• Local broadcast of processed data
• Group leader elected to aggregate
• time sensitive
• Multihop geographic transport to well-define dest
• base-station for processing response
• mobile pursuer node
• Additional time-sensitive cooperative
  localization data for pursuer navigation and
  planning

Hill, Whitehouse, et al.