Programming Vast Networks of Tiny Devices

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Programming Vast Networks of Tiny Devices

• David Culler
• University of California, Berkeley
• Intel Research Berkeley

http//webs.cs.berkeley.edu
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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 (Phil Levis)
• 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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Mate Tiny Virtual Machine

• Comm. centric stack machine
• 7286 bytes code, 603 bytes RAM
• dynamicly typed
• Four context types
• send, receive, clock, subroutine (4)
• each 24 instructions
• Fit in a single TinyOS AM packet
• Installation is atomic
• Self-propagating
• Version information

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Case Study GDI

• Great Duck Island application
• Simple sense and send loop
• Runs every 8 seconds low duty cycle
• 19 Maté instructions, 8K binary code
• Energy tradeoff if you run GDI application for
  less than 6 days, Maté saves energy

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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 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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Motivation Sensor Nets and In-Network Query
Processing

• Many Sensor Network Applications are Data
  Oriented
• Queries Natural and Efficient Data Processing
  Mechanism
• Easy (unlike embedded C code)
• Enable optimizations through abstraction
• Aggregates Common Case
• E.g. Which rooms are in use?
• In-network processing a must
• Sensor networks power and bandwidth constrained
• Communication dominates power cost
• Not subject to Moores law!

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SQL Primer
SELECT AVG(light) FROM sensors WHERE sound lt
100 GROUP BY roomNo HAVING AVG(light) lt 50

• SQL is an established declarative language not
  wedded to it
• Some extensions clearly necessary, e.g. for
  sample rates
• We adopt a basic subset
• sensors relation (table) has
• One column for each reading-type, or attribute
• One row for each externalized value
• May represent an aggregation of several
  individual readings

SELECT aggn(attrn), attrs FROM
sensors WHERE selPreds GROUP BY
attrs HAVING havingPreds EPOCH DURATION s
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TinyDB Demo (Sam Madden)
Joe Hellerstein, Sam Madden, Wei Hong, Michael
Franklin
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Tiny Aggregation (TAG) Approach

• Push declarative queries into network
• Impose a hierarchical routing tree onto the
  network
• Divide time into epochs
• Every epoch, sensors evaluate query over local
  sensor data and data from children
• Aggregate local and child data
• Each node transmits just once per epoch
• Pipelined approach increases throughput
• Depending on aggregate function, various
  optimizations can be applied

hypothesis testing
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Aggregation Functions

• Standard SQL supports the basic 5
• MIN, MAX, SUM, AVERAGE, and COUNT
• We support any function conforming to

Aggnfmerge, finit, fevaluate Fmergelta1gt,lta2gt
? lta12gt finita0 ? lta0gt Fevaluatelta1gt ?
aggregate value (Merge associative, commutative!)
Partial Aggregate
Example Average AVGmerge ltS1, C1gt, ltS2, C2gt ?
lt S1 S2 , C1 C2gt AVGinitv ?
ltv,1gt AVGevaluateltS1, C1gt ? S1/C1
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Query Propagation

• TAG propagation agnostic
• Any algorithm that can
• Deliver the query to all sensors
• Provide all sensors with one or more duplicate
  free routes to some root
• simple flooding approach
• Query introduced at a root rebroadcast by all
  sensors until it reaches leaves
• Sensors pick parent and level when they hear
  query
• Reselect parent after k silent epochs

Query
1
P0, L1
2
3
P1, L2
P1, L2
4
P2, L3
6
P3, L3
5
P4, L4
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Illustration Pipelined Aggregation
SELECT COUNT() FROM sensors
Depth d
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Illustration Pipelined Aggregation
SELECT COUNT() FROM sensors
Epoch 1
1
Sensor
1 2 3 4 5
1 1 1 1 1 1
1
1
1
Epoch
1
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Illustration Pipelined Aggregation
SELECT COUNT() FROM sensors
Epoch 2
3
Sensor
1 2 3 4 5
1 1 1 1 1 1
2 3 1 2 2 1
1
2
2
Epoch
1
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Illustration Pipelined Aggregation
SELECT COUNT() FROM sensors
Epoch 3
4
Sensor
1 2 3 4 5
1 1 1 1 1 1
2 3 1 2 2 1
3 4 1 3 2 1
1
3
2
Epoch
1
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Illustration Pipelined Aggregation
SELECT COUNT() FROM sensors
Epoch 4
5
Sensor
1 2 3 4 5
1 1 1 1 1 1
2 3 1 2 2 1
3 4 1 3 2 1
4 5 1 3 2 1
1
3
2
Epoch
1
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Illustration Pipelined Aggregation
SELECT COUNT() FROM sensors
Epoch 5
5
Sensor
1 2 3 4 5
1 1 1 1 1 1
2 3 1 2 2 1
3 4 1 3 2 1
4 5 1 3 2 1
5 5 1 3 2 1
1
3
2
Epoch
1
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Discussion

• Result is a stream of values
• Ideal for monitoring scenarios
• One communication / node / epoch
• Symmetric power consumption, even at root
• New value on every epoch
• After d-1 epochs, complete aggregation
• Given a single loss, network will recover after
  at most d-1 epochs
• With time synchronization, nodes can sleep
  between epochs, except during small communication
  window
• Note Values from different epochs combined
• Can be fixed via small cache of past values at
  each node
• Cache size at most one reading per child x depth
  of tree

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2
3
4
5
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Testbench Matlab Integration

• Positioned mica array for controlled studies
• in situ programming
• Localization (RF, TOF)
• Distributed Algorithms
• Distributed Control
• Auto Calibration
• Out-of-band squid instrumentation NW
• Integrated with MatLab
• packets -gt matlab events
• data processing
• filtering control

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Acoustic Time-of-Flight Ranging
no calibration 76 error

• Sounder/Tone Detect Pair
• Emit Sounder pulse and RF message
• Receiver uses message to arm Tone Detector
• Key Challenges
• Noisy Environment
• Calibration
• On-mote Noise Filter
• Calibration fundamental to many cheap regime
• variations in tone frequency and amplitude,
  detector sensitivity
• Collect many pairs
• 4-parameter model for each pair
• T(A-gtB, x) OA OB (LA LB )x
• OA , LA in message, OB, LB local

joint calibration 10.1 error