Hang on for the Ride:
The Thrills and Spills of
Sensornet Research
Phillip B. Gibbons
Intel Research Pittsburgh
November 5, 2008
Slides (except those borrowed from colleagues) are © Phillip B. Gibbons
Outline
 Musings on the Thrills & Spills of
sensornet research
 Peak at our lab’s sensing related
research
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Phillip B. Gibbons, SenSys’08 keynote
Sensornet Research: Thrills!
 Many Thrills in Past Decade
– Exploded as a new, exciting, important area
– New playground, Intellectually challenging,
Hands on, Interdisciplinary
– Burst of new conferences; Papers in old conferences
Remarkable
progress
Open new windows
on the world
How many conferences have published a paper
with “sensor network” in title?
302
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Phillip B. Gibbons, SenSys’08 keynote
Sensornet Research: Spills?
 Many false starts
 Many lessons learned
– E.g., in SenSys’08, see Barrenetxea et al.,
The Hitchhiker's Guide to Successful
Wireless Sensor Network Deployments
 Big question: What’s next?
– Is the thrill gone?
– Sensornets now commercialized
– What are the big open problems?
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Phillip B. Gibbons, SenSys’08 keynote
Where Do We Go From Here?
 Expanding our sights
– Field of View
– Time Horizon
WSN
core
Expanding scope
Will talk about each in turn
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Phillip B. Gibbons, SenSys’08 keynote
What is a Sensor Network?
 Tiny sensor nodes with very limited processing power,
memory, battery. Scalar sensors (e.g., temperature)
 Closely co-located, communicating via an ad hoc
low-bandwidth wireless network
 Singly tasked
Microservers?
not so tiny, PDA-class processor
Fault-line monitoring?
Broadband?
not low-bandwidth
Webcams?
not scalar, can be multi-tasked
Tanker/Fab monitoring?
6
wide-area, not ad hoc
powered, wired
Slide
IrisNet
talks
~2005
Phillipfrom
B. Gibbons,
SenSys’08
keynote
Sensor Networks is a Rich Space
 Characteristics of sensor network
depend on
– Requirements of the application
– Restrictions on the deployment
 Characteristics of sensed data
– Sampling the real world
– Tied to particular place and time
– Not all data equally interesting
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Phillip B. Gibbons, SenSys’08 keynote
CENS’ NIMS
James Reserve
SenSys Scope has been Expanding
 Cameras, Mobile phones, etc
 From the SenSys’09 draft CFP:
SenSys takes a broad view of embedded
networked sensor systems to include
any distributed systems that collectively
interact with the physical world
Note: No mention of “low power”, “wireless”, etc.
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Phillip B. Gibbons, SenSys’08 keynote
But What are the Boundaries?
 Sensing + Actuation + Mobility
– Robotics?
 Distributed Smart Cameras
– Computer Vision?
WSN
core
 Etc
Thrilling Opportunity ?
or
Self-inflicted Identity Theft ?
Discussion topic among the SenSys Steering Committee
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Phillip B. Gibbons, SenSys’08 keynote
Embracing the Broadening
 E.g., More interaction with Robotics
– SenSys workshop on Sensor-Robotic systems (?)
Tues lunch conversation
10
Phillip B. Gibbons, SenSys’08 keynote
Where Do We Go From Here? (2)
 Expanding our sights: Time horizons
Impact of Sensor Network Commercialization
Academic research must be more forward looking,
to stay ahead of commercial offerings
Often, research goes beyond
what can be demonstrated
on today’s technology
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Phillip B. Gibbons, SenSys’08 keynote
SenSys’07
Soap Box Talk
“A Tale of a Hypothetical SenSys Submission”
(Challenges of Publishing More Forward-Looking Work,
using Claytronics as a fictional example)
Key ingredients of a solid systems paper:
• Important problem
• Effective design: addresses
Spills:core challenges, novel
• Solid evaluation: realistic,
answers key questions,
Authors
fair comparison
often get it with previous work
wrong
Beyond what can be demonstrated
on today’s
technology => Many aspects are open to dispute
12
Phillip B. Gibbons, SenSys’08 keynote
A System Research “Formula”
 Imagine a plausible future
 Create an approximation of that vision
using technology that exists
 Discover what is True in that world
– Empirical experience: Bashing your head, stubbing
your toe, rubbing your nose in it
– Quantitative measurement and analysis
– Analytics and Foundations
“Bold, concise, revolutionary goals
to shoot for are invaluable”
[David Culler’s SenSys’07 Soap Box]
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Phillip B. Gibbons, SenSys’08 keynote
Outline
 Musings on the Thrills & Spills of
sensornet research
 Peak at IRP’s sensing related research
–Everyday Sensing & Perception (ESP)
– Personal Robotics
– SLIPstream
– Hi-Spade: Flash
– Claytronics
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Phillip B. Gibbons, SenSys’08 keynote
Everyday Sensing & Perception
Build a context recognition system that
is 90% accurate over 90% of your day
Environmental
Coord. location
Symbolic location
Surroundings
Activity
(lat,lon)
Object-based
drawing
in a car
Kinematic
running
low crime
High-level
vacationin
Social
ID: you and others nearby
Type of interaction work
Current role
teacher
15
Cognitive
Emotional
Goal
Temporal
angry
finish taxes
rushing
SenSys’08
Philipose etPhillip
al, B.IRGibbons,
Seattle,
IR keynote
Pittsburgh, etc
ESP Application Structure
Applications
Interaction
Digital valet
Adaptive Low attention
interfaces
interfaces
plantcare
Learning &
inference
Interaction
planning
point
plantfood
object
edge
color
Location from objects
gesture
Carry inference
SVMgesture
SIFT
FFT
energy
video
Sensing
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Haptics
Activity from objects
activity
SVMobject
Feature
extraction
Life coach
accelerometer
Phillip B. Gibbons, SenSys’08 keynote
Digital Valet
Pedestrian navigation
Location-based security
Finding lost & hidden objects
Fitness tracking
Smart scrap booking
Virtual tour guide
Home automation
Context-aware interruptions
Pre-destination/route prediction
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Real time energy awareness
Smart appliances
Entertainment integration
In-situ recommender systems
Personal health monitoring
Smart shopping assistant
Social networking
Context-aware filtering
Home security monitoring
Phillip B. Gibbons, SenSys’08 keynote
Research Problems
• Achieve high quality perception
– How can we get accuracy, variety, detail & coverage
simultaneously?
– How do we retain acceptable performance?
• Lower the human cost of getting & using context
– How can we enable non-ML-PhDs to build context recognizers?
– How can we be minimally intrusive, both in privacy and
overhead?
• Establish the value of high-volume context data
to consumers
– Which contexts matter most in everyday settings?
– How will applications, interfaces and interaction techniques be
optimized to leverage context?
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Phillip B. Gibbons, SenSys’08 keynote
Activity from Objects:
Touching is Doing
Egocentric
camera
“water”
“mustard”
“having
a meal”
“pepper”
• Highly constrained object recognition problem
• Pose, scale, clutter, occlusion
• ~75% recognition across 15 objs on real data
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Phillip B. Gibbons, SenSys’08 keynote
Why Intel is Interested
Far more $$$ in services than silicon
Revenue above silicon for mobility, health
and home from users & advertisers (e.g. LBS
, assisted living, ‘who’s in front of the TV,
etc.)
Perception SW layer that plays well
with Multicore Graphics + Physics
layer
Key platform differentiator
Embedded sensors provide stickiness,
platform differentiation and Avg Selling Price
uplift
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Context-aware interfaces,
Services & device
adaptation
Fast, efficient
inference & perception
Platform integration
of sensors
Always-on/connected
mobile platforms
Context inference is key next-generation capability
Always-on
power efficiency
Positions
Intelwith
platforms
for next gen apps/services
Phillip B. Gibbons, SenSys’08 keynote
Personal Robotics
Goal: Useful robotic assistants for
indoor, populated environments
Short-range sensing & perception:
Custom electric field sensors in fingers
Mid-range perception & manipulation:
The robotic barkeep
21
Gibbons,
SenSys’08 keynote
Srinivasa etPhillip
al,B.IR
Pittsburgh,
IR Seattle, CMU
SLIPstream
Goal: Scalable Low-latency Interactive
Perception on video Streams
• Treat video & templates as spatio-temporal volumes
• Analyze using volumetric shape
& motion consistency features
• Parallelized implementation on shared cluster
Natural gesture
user interfaces
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Phillip B.et
Gibbons,
SenSys’08
keynote
Sukthankar
al, IR
Pittsburgh,
CMU
Gestris
Outline
 Musings on the Thrills & Spills of
sensornet research
 Peak at IRP’s sensing related research
– ESP
– Personal Robotics
– SLIPstream
–Hi-Spade: Flash
– Claytronics
23
Phillip B. Gibbons, SenSys’08 keynote
Flash Superior to Magnetic Disk
on Many Metrics
• Energy-efficient
• Smaller
• Lighter
• More durable
• Higher throughput
• Less cooling cost
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Phillip B. Gibbons, SenSys’08 keynote
NAND Flash Chip Properties
Block (64-128 pages)
Page (512-2048 B)
…
Read/write pages,
erase blocks
…
• Write page once after a block is erased
In-place update
1. Copy
2. Erase
3. Write
4. Copy
5. Erase
Random
0.4ms 0.6ms
Phillip B. Gibbons, SenSys’08 keynote
Read
Sequential
Random
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Sequential
• Expensive operations:
• In-place updates
• Random writes
0.4ms 127ms
Write
Hi-Spade
Goal for Flash: Algorithms that avoid
random writes & in-place updates
Our main result:
A subclass of “semi-random” writes
are both fast & useful in many algorithms
[Nath, Gibbons, VLDB’08]
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Phillip B. Gibbons, SenSys’08 keynote
Semi-random Access Pattern
 Select pages within a block sequentially
– May jump around across blocks
378
1 4 6 12
2 11
5 9 10
Random
0.4ms
0.5ms
Read
27
0.6ms
0.4ms
0.5ms
127ms
Write
Phillip B. Gibbons, SenSys’08 keynote
Applies to
flash chips,
flash cards,
and SSDs
Example Application
 Maintain on flash a large (several GBs),
bounded size random sample of a stream of
data items
Query
Local archiving of sensor data
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Phillip B. Gibbons, SenSys’08 keynote
Existing Sampling Algorithms
 Memory: Reservoir Sampling
Overwrite
random item
[Vitter’85]
Accept with
prob |R|/i
i’th item
Reservoir R
• Disk: Geometric File
[Jermaine’04]
Not Flash-Friendly:
Random writes, in-place updates
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Phillip B. Gibbons, SenSys’08 keynote
Flash-friendly Sampling Algorithm
1. Assign random
“levels” to items and
put them in buckets
Level 1
Level 2
Level 3
Level 4
Level 5
Storage is full ….
…
Storage limit: 25
Semi-random writes, No in-place updates
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Phillip B. Gibbons, SenSys’08 keynote
Flash-friendly Sampling Algorithm
1. Assign random
“levels” to items and
put them in buckets
2. Drop the largest bucket if storage is full
3. Ignore items assigned to discarded buckets
Level 1
Level 2
Level 3
Level 4
Level 5
…
Semi-random writes, No in-place updates
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Phillip B. Gibbons, SenSys’08 keynote
B-File (Bucket-File)
• Abstraction for storing self-expiring objects
AppendItem(item, bucket), DiscardBucket(bucket)
• Fixed number of buckets
• Buckets in block boundary
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• Small buckets as log
• Small memory
Phillip B. Gibbons, SenSys’08 keynote
Energy to Maintain Sample
Our algorithm
Our Algorithm
3 orders
of
magnitud
e better
33
Phillip B. Gibbons, SenSys’08 keynote
On Lexar CF card
Sub-sampling within Time Window
 Query: Find a smaller random sample
within a specified time window
12
19
35
Bucket Bi
59 75 99 100
130
189
 Observation: Each bucket is time sorted
– Use skip list to locate the first block in bucket
– Use binary search within a block to find the page
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Phillip B. Gibbons, SenSys’08 keynote
Biased Sampling
Only change: the level generation
function
Lemma: lw gives an weighted sample
Lemma: le gives an exponentially decaying sample
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Phillip B. Gibbons, SenSys’08 keynote
The Spill
Hazards of
research on
fast-moving
technology
Intel rolls out
new SSD last month
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Phillip B. Gibbons, SenSys’08 keynote
Random Writes as Fast as
Sequential Writes!
Sequential Reads
Intel X25-M SSD
0.25
time (ms)
0.2
0.15
0.1
0.05
Request Size
seq-read
37
seq-write
ran-read
Phillip B. Gibbons, SenSys’08 keynote
ran-write
16K
8K
4K
2K
1K
512
0
Outline
 Musings on the Thrills & Spills of
sensornet research
 Peak at IRP’s sensing related research
– ESP
– Personal Robotics
– SLIPstream
– Hi-Spade: Flash
–Claytronics
38
Phillip B. Gibbons, SenSys’08 keynote
The Claytronics Vision:
A Material That Changes Shape
 Large groups of tiny robot modules (106
-109 units), working in unison to form
tangible, moving 3D shapes
 Not just an illusion of 3D (as with stereo
glasses), but real physical objects
 Both an output device (rendering,
haptics) & an input device (sensing)
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Phillip B. Gibbons, SenSys’08 keynote
Applications
 Product design
 Medical visualization
 Adaptive form-factor devices
 Telepario
 3D fax
 Smart antennas
 Paramedic-on-demand
 Entertainment
 Etc.
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Phillip B. Gibbons, SenSys’08 keynote
Claytronics
[PIs: Seth Goldstein, Jason Campbell, Todd Mowry]
 Each sub-millimeter module (“catom”)
integrates computing & actuation
 Key issues:
– very high concurrency (106 -109 catoms)
– nondeterminism & unreliability
– efficient actuators, strong adhesion
– power, heat, dirt
– complex, dynamic networking (network diameters
≥ 1000, and changing topologies)
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Phillip B. Gibbons, SenSys’08 keynote
Making Submillimeter Catoms
patterned “flower”,
including actuators
& control circuitry
2 mold wafers
bonded around
1 thinned logic wafer
arms curl up
due to stresses
between layers
Note: Both are
early attempts
[J. Robert Reid,
Air Force Research Labs]
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[Igal Chertkow & Boaz Weinfeld,
Intel]
Phillip B. Gibbons, SenSys’08 keynote
Catom Design
 Actuation: Roll across each other (using
electrostatics) under software control
– Planned motion, Reactive motion
 Power: Form own power grid
– Connected to external power source
 Communication: Between physically
adjacent modules
– Either electrical contact, capacitive-coupled
connections, or free space optics (wire-like)
– Simultaneously with multiple neighbors
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Phillip B. Gibbons, SenSys’08 keynote
Aggregation Goal
 In order to self-organize into a desired
shape, the catom ensemble must:
– Be able to measure key aggregate properties
(e.g., center of mass)
– Coordinate their activities
…in real time
Diameter too large for standard
hop-by-hop approach
Ensemble too dense for
longer range wireless
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Phillip B. Gibbons, SenSys’08 keynote
Speculative Forwarding
[with Casey Helfrich, Todd Mowry, Babu Pillai,
Ben Rister, Srini Seshan]
E.g., regular 2D grid
Standard approach:
(regular) gradient
Our approach:
• Hierarchical Overlay
• Speculative forwarding
on the long links
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Phillip B. Gibbons, SenSys’08 keynote
Speculative Forwarding
 Each catom maintains incoming-to-outgoing
link mapping (e.g., last used)
 Each bit along incoming wire sent on outgoing
wire according to the mapping
 When accumulate header, check for missspeculation
Initial results
are promising
Many issues:
• miss-speculations
• creating overlay
• shape changes
46
Aggregation deferred to nodes in the overlay
Phillip B. Gibbons, SenSys’08 keynote
Spills?
 Beyond what can be demonstrated on
today’s technology =>
Many aspects are open to dispute
Key ingredients of a solid systems paper:
• Important problem
• Effective design: addresses core challenges, novel
• Solid evaluation: realistic, answers key questions,
fair
comparison
Authors
get with previous work
it wrong?
Still a Thrill!
47
Phillip B. Gibbons, SenSys’08 keynote
Sensornet Research
 What a thrill: exciting, impactful work
– A peak at our lab’s current sensornet+ research
 Expanding our scope & time horizon
helps maintain impact & thrill
WSN
 Expect spills in research on fastmoving or futuristic technologies
48
Phillip B. Gibbons, SenSys’08 keynote
Sensornet Research
Hang on for the Ride!
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Phillip B. Gibbons, SenSys’08 keynote