Post on 30-May-2018
transcript
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Hang on for the Ride:
The Thrills and Spills ofSensornet Research
Phillip B. Gibbons
Intel Research P ittsburgh
November 5, 2008
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Outline
Musings on the Thrills & Spills of
sensornet research
Peak at our labs sensing relatedresearch
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How many conferences have published a paperwith sensor network in title?
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
302
Remarkableprogress
Open new windowson the world
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Many false starts
Many lessons learned
E.g., in SenSys08, see Barrenetxea et al.,
Big question: Whats next?
Is the thrill gone?
Sensornets now commercialized
What are the big open problems?
Sensornet Research: Spills?
The Hitchhiker's Guide to Successful
Wireless Sensor Network Deployments
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Where Do We Go From Here?
Expanding our sights
Field of ViewTime Horizon
Will talk about each in turn
WSNcore
Expanding scope
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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 hoclow-bandwidth wireless network
Singly taskednot so tiny, PDA-class processor
wide-area, not ad hoc
Microservers?
Webcams?
Fault-line monitoring?
not scalar, can be multi-tasked
Tanker/Fab
monitoring? powered, wired
Broadband? not low-bandwidth
Slide from IrisNet talks ~2005
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Sensor Networks is a Rich Space
Characteristics ofsensor networkdepend on
Requirements of the applicationRestrictions on the deployment
Characteristics ofsensed data
Sampling the real world
Tied to particular place and time
Not all data equally interestingCENS
NIMS
James Reserve
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Cameras, Mobile phones, etc
From the SenSys09 draft CFP:
SenSys takes a broad view of embeddednetworked sensor systems to include
any distributed systems that collectivelyinteract w ith the physical world
SenSys Scope has been Expanding
Note: No mention of low power, wireless, etc.
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But What are the Boundaries?
Sensing + Actuation + Mobility
Robotics?
Distributed Smart Cameras
Computer Vision?
Etc
Thrilling Opportunity ?orSelf-inflicted Identity Theft ?
Discussion topic among the SenSys Steering Committee
WSNcore
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Embracing the Broadening
E.g., More interaction w ith Robotics
SenSys workshop on Sensor-Robotic systems (?)
Tues lunch conversation
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Where Do We Go From Here? (2)
Impact of Sensor Network Commercialization
Academic research must be more forward looking,
to stay ahead of commercial offerings
Often, research goes beyondwhat can be demonstrated
on todays technology
Expanding our sights: Time horizons
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SenSys07Soap Box Talk
Key ingredients of a solid systems paper: Important problem Effective design: addresses core challenges, novel Solid evaluation: realistic, answers key questions,
fair comparison with previous work
A Tale of a Hypothetical SenSys Submission
(Challenges of Publishing More Forward-Looking Work,using Claytronics as a fictional example)
Beyond what can be demonstrated on todays
technology => Many aspects are open to dispute
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SenSys07Soap Box Talk
Key ingredients of a solid systems paper: Important problem Effective design: addresses core challenges, novel Solid evaluation: realistic, answers key questions,
fair comparison with previous work
A Tale of a Hypothetical SenSys Submission
(Challenges of Publishing More Forward-Looking Work,using Claytronics as a fictional example)
Beyond what can be demonstrated on todays
technology => Many aspects are open to dispute
Spills:
Authorsoften get itwrong
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A System Research Formula
I m ag in ea plausible future
Createan approximation of that vision
using technology that existsDiscover 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
[David Cullers SenSys07 Soap Box]
Bold, concise, revolutionary goalsto shoot for are invaluable
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Outline
Musings on the Thrills & Spills ofsensornet research
Peak at IRP s sensing related research
Everyday Sensing & Perception (ESP)Personal Robotics
SLIPstream
Hi-Spade: Flash
Claytronics
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Everyday Sensing & Perception
Build a context recognition system thatis 90% accurate over 90% of your day
EnvironmentalCoord. location
(lat,lon)Symbolic location in a car
Surroundings low crime
ActivityObject-based
drawingKinematic
running
High-level vacationing
SocialID: you and others nearbyType of interaction workCurrent role teacher
CognitiveEmotional angryGoal
finish taxes
Temporal rushing
Philipose et al, IR Seattle, IR Pittsburgh, etc
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ESP Application Structure
activity
object gesture
plantcare
point
plantfood
SVMobject SVMgesture
edge SIFT FFT energyFeature
extraction
Learning &inference
Interaction
Applications
Sensing
video
accelerometer
color
Activity from objects
Interactionplanning
Low attentioninterfaces
Adaptiveinterfaces Haptics
Life coachigital valet
Carry inference
Location from objects
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Digital Valet
Pedestrian navigationLocation-based securityFinding lost & hidden objects
Fitness trackingSmart scrap bookingVirtual tour guideHome automation
Context-aware interruptionsPre-destination/route prediction
Real time energy awarenessSmart appliancesEntertainment integration
In-situ recommender systemsPersonal health monitoringSmart shopping assistantSocial networking
Context-aware filteringHome security monitoring
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19
Achieve high quality perception
How can we get accuracy, variety, detail & coveragesimultaneously?
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 andoverhead?
Establish the value of high-volume context datato consumers
Which contexts matter most in everyday settings?
How will applications, interfaces and interaction techniques be
optimized to leverage context?
Research Problems
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Activity from Objects:Touching is Doing
Highly constrained object recognition problem
Pose, scale, clutter, occlusion
~75% recognition across 15 objs on real data
water
mustard
pepper
havinga meal
Egocentriccamera
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Personal Robotics
Short-range sensing & perception:Custom electric field sensors in fingers
Mid-range perception & manipulation:The robotic barkeep
Goal: Useful robotic assistants forindoor, populated environments
Srinivasa et al, IR Pittsburgh, IR Seattle, CMU
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SLIPstreamSLIPstream
Goal: Scalable Low -latency InteractivePerception on video Streams
Treat video & templates as spatio-temporal volumes Analyze using volumetric shape
&motion consistency features
Parallelized implementation on shared cluster
Gestris
Sukthankar et al, IR Pittsburgh, CMU
Natural gesture
user interfaces
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Outline
Musings on the Thrills & Spills ofsensornet research
Peak at IRP s sensing related research
ESPPersonal Robotics
SLIPstream
Hi-Spade: Flash
Claytronics
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Flash Superior to Magnetic Disk
on Many Metrics
Energy-efficient
Smaller
Higher throughput
Less cooling cost
Lighter
More durable
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NAND Flash Chip Properties
Block
(64
128
pages) Page
(512
2048
B)
Read/writepages,
eraseblocks
WritepageonceafterablockiserasedIn-place update
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NAND Flash Chip Properties
Block
(64
128
pages) Page
(512
2048
B)
Read/writepages,
eraseblocks
WritepageonceafterablockiserasedIn-place update1. Copy 2. Erase
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NAND Flash Chip Properties
Block
(64
128
pages) Page
(512
2048
B)
Read/writepages,
eraseblocks
WritepageonceafterablockiserasedIn-place update1. Copy 2. Erase 3. Write 4. Copy
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NAND Flash Chip Properties
Block
(64
128
pages) Page
(512
2048
B)
Read/writepages,
eraseblocks
Writepageonceafterablockiserased
Expensiveoperations:
Inplaceupdates
Randomwrites
In-place update
1. Copy 2. Erase 3. Write 4. Copy 5. Erase
Random
Sequential
0.4ms 0.6msRead
Random
Sequential
0.4ms
127ms
Write
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Hi-Spade
Goal for Flash: Algorithms that avoidrandom writes & in-place updates
Our main result:
A subclass of semi-random writesare both fast & useful in many algorithms
[Nath, Gibbons, VLDB08]
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Semi-random Access Pattern
Select pages w ithin a block sequentially
May jump around across blocks
1
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Semi-random Access Pattern
Select pages w ithin a block sequentially
May jump around across blocks
1 2
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Semi-random Access Pattern
Select pages w ithin a block sequentially
May jump around across blocks
1 23
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Semi-random Access Pattern
Select pages w ithin a block sequentially
May jump around across blocks
1 4 23
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Semi-random Access Pattern
Select pages w ithin a block sequentially
May jump around across blocks
1 4 6 2 53 7 8
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Existing Sampling Algorithms
Memory: Reservoir Sampling [Vitter85]
ith item
Reservoir R
Accept with
prob |R|/i
Disk: Geometric File [Jermaine04]NotFlashFriendly:
Randomwrites,
in
place
updates
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Existing Sampling Algorithms
Memory: Reservoir Sampling [Vitter85]
ith item
Reservoir R
Overwrite
random item
Accept with
prob |R|/i
Disk: Geometric File [Jermaine04]NotFlashFriendly:
Randomwrites,
in
place
updates
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Flash-friendly Sampling Algorithm
Level1 Level2 Level3 Level4 Level5
1. Assign randomlevels
to items and
put them in buckets
Storagelimit:25
Semirandom
writes,
No
in
place
updates
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Flash-friendly Sampling Algorithm
Level1 Level2 Level3 Level4 Level5
1. Assign randomlevels
to items and
put them in buckets
Storagelimit:25
Semirandom
writes,
No
in
place
updates
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Flash-friendly Sampling Algorithm
Level1 Level2 Level3 Level4 Level5
1. Assign randomlevels
to items and
put them in buckets
Storagelimit:25
Semirandom
writes,
No
in
place
updates
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Flash-friendly Sampling Algorithm
Level1 Level2 Level3 Level4 Level5
1. Assign randomlevels
to items and
put them in buckets
Storagelimit:25
Semirandom
writes,
No
in
place
updates
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Flash-friendly Sampling Algorithm
Level1 Level2 Level3 Level4 Level5
1. Assign randomlevels
to items and
put them in buckets
Storagelimit:25
Semirandom
writes,
No
in
place
updates
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Flash-friendly Sampling Algorithm
Level1 Level2 Level3 Level4 Level5
1. Assign randomlevels
to items and
put them in buckets
Storagelimit:25Storageisfull.
Semirandom
writes,
No
in
place
updates
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Level2 Level3 Level4 Level5
1. Assign randomlevels
to items and
put them in buckets
2. Drop the largest bucket if storage is full
Semirandom
writes,
No
in
place
updates
Flash-friendly Sampling Algorithm
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Level2 Level3 Level4 Level5
1. Assign randomlevels
to items and
put them in buckets
2. Drop the largest bucket if storage is full
Semirandom
writes,
No
in
place
updates
Flash-friendly Sampling Algorithm
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Level2 Level3 Level4 Level5
1. Assign randomlevels
to items and
put them in buckets
2. Drop the largest bucket if storage is full3. Ignore items assigned to discarded buckets
Semirandom
writes,
No
in
place
updates
Flash-friendly Sampling Algorithm
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B-File (Bucket-File)
Abstraction for storing self-expiring objectsAppendItem(item, bucket), DiscardBucket(bucket)
Fixed number of buckets
Buckets in block boundary
Small buckets as log
Small memory
E t M i t i S l
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Energy to Maintain Sample
Our algorithm
Our Algorithm
On Lexar CF card
E t M i t i S l
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Energy to Maintain Sample
Our algorithm
Our Algorithm
On Lexar CF card
3 orders ofmagnitude
better
Sub sampling within Time Window
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Sub-sampling w ithin Time Window
Query: Find a smaller random samplew ithin a specified time w indow
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
BucketBi
12 19 35 59 75 99 100 130 189
d S l
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Biased Sampling
Lemma: lw
gives an weighted sample
Lemma: le gives an exponentially decaying sample
Onlychange:thelevelgenerationfunction
Th S ill
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The Spill
Intel rolls outnew SSD last month
Hazards ofresearch on
fast-movingtechnology
Random Writes as Fast as
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Random Writes as Fast asSequential Writes!
Sequential Reads
0
0.05
0.1
0.15
0.2
0.25
512
1K
2K
4K
8K
16K
Request Size
time
(ms)
seq-read seq-write ran-read ran-write
Intel X25-M SSD
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Outline
Musings on the Thrills & Spills ofsensornet research
Peak at IRP s sensing related research
ESPPersonal Robotics
SLIPstream
Hi-Spade: Flash
Claytronics
The Claytronics Vision:
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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 i l l us ion of 3D (as w ith stereoglasses), but r eal ph ys ica l ob j ect s
Both an output device (rendering,
haptics) & an input device (sensing)
Applications
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Applications
Product design
Medical visualization
Adaptive form-factor devicesTelepario
3D faxSmart antennas
Paramedic-on-demand
Entertainment
Etc.
Claytronics
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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 & unreliabilityefficient actuators, strong adhesion
power, heat, dirt
complex, dynamic networking (network diameters
1000, and changing topologies)
Making Submillimeter Catoms
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patterned flower,including actuators& control circuitry
arms curl up
due to stressesbetween layers
Making Submillimeter Catoms
[J. Robert Reid,Air Force Research Labs]
[Igal Chertkow & Boaz Weinfeld,Intel]
2 mold wafers
bonded around1 thinned logic wafer
Note: Both areearly attempts
Catom Design
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Catom Design
Actuation: Roll across each other (usingelectrostatics) under software control
Planned motion, Reactive motion
Power: Form own power grid
Connected to external power source
Communication: Between physically
adjacent modulesEither electrical contact, capacitive-coupled
connections, or free space optics (wire-like)
Simultaneously with multiple neighbors
Aggregation Goal
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Aggregation Goal
In order to self-organize into a desiredshape, 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 standardhop-by-hop approach
Ensemble too dense forlonger range w ireless
Speculative Forwarding
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Speculative Forwarding[with Casey Helfrich, Todd Mowry, Babu Pillai,
Ben Rister, Srini Seshan]
Standard approach:(regular) gradient
E.g., regular 2D grid
Our approach:
Hierarchical Overlay Speculative forwarding
on the long links
Speculative Forwarding
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Speculative Forwarding
Each catom maintains incoming-to-outgoinglink mapping (e.g., last used)
Each bit along incoming w ire sent on outgoing
w ire according to the mapping
When accumulate header, check for miss-
speculation
Aggregation deferred to nodes in the overlay
Many issues:
miss-speculations creating overlay shape changes
Initial resultsare promising
Spills?
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Spills?
Beyond what can be demonstrated ontodays 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 with previous work
Spills?
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Spills?
Beyond what can be demonstrated ontodays 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 with previous work
Authors getit wrong?
Still a Thrill!
Sensornet Research
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Sensornet Research
What a thrill: exciting, impactful work
A peak at our labs current sensornet+ research
Expanding our scope & time horizonhelps maintain impact & thrill
Expect spills in research on fast-
moving or futuristic technologies
WSN
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