gibbons sensys08 keynote

8/14/2019 Gibbons SenSys08 Keynote

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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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Phillip B. Gibbons, SenSys08 keynote2

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



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



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.



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



Embracing the Broadening

E.g., More interaction w ith Robotics

SenSys workshop on Sensor-Robotic systems (?)

Tues lunch conversation



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



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



SenSys07Soap Box Talk

Key ingredients of a solid systems paper: Important problem Effective design: addresses core challenges, novel Solid evaluation: realistic, answers key questions,


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



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



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



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



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



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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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



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



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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Block

(64

128

pages) Page

(512

2048

B)

Read/writepages,

eraseblocks

WritepageonceafterablockiserasedIn-place update1. Copy 2. Erase


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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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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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1 2


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1 23


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1 4 23


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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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to items and

put them in buckets

Storagelimit:25

Semirandom

writes,

No

in

place

updates


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to items and

put them in buckets

Storagelimit:25

Semirandom

writes,

No

in

place

updates


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to items and

put them in buckets

Storagelimit:25

Semirandom

writes,

No

in

place

updates


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to items and

put them in buckets

Storagelimit:25

Semirandom

writes,

No

in

place

updates


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to items and

put them in buckets

Storagelimit:25Storageisfull.

Semirandom

writes,

No

in

place

updates


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Level2 Level3 Level4 Level5


to items and

put them in buckets

2. Drop the largest bucket if storage is full

Semirandom

writes,

No

in

place

updates



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to items and

put them in buckets

2. Drop the largest bucket if storage is full

Semirandom

writes,

No

in

place

updates



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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



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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



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



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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,


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,


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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gibbons sensys08 keynote

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