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Cyber-PhysicalSystems:Frontiers andMisconceptions
Tarek Abdelzaher
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Acknowledgements
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Acknowledgements
I am here because of Jack Stankovic
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Acknowledgements
I am here because of Jack Stankovic
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It started in 1990…
I got my undergrad degree…
and started looking for a Masters…
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1990
I got my undergrad degree…
and started looking for a Masters…
in automatic control
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1990
I got my undergrad degree…
and started looking for a Masters…
in automatic control
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1990
I got my undergrad degree…
and started looking for a Masters…
in automatic control
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1990
I got my undergrad degree…
and started looking for a Masters…
in automatic control
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1990
I got my undergrad degree…
and started looking for a Masters…
in automatic control
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1990
I got my undergrad degree…
and started looking for a Masters…
in automatic control
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1990
I got my undergrad degree…
and started looking for a Masters…
in automatic control
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1990
I got my undergrad degree…
and started looking for a Masters…
in automatic control
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How to do Research?
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Tree of Research
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Tree of Research
Misconception #1:To advance research,find a niche andextend a branch
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Tree of Research
Jack’s practice:Divert the trunk!Render the treeobsolete
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How to do Research?
Identify fundamental trends (those thathave a reason to exist… so far)
Predict inflexion points (points where atrend becomes unsustainable/obsolete)
Position yourself to capitalize on theaftermath (before others do)!!!
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Example: Moore’s Law
Moore’s Law (1980 – 2005):Exponential growth in the number oftransistors on a chip… until thermallimits were reached, heralding theemergence of massivelyparallel/distributed frameworks
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Example: Moore’s Law
Moore’s Law (1980 – 2005):Exponential growth in the number oftransistors on a chip… until thermallimits were reached, heralding theemergence of massivelyparallel/distributed frameworks
Fundamental thermallimits to scaling
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Example: Moore’s Law
Moore’s Law (1980 – 2005):Exponential growth in the number oftransistors on a chip… until thermallimits were reached, heralding theemergence of massivelyparallel/distributed frameworks
2004: Google introducesMap/Reduce (ApacheHadoop)
Fundamental thermallimits to scaling
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SensorSources
Databases
Industrialcargo, machineryfactory floor, …
Smart Spaces
Misconception #2:Future CPS Research is Driven byEmbedded Tech, Everywhere
RFIDs
Mobile Devices
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Manifestation:
The Internet of Things Source: Texas Instruments
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A Bigger Trend:
Democratized Broadcast
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Broadcast in the 20th century:
Democratized Broadcast
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Broadcast in the 20th century:
Broadcast today:
Democratized Broadcast
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O(n) O (n2)
O(n) O(n)
O(n) O(n2)
The Present
The Past
Democratized BroadcastFundamental Implications
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A Deluge of Information!!!
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Real-time datagrows much fasterthan our cognitive
ability to consume it
Slow Evolution!
Fast data growth!
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Real-time datagrows much fasterthan our cognitive
ability to consume it
Slow Evolution!
Fast data growth!Services to Bridge the Gap:
Autonomous/Cyber-physical InformationProcessing and Distillation Services
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Example: Smart CityData-to-Decision Pipelines
Sensors
Autonomic
Service Stack
Data
News and PublicSources
People
Events
DecisionSupport
Traffic jams
Disasters
Civil unrest
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Misconception #3
CPS is about cyber components meetingphysical/engineered artifacts
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33
MedicalInsulin pumps, pacemakers,
glucose monitors, …
Energy
Transportation
Sustainability
Disaster Recovery
It’s All AboutPeople
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Today’s Smart Sensor
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Today’s Smart SensorInterface to the Most Versatile Data Source
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An Analogy: Broadcast Media are“Materials” Propagating “Signals”
Physical targetResponse of physical propagation medium
(e.g., acoustic, vibration, optical, …)
Received signature (energy inmultiple signal frequency bands)
Physical event Response of social propagation medium(e.g., tweets)
Received signature (energy inmultiple keyword frequency bands)
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The Social Signal Layer
Keywords
Frequency countsEvent
Detectionand Tracking
Social MediumObservers ofPhysical Events
Social Sensing
Signal, Signal (k)Event MapEvents and Trajectories
Event Data(Tweets Associated
with the Event)
EventFlash mob
EventProtestEvent
Marathon
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Misconception #4
CPS is embedded systems on steroids…
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A Future CPS Skill:
Language-agnostic Processing ofUnstructured Text (Input to CPS)
A wealth of data will be generated globally byhuman sources (social media, blogs, articles,news, print, …)
How can a machine ingest such unstructureddata and convert it into a structured formatamenable to data fusion in CPS applications?
Text presents a particular challenge: manylanguages, rich semantics, special dialects, newjargon, grammatical mistakes, sarcasm, …
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Misconception #5
CPS is based on understanding andexploiting physical world/artifact models
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Conter-example:
Deep Learning (Tensor Flow)
41
Sigmoid: g(t) =1
1+ e-t
Can learn nonlinear functions providedeach perceptron has a differentiablenonlinearity
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The Power of Deep Learning
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Enable human-like interaction modalities with physical things
Speech Recognition
Vision/Object Detection
Context/Activity Recognition
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Now The Ugly Side…
Cyber-physical computing (and computingin general) is a victim of its own success!
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Rate of Innovation andDevelopment Time Issues
Near the turn of the 20th century productshad a 20-30 year life-span before new“versions” were developed
At present, a software product is obsoletein less than 2 years No time to discover and “debug” all possible
problems
New problems introduced in new versions
Component reuse generates additionalproblems
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Typical Software EngineeringPractice
Abstraction
Separation of concerns
Physical
Link
Network
Transport
Abstraction
Kernel
Virtualization
Separate virtual machines orprotection domains
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Abstraction Specialization
Complexity
More levels of abstraction
Narrower specialization
More details are “abstracted away”
Myopic view. Less knowledge of
possible adverse interactions
More potential for interaction
or incompatibility errors
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Abstraction Specialization
Complexity
More levels of abstraction
Narrower specialization
More details are “abstracted away”
Myopic view. Less knowledge of
possible adverse interactions
More potential for interaction
or incompatibility errors
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Challenge: Building ReliableSystems on Unreliable Components
The reliability problem will get worse
The classical research directions in CPSdesign and analysis that ensure correctnessof temporal and logical system behavior willremain an important component of theresearch roadmap!
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Conclusion
Cyber-physical systems are ubiquitous
Information distillation challenges will become moredominant
Research frontier: Tools for ingesting unstructured data and converting it into structured form
Tools for learning (take the human out of the loop)
Approaches for resilience: producing reliable outputs using unreliablecomponents
Goal: Elevate the human to progressively higher-level“supervisory” roles in CPS systems; bridge the gap betweena growing data volume and our cognitive limits to consumeit.