Intelligence at the Edge - events.static.linuxfound.org · c Intelligence at the Edge Embracing the...
Transcript of Intelligence at the Edge - events.static.linuxfound.org · c Intelligence at the Edge Embracing the...
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Intelligence at the EdgeEmbracing the Data Flood
Andrzej Wieczorek
IoT, Connectivity & Traffic, Tieto
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IoT is about making decisions in the right place, at the right time and in time
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Network Proximity
At the Edge
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CloudEdge
gateways
Things /
Edge things
Backhaul
Core Network
Local
connectivity
Edge is local
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Fog computing (Cisco)
Many names similar concept
Cloudlet (Carnegie Mellon
University)
Micro datacenter (Microsoft)
Mobile Edge Computing (ETSI)
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CloudEdge
gateways
Edge things
Backhaul
Core Network
Local
connectivity
Getting beyond the Edge
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Behaviors detection
Ecosystems applications
Self learning
What else?
Pattern recognition
Access point
Programming
Calls
Edge evolving
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Example: Edge camera
Source: http://www.westjr.co.jp/press/article/2015/08/page_7489.html
Tokio metro, Kyobashi station
Suspicious behavior detection
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Data at the Edge
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Knowledge
Information
DataFacts, figures, signals
Structure, meaning, context, importance
Ability to use information to achieve objectives
Choosing objectives based on values
Wisdom
Are not important, knowledge is!
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http://www.juniperresearch.com/press/press-releases/iot-connected-devices-to-triple-to-38-bn-by-2020
http://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf
http://www.gartner.com/newsroom/id/3165317
Gartner
Cisco
Juniper Research
IoT is BIG
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In 2019 –> 507 ZB (507E+9 TB) data from IoT devices[source: Global Cloud Index: Forecast and Methodology, 2014–2019]
SSD 1TB 2.5’’32 millions km [~20 millions miles]
light ray would need 1 min 45 sec
How BIG?
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Metal bar manufacturing process
5 stages with very intense sampling
All machines storing data, single able to process it
Key here: data aggregation, filtering, anomaly
detection, feedback loop for self adjustment
Manufacturing example
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All machines connected to data
collector
• Connectivity
• Data processing engine
• Adapters
Manufacturing – connecting machines
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Additional input ERP etcData filtering
and aggregation Pattern and trends/
anomalies recognition
Manufacturing – handling data flow
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Limited communication capabilities (satellite)
Thousands of different containers to monitor
„Unstable” work conditions (weather, storm)
(Selected) data to analyze:
• Containers: inside and outside environment
• Ship: tilt/lean, vibration, ballast value
Cargo ship example
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• Do we need all these data?
• How much to store? For how long?
• Ways to optimize? (more than tar.gz)
• What is volume/ value relation?
Absolutely!
All!
What is „tar.gz”?
More is more!
No!
What for?
Hmmm…
Less is more!
To be continued
Data flooding the clouds
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…true, but most of data will never go beyond Edge
?
Data flooding clouds… really?
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Intelligent edge
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A smart machine will first consider which is more worth its while:
to perform the given task or, instead, to figure some way out of it.
Whichever is easier.
And why indeed should it behave otherwise, being truly intelligent?
For true intelligence demands choice, internal freedom
Stanislaw LEM „The Futurological Congress (1971)
Intelligence is a freedom of choice
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Knowledge
Information
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Data Information Knowledge
Data
Old school
Intelligent Edge
Building knowledge at the Edge
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• Proximity to the source of data
• Lower latency - speedup of decision making time
• Reduced global bandwidth
• Local context, local security
• Ownership of data, privacy protection
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Why Edge/ Fog computing
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Building knowledge (analytics, patterns,
trends, co-relations, rules)
Taking local decisions
Sharing knowledgewith clouds and with other edges
Evolving (feedback loop, new data, knowledge from
outside)
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Handling data at the Edge
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Learning / building knowledge
Goal: learn rather than follow instructions
• find patterns
• verify/ filter data
• predict or best-guess
• decide
• get feedback and adjust
Modeling learning: behavior trees, neural networks, genetic algorithms,…
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http://www.craft.ai/blog/NEST-like/
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Resources constraints
Type of problem (optimization,
pattern recognition)
Regression analysis
Adjustment possibility
Derivatives known?
Patterns known?
Amount of input data
Learning time
http://www.craft.ai/blog/NEST-like/
Selecting AI for the Edge
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Wrap up
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Data processing and analysis close to the source
Critical especially when privacy, time or transmission matters
AI key to handle, classify and analyze big flows (flood)
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Q&A