Intelligence at the Edge - events.static.linuxfound.org · c Intelligence at the Edge Embracing the Data Flood Andrzej Wieczorek IoT, Connectivity & Traffic, Tieto [email protected]

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Intelligence at the EdgeEmbracing the Data Flood

Andrzej Wieczorek

IoT, Connectivity & Traffic, Tieto

[email protected]

© Tieto Corporation

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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?

http://www.cisco.com/c/en/us/solutions/collateral/service-provider/global-cloud-index-gci/Cloud_Index_White_Paper.pdf


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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

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Thank you

Andrzej Wieczorek

IoT, Connectivity & Traffic, Tieto

[email protected]

Documents

Intelligence at the Edge - events.static.linuxfound.org · c Intelligence at the Edge Embracing the Data Flood Andrzej Wieczorek IoT, Connectivity & Traffic, Tieto [email protected]