IOT Use Cases by Derick Jose - Co-founder and Chief Product Officer of M2M platforms at Flutura

IOT Use Cases Aug 2014

Intel Campus

Social Signal Mechanical signal

What e ll o er i e t 30 mins

1. 12 IOT Technical Design Patterns + Biz Use cases

2. 5 Key Learnings

3. 7 Emergent Transitions which play itself out

Landing gear becomes an addressable NODE !

Machines meet Software !

• The Ulti ate U io … Software + Ph si al i ter o e … walls are falling

• Value moving from Machines S/W

• Machines as a Service ( MAAS )

• Intelligence can live anywhere

• Edge

• Command centre

• Anywhere between machines controller and universal network level

Scenario-1 : Event sink

Massive sensor event ingestion

Flow sensors

Steam pressure

sensors

Temperature

sensors

Status sensors

Scenario-2 : Real time event Monitoring

States + Alarms

Scenario-3 : Vertical spe ifi Chai of thought a al sis

Chain of thought analysis

Scenario-4 : Ooops is my Asset down ?

Detecting Breakdowns !

Scenario-5 : Is it reall ad ? Tria gulati g false alarms

• A smoke alarm goes of on second floor

• It could be a smoker doing it in isolation

• GCC wants to verify it

• They look at the status of adjacent sensors on same floor on different floor

TRIANGULATE !

Scenario-6 : I a t to Dig deep i to a asset

Digging Deeper into Strategic asset sensors

Discovering buried cause and effects

Scenario-7 : I ha e a hu h – Hypothesis testing of domino effect

Scenario-8 : Whi h are the alar hotspots

Geospatial dimension

Scenario-9 : Adverse event Message forensics

E a i e u tou hed messages !

Scenario-10 : Looking for a needle in a haystack

Thin Slice !

X

1

2

3

4

Day-1 9:12 am

Fuse blowout

Day-6 3:15 pm

Line

Maintenance

Inspection

Day-9 10:15 pm

Transformer or

Eco system device

recalibration

Day-11 3:15 pm

Three 2 sigma Voltage

Surge Event occurred

Within 24 hours

Day-12 3:15 pm

Power Outage

Breaks down

X

X

X

X

X

X

X

X

X

X

X

Scenario-11 : Diagnosing Asset outcomes

Scenario-12 : Rank order Predictors of process or asset outcomes

So what are the key learnings ?

1. Bridging Engineering mental model Software mental model

ENGINEER DATA GUY

0

2000

4000

6000

8000

10000

12000

14000

16000

1 1 1 1 1 2 10 100 120 130 140 1000

Wri

te S

pe

ed

Number of threads

Write speed with the number of threads

Cassandra HBase

2. Stress test workloads

3.Beware of data blind spots for modelling Asset Outcomes

What are the 7 irreversible transitions have been set in motion ?

Transition-1 :

From Atoms to Bytes

Mech intensive Intelligence intensive

Transition-2 :

From Uncoupled to Tightly coupled IT-OT

Transition-3 :

From Fragmentation to Standardisation • Mind-sets + Interop /IOT Standards +

Wrappers

• Ma ufa turer s asi i sti t is to lock things down

FACEBOOK OF

MACHINES

Transition-4 :

From Isolated m/c to FB of m/c

Transition-5 :

From Traditional to Gamified command centre

Do 't desig a application, but alter a behaviour

Transition-6 :

From Pipe to Platform

Transition-7 :

From Data Streams to Revenue streams