iot use cases by derick jose - co-founder and chief product officer of m2m platforms at flutura
Post on 21-Oct-2014
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Talk by Derick Jose - Co-founder and Chief Product Officer of M2M platform at Flutura at The Hive Big Data Think Tank Internet of Things Meetup hosted at Intel IndiaTRANSCRIPT
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