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 India

1. IOT Use Cases
Aug 2014
Intel Campus

2. Social Signal  Mechanical signal

3. What ǁe͛ll Đoǀer iŶ Ŷedžt 30 mins
1. 12 IOT Technical Design Patterns + Biz Use cases
2. 5 Key Learnings
3. 7 Emergent Transitions which play itself out

4. Landing gear becomes an addressable NODE !

5. Machines meet Software !
• The Ultiŵate UŶioŶ … Software + PhLJsiĐal iŶterǁoǀeŶ… 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

6. Scenario-1 :
Event sink
Massive sensor event ingestion
Flow sensors
Steam pressure
sensors
Temperature
sensors
Status sensors

7. Scenario-2 :
Real time event
Monitoring
States + Alarms

8. Scenario-3 : Vertical
speĐifiĐ ͞ChaiŶ of
thought͟ aŶalLJsis
Chain of thought analysis

9. Scenario-4 : ͞Ooops is
my Asset down ?͟
Detecting Breakdowns !

10. Scenario-5 : ͞Is it reallLJ ďad ?͟ TriaŶgulatiŶg
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 !

11. Scenario-6 :
͞I ǁaŶt to Dig deep iŶto aŶ asset͟
Digging Deeper into Strategic asset sensors

12. Discovering buried cause and effects
Scenario-7 : ͞I haǀe a
huŶĐh͟ – Hypothesis
testing of domino
effect

13. Scenario-8 : ͞WhiĐh are
the alarŵ hotspots͟
Geospatial dimension

14. Scenario-9 :
Adverse
event
Message
forensics
EdžaŵiŶe ͚uŶtouĐhed͛ messages !

15. Scenario-10 : Looking for a needle
in a haystack
Thin Slice !

16. X
X
4
X
X
X
X
3
X
X
X
2
X
X
1
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
Scenario-11 : Diagnosing Asset outcomes

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

18. So what are the key learnings ?

19. ENGINEER DATA GUY
1. Bridging Engineering mental model  Software mental model

20. 16000
14000
12000
10000
8000
6000
4000
2000
0
Write speed with the number of threads
1 1 1 1 1 2 10 100 120 130 140 1000
Write Speed
Number of threads
Cassandra HBase
2. Stress test workloads

21. 3.Beware of data blind spots for modelling Asset Outcomes

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

23. Transition-1 :
From Atoms to Bytes
Mech intensive  Intelligence intensive

24. Transition-2 :
From Uncoupled to Tightly coupled IT-OT

25. Transition-3 :
From Fragmentation to Standardisation
• Mind-sets + Interop /IOT Standards +
Wrappers
• MaŶufaĐturer͛s ďasiĐ iŶstiŶĐt is to lock things
down

26. Transition-4 :
From Isolated m/c to FB of m/c
FACEBOOK OF
MACHINES

27. Transition-5 :
From Traditional to Gamified command centre
͞DoŶ't desigŶ aŶ application,
but alter a behaviour͟

28. Transition-6 :
From Pipe to Platform

29. Transition-7 :
From Data Streams to Revenue streams