The document discusses emerging technology trends including wearable devices, iBeacons, contextual computing, virtual assistants, the Internet of Things (IoT), and how analytics can make sense of large amounts of data. Key points include: wearables collecting sensor data to gain user context; iBeacons using Bluetooth to provide micro-location awareness; virtual assistants integrating various technologies to anticipate user needs; and the IoT generating big data that analytics can analyze for patterns and insights. The trends are moving computing closer to users and making more information available through smarter use of data.

1. Internet of Things
And other interesting technology trends
Jim Boland
Software Architect
Cognos Analytics
IBM Canada Ltd
@neoslimjim

2. Trends in computing
• Wearable technology
• iBeacons, BLE and micro-location context
• Contextual and Anticipatory computing
• Virtual assistants
• IoT, big data and analytics
• Wrap up: how all this is about plugging us into the IoT!
Mainframe Desktop PC Laptop Mobile
(smartPhone/tablet)
Trends: Moore's law, computing distribution, personal/specialized, mobility
...

3. Wearables

4. vandrico.com/wearables
• Head: 63. 71
• Neck: 12 13
• Chest: 10 12
• Torso: 19 22
• Arm: 8 10
• Wrist: 119 160
• Hand: 5 7
• Fingers: 10 12
• Legs: 9 10
• Feet: 9 10
2014 2015

5. Wearable market size estimates varies greatly

6. Wearables
• Wearables complement (rather than replace) smartPhones
• Wearable technology is about the more seamless integration of tech:
• Notifications - SmartWatches, SmartGlasses
• Sensors - Fitness/health devices
• Remotes and Identification
• Virtual Assistants

7. SmartWatch & Glasses
• While much of the hype is with these, not the early
success...

8. Fitness/Health
• Fitness/health devices the early leader
in wearables
• Sensors to monitor heart rate, activity,
sleep, etc.
• Nike abandoned the Fuelband

9. Remotes
• Want an interface like Minority Report and Tony
Stark?
• Map physical gestures to intent, from:
• Fingers - Fin ring
• Arm - Myo ( Thalmic Labs)
• Wrist - Glance (kiwi wearables)

10. Identification
Wearables represent your identity for:
• Payment systems - Apple Watch and ApplePay
• Application/device - vivalnk tatoos
• Physical security - Kevo locks
• Contextual/anticipatory computing
As well, wearable biometric sensors create ways for establishing identity:
• Touch ID - Fingerprint scanners
• Nymi - heartbeat signature

11. Smart Shoes?
• Vibration in respective shoe to indicate right/left turn

12. Wearables ecosystem
Google and Apple platforms for wearable tech:
• Android wear
• Google Fit
• Apple HealthKit
• Apple WatchKit

13. And the most important area…

14. Power!
• The success of technologies to reduce the inconvenience of
having wearables charged may be the biggest single factor
affecting wearables adoption

15. Summary on wearables
• Still early days, with very mixed forecasts
• Apple and Google getting behind it
• Overall themes:
• Collect sensor data to gain context of the user
• Unobtrusive means to feed information back to the user
• Represent the user remotely (intent and identity)
• There are technical challenges still: security and power
• Bluetooth Low-Energy an enabler for power and communications

16. iBeacon
• Conceptually similar to a GPS satellite, but at a micro-location (and often indoors) scale
• In a store with a beacon in each department, the phone app knows where it is, by which
beacon(s) it sees
• iBeacons don't track/collect data from phones. Without an installed app, there is no interaction
with the phone/beacon
iBeacons
"I'm
beacon
#53"

17. iBeacon and BLE
• iBeacon is based on Bluetooth Low-Energy (BLE)
• Bluetooth 4.0 spec allows BLE and/or "classic"
• Characteristics difference of BLE (vs. "classic" Bluetooth)
• Simpler pairing
• Lower power consumption
• Lower data exchange
• iOS 5, Windows Phone 8.1, Android 4.3, BlackBerry 10
• An enabling technology for wearables, and IoT interconnectivity

18. Micro-location in apps
• Contextual retail assistance
• Indoor mapping/directions
• Home automation
• Tealeaf/Google Analytics style analytics of in-store foot traffic
• Micro-location based geo-fencing/context

19. iBeacon Competitors
• Apple doesn't produce iBeacons itself (yet*) - Radius, Estimote, Kontakt,...
• Other micro-location technologies:
• Google Eddystone - BLE
• Gimbal (formally Qualcomm) - BLE
• IBM Presence Insights - WiFi, BLE, others
• Philips Intelligent Lighting - VLC
• GE/ByteLight - VLC, BLE
• Indoor Atlas - Magnetic field variations
• Samsung Placedge - BLE (no app required)

20. Contextual Computing
Better understand the context of a user request, in terms of:
• Context from device sensors
• Context from user interaction history
• Context from user profile/social graph
Providing context of who, where, when and the previous
conversation

21. Siri example
Next request in context
of first

22. Affective Computing
• Special case: using context of a user, to understand
how people feel and emotions (and adapting
responses as a result)
• With the pervasive sensors of smartphones and
wearables, there is a new source of information

23. Anticipatory Computing
• Contextual computing: context is used to interpret a
user request
• Anticipatory computing: monitors context and
anticipates user's need without an explicit request

24. Google Now
• Uses context like your schedule, location, time of day, and past behaviors
• Eg. deliver the weather for the day and the morning traffic before you leave home.
• Notifies the user with “Cards,” when new information is available.

25. "Launch Here" iPhone
app
• "Anticipates" which tool you
need, when you take out your
iPhone
• Uses micro-location context
(iBeacons)
• Signul triggers events when you
enter “beacon zones”

26. "Invisible buttons"
• Amber Case [ESRI/Geoloqi] coined the term "invisible buttons"
• Concept: instead of touching a button on a smartphone as the
trigger for an action, the user becomes the trigger by entering a
geofenced area
• Ultimate example of the User Interface getting out of the way
• Example: my house! (Kevo lock, 21 iBeacons, Hue lights, airPlay
speakers)

27. Virtual Assistants
• Ties a lot of the previous ideas together
• Examples:
• Siri [Apple]
• Google Now [Google]
• Cortana [Microsoft]
• Nina Mobile [Nuance]
• Often based on voice technologies

28. Voice Interface
• Google: Voice search usage has more than double over
the previous year
• 55% of teens/43% of adults use voice search more than
once a day

29. IoT and Big Data
• IoT = devices sending and receiving data
• IoT = LOTS of devices, sending LOTS of data, ALL the time
• IoT = Big Data!
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30. Making sense of big data
• How do you get value from all that data?
• Analytics can help!

31. Data in motion
• Streaming analytics
• Scalable solutions to tame the firehose
• Latency sensitive response
• Realtime feedback
• Moving averages
• Identify out of range values
• Smooth out jittery sensor data

32. Data in motion: Connected car
• http://m2m.demos.ibm.com/connectedCar.html

33. Data at Rest
• Batch analytics
• Historical analysis
• Trends
• Outliers
• Correlations in the data
• Predictive analytics
• Stream analytics, batch analytics - both have a role in IoT!
Database

34. Data at rest:
SmartHouse
• http://www.ibm.com/extremeblue
• Fitbit for your house

35. • All the data from the sensors, combined with external data (e.g. Weather) creates a
rich big data source to mine for "interesting bits". Different external data sources can
be included to provide more context to do "smarter analysis"
• Running that data source through Analytics will identify the "findings"
• Trend analysis: Identify patterns over time, and identify outliers (Watson Analytics)
• Identify correlations between elements (IBM Catalyst)
• Predictive analytics(SPSS)
• Predicitve maintenance & Quality (IBM PMQ)
Big Data
Stream
Weather
Service
Analytics
Findings
Sensor data

36. Scenario 1: The night of the mouse

37. Scenario 2 - Rainy day people
• Today was a rainy day, and a set of
sensors on one exterior wall showed
increased humidity
• The system notices a trend of humidity
increases, on the last 10 days with over
15mm of rain. Moreover, the humidity
rate is trending up on each subsequent
rainy day
• Again, the system matches this sensor
pattern against the database and notifies
Mary of the potential of a leak.

38. Analytics in bluemix
• Trying analytics out!
• Freemium account:
https://console.ng.bluemix.net

39. Watson Analytics
• Cognitive computing - machines doing the analysis
• Freemium account:
www.ibm.com/analytics/watson-analytics/

40. Plugging us into the IoT!
Overarching themes:
• Bring computing closer to where it needs to be
• Smarter analytics of all this data improve the information
It's a fun time to be involved in technology!