Internet of ThingsAnd other interesting technology trends

Jim BolandSoftware Architect

Cognos AnalyticsIBM Canada Ltd

@neoslimjimhttps://www.slideshare.net/JimBoland3/hacking-health-iot-analytics-and-other-trends

Trends in computing

• Wearable technology

• iBeacons and micro-location context

• Contextual and anticipatory computing

• 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 ...

Wearables

vandrico.com/wearables

• Head: 63. 84

• Neck: 12 12• Chest: 10

21• Torso: 19

27• Arm: 8 11• Wrist: 119

213• Hand: 5 8• Fingers: 10

11• Legs: 9

13• Feet: 9

12

2014 2017

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

SmartWatch & Glasses

• While much of the hype is with these, not the early success...

Fitness/Health

• Fitness/health devices the early leader in wearables

• Sensors to monitor heart rate, activity, sleep, etc.

• Nike abandoned the Fuelband

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)

IdentificationWearables 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

Smart Shoes?

• Vibration in respective shoe to indicate right/left turn

Summary on wearables

• 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 DYI prototyping kits out there (e.g. MbientLab’s Metawear)

• There are technical challenges still: security and power

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"

Micro-location

• Emergency ward patient tracking

• Contextual retail assistance

• Indoor mapping/directions

• Home automation

• Tealeaf/Google Analytics style analytics of in-store foot traffic

• Micro-location based geo-fencing/context

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

Siri example

Next request in context of first

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

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

"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)

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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Making sense of big data

• How do you get value from all that data?• Analytics can help!

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

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

Example IoT Architecture

Local Hub/IoT

Gateway

Device

Sensor

Sensor

Device

Bluemix

- Streaming Analytics- Batch Analytics- Other business logic

MQTTBLE

WIFI

ZigBee

Analytics in bluemix

• Trying analytics out!• Freemium account:

https://console.ng.bluemix.net

Watson Analytics

• Cognitive computing - machines doing the analysis

• Freemium account:

www.ibm.com/analytics/watson-analytics/

Node-Red

• https://nodered.org/ • Run locally (e.g. Rpi) or cloud (e.g Bluemix)