1) When IoT meets AI, devices are becoming widely available and connected due to reduced costs and improved connectivity and cloud services, driving interest in IoT. 2) API economies and big data analytics are major drivers of profit, connecting sensor data with cloud logic. Intelligence can be located in sensors, gateways, the cloud, or a combination. 3) The presenter argues that the standard IoT reference model is suboptimal and that a technology is needed to blend event-based and query-based data processing in real-time. A rule engine and Bayesian inference could help address challenges in combining physical and API-sourced data.

1. When IoT Meets Artificial
Intelligence
Veselin Pizurica
Internet of Things Event, 5th edition, 8/06/2016

2. Why so much interest in IoT?

3. Why now? - Perfect storm
● Cost of adding new connected sensors/actuators has come down dramatically
● Connectivity
● Cloud
● API economy
● Big Data/Analytics
● AI
● Robotics

4. Devices are becoming widely available
Off-the-shelf gadgets Programmable devices

5. API economy
● APIs have become new patents
● Who holds the data, holds the knowledge
● Companies don’t share their know-how, but they are willing to share their
know-what (via Application Programming Interface API)
● API economy will be the major driver of the profit for many companies

6. Weather API - monetization

7. Big data analytics

8. What connects these two pictures?

9. Intelligence - where?
“Swarm” intelligence Logic in the gateway
“Fog” computing
Logic in the cloud
Logic in the device

10. Swarm Intelligence - in sensor networks?
● Limited storage, power and processing power
● Sensors and actuators local

11. Fog computing
● Anomaly detection
● Compress sensing (not for computing, but bandwidth optimization, as data
leaves the edge)
● Fast reaction time
● No privacy issues if data doesn’t leave the edge
● Doesn’t work for LoRA and Sigfox, as data deduplication happens in the cloud
● Mostly in factory settings - transition from SCADA (legacy) systems to more
internet oriented solutions

12. Why NOT intelligence in the cloud?
● Latency requirements
● Failure (in)tolerance (lack of redundancy) – adding more blocks system even
less stable
● Cost of pushing data in the cloud (storage, bandwidth)
● SW cost of integration
● Lack of standardization
● Security concerns: Authentication/Authorization
● Privacy concerns

13. Why intelligence in the cloud?
● Device-agnostic and decouples logic from the presentation layer
● Combination of the sensor data with API “economy”
● Integrating multiple IoT vertical solutions
● Cloud-capacity scales horizontally, while distributed HW often needs to be
swapped when HW resources are no longer sufficient
● Cloud intelligence also allows easy generation of analytics regarding the
usage of the logic itself. Which rules fired and why? How often?
● An architectural model arises where logic is built together with a REST API

14. Our vision

15. IoT reference model is suboptimal
Critical in IoT is the ability to process data in real-time as they come in, i.e.the
ability to act on data in motion.
We need a technology that effortlessly can blend event-based and query based
data in real-time, not one or the other!

16. So let’s talk about AI!
Y = f (X) Y = f (X)

17. How do we evolve to a programmable world?
Rule engine is a
knowledge modeling problem
Y = f (X)

18. IoT/API Rule Engine Challenges
● Changes of the (customers’) environment and requirements.
● Lack of compact representation, leading to difficult simulation, debugging and
maintenance.
● Rule engines don’t provide us with easy ways to gain additional insights: why
a rule has fired and under which conditions?
● Combining data from the physical world (PUSH mode) with data from the “API
world” (PULL mode).
● How long do we wait for the next information to come before deciding to move
on in decisions?
● How long is the measurement is valid?

19. Bayes and inference engine to the rescue!

20. Waylay Rule Engine is a Cloud Smart Agent

21. Waylay platform