The First International Conference on Cognitive Internet of Things Technologies Talk: A Cloud-Based Bayesian Smart Agent Architecture for Internet-of-Things Applications Authors: Veselin Pizurica, Piet Vandaele Company: waylay Website: http://coiot.org/2014/show/program-final

1. A Cloud-Based Bayesian Smart
Agent Architecture
for Internet-of-Things Applications
Authors: Veselin Pizurica, Piet Vandaele @waylay
Rome, 27/10/2014

2. IoT early years (technology) view
• IoT was about devices, protocols and data flows
• “gateway centric”
• “Liner logic”: left devices, right services…

3. IoT today: business point of view
• You see marketing departments taking over 
• Picture more fuzzy, devices and services all over the
place

4. Connecting dots
“Swarm Intelligence”
Logic in a gateway
“Fog” computing
Logic in the cloud
Conway's Game of Life,
Nash gaming theory
TIT for TAT …

5. Why NOT intelligence in the cloud?
• Latency
• Failure (in)tolerance (lack of redundancy) – general issue
in internet, adding more blocks system even less stable
• Cost of pushing data in the cloud
– Energy (battery)
– Data storage (data can be of a huge volume)
– SW cost of integration
– Lack of standardization
• Security concerns: Authentication/Authorization
• Privacy concerns

6. 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 once
together with a REST API

7. A Cloud-Based Smart Agent
Artificial Intelligence provides us the framework and tools to
go beyond trivial real-time decision and automation use
cases for IoT.
In this presentation, we present a cloud-based smart agent
architecture for real-time decision taking in IoT applications
Sense
Transmit
Store
Analyze offline
Act
Reason Present

8. Rational Agent
Rational Agent Architecture *
* Russell S., Norvig P.: Artificial Intelligence A Modern Approach, Third Edition, Pearson (2014)

9. Agent architecture choices
• The choice for a particular type of agent logic is
influenced by the characteristics of the environment in
which an agent needs to operate
• Type of agents (using software language to express the
logic):
– ‘if-then-else’ constructs that are available in any programming
language or rules engine
– flowchart models
– CEP (complex event processing) engines
– Graph models (Markov, Bayesian nets)

10. Why Bayesian Networks in IOT?
• Environments that cannot be completely observed, i.e.
when not all aspects that could impact a choice of action
are observable.
• Unreliable, noisy or incomplete data or when domain
knowledge is incomplete such that probabilistic reasoning
is required
• Use cases where the number of causes for a particular
observation is so large, that it is nearly impossible to
enumerate them explicitly
• Well suited to model expert-knowledge together with
knowledge that is retrieved from accumulated data
• Use cases where there are asynchronous information flows

11. Belief propagation
• Belief propagation algorithm was introduced by Judea Pearl, 1982
• Pearl was inspired by the paper of cognitive psychologist Rumelhart on how
children comprehend text
• Generalization of the Kalman’s algorithm
• Became very popular after it was shown that the same computations are in
turbo codes and the same principles in the Viterbi algorithm
• Main idea: inference by local message passing among neighboring nodes
The message can loosely be interpreted as “I (node i ) think that you
(node j) are that much likely to be in a given state”.

12. Example: Car diagnosis
• Initial evidence: car won't start
• Testable variables (green), “broken, so fix it” variables
(orange)
• Hidden variables (gray) ensure sparse structure, reduce
parameters

13. Let’s focus on battery->lights

14. Power of casual modelling
Lights are on
Lights are off

15. Compactness (and correctness)
Decision trees
Flow charts
X Y Z

16. SW-defined
Sensors
Graph
Modeling
SW-defined
Actuators
Percepts
Actions
IoT platforms
Physical Sensors
Social media
Location
Open Data
Big Data
API economy
Cloud Smart
Agent Platform Environment
REST
API
LOB apps
Proposed architecture
Vertical
Specific
End-user
Interface

17. Example: waylay platform