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A Cloud-Based Bayesian Smart 
Agent Architecture 
for Internet-of-Things Applications 
Authors: Veselin Pizurica, Piet Van...
IoT early years (technology) view 
• IoT was about devices, protocols and data flows 
• “gateway centric” 
• “Liner logic”...
IoT today: business point of view 
• You see marketing departments taking over  
• Picture more fuzzy, devices and servic...
Connecting dots 
“Swarm Intelligence” 
Logic in a gateway 
“Fog” computing 
Logic in the cloud 
Conway's Game of Life, 
Na...
Why NOT intelligence in the cloud? 
• Latency 
• Failure (in)tolerance (lack of redundancy) – general issue 
in internet, ...
Why intelligence in the cloud? 
• Device-agnostic and decouples logic from the 
presentation layer 
• Combination of the s...
A Cloud-Based Smart Agent 
Artificial Intelligence provides us the framework and tools to 
go beyond trivial real-time dec...
Rational Agent 
Rational Agent Architecture * 
* Russell S., Norvig P.: Artificial Intelligence A Modern Approach, Third E...
Agent architecture choices 
• The choice for a particular type of agent logic is 
influenced by the characteristics of the...
Why Bayesian Networks in IOT? 
• Environments that cannot be completely observed, i.e. 
when not all aspects that could im...
Belief propagation 
• Belief propagation algorithm was introduced by Judea Pearl, 1982 
• Pearl was inspired by the paper ...
Example: Car diagnosis 
• Initial evidence: car won't start 
• Testable variables (green), “broken, so fix it” variables 
...
Let’s focus on battery->lights
Power of casual modelling 
Lights are on 
Lights are off
Compactness (and correctness) 
Decision trees 
Flow charts 
X Y Z
SW-defined 
Sensors 
Graph 
Modeling 
SW-defined 
Actuators 
Percepts 
Actions 
IoT platforms 
Physical Sensors 
Social me...
Example: waylay platform
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A Cloud-Based Bayesian Smart Agent Architecture for Internet-of-Things Applications

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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

Published in: Internet
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A Cloud-Based Bayesian Smart Agent Architecture for Internet-of-Things Applications

  1. 1. A Cloud-Based Bayesian Smart Agent Architecture for Internet-of-Things Applications Authors: Veselin Pizurica, Piet Vandaele @waylay Rome, 27/10/2014
  2. 2. IoT early years (technology) view • IoT was about devices, protocols and data flows • “gateway centric” • “Liner logic”: left devices, right services…
  3. 3. IoT today: business point of view • You see marketing departments taking over  • Picture more fuzzy, devices and services all over the place
  4. 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. 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. 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. 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. 8. Rational Agent Rational Agent Architecture * * Russell S., Norvig P.: Artificial Intelligence A Modern Approach, Third Edition, Pearson (2014)
  9. 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. 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. 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. 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. 13. Let’s focus on battery->lights
  14. 14. Power of casual modelling Lights are on Lights are off
  15. 15. Compactness (and correctness) Decision trees Flow charts X Y Z
  16. 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. 17. Example: waylay platform

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