From Internet of Things Mashups to Model-based Development

1. fortiss GmbH
An-­Institut Technische Universität München
From Internet of Things Mashups to Model-­based
Development
Christian Prehofer & Luca Chiarabini

2. Motivation
• Goals of this presentation
– Review and compare mashup and model-­based concepts
– Propose combination both approaches
• IoT Mashup tools: Visual Service Composition and Programming
– E.g. paraimpu, IBM node red, …
• Mashups connect sensors, actuators and cloud services
– Simple, visual programming

3. IBM Node Red Work Mashup Tool Example
• Workplace models nodes and connections

4. IBM Node Red Work Mashup Tool (cont.)
• Nodes can be
– Functions
– Data input, output, debuxg
– Services (here MongoDB Query)

5. Paraimpu Mashup Tool
• Example: Read sensor data and alert user via Twitter

6. Recap Mashups
• Mashups tools mainly specify data flow & UI
• Mashup tools can specify behaviorvia
– Pre-­defined blocks for specific computation, e.g. and/or operators
– Open blocks, where program code is inserted (mainly Javascript)
• Mashup tools integrate data flow, configuration and deployment
• Fast execution from data flow model
– Partly simulation / emulation

7. Model-­based Approaches for System Development
• Classic Example is UML
– Many other domain-­specific languages
• Many different views on a system
– Architecture
– Behavior
– Deployment
– ...
• Widely use: State Machines
– Very natural for control of actuators
• Very sophisticated concepts and tools
– Considerable tool investment needed
– Considerable learning effort needed
Off On
switchOn
switchOff
Lightcontroller

8. What is in a Mashup Model? The UML view
• Componentdiagram
• Activity diagram
• Behavior, e.g. via state machine
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Sensor data Tweet
Sensors
Arduino
Arduino Controller
Sensors
Sensor data Sensor data
dioneWaterLevel < 5
5 < dioneWaterLevel <=15
@alberserra –
WARING! Your
dione needs water
NOW!
@alberserra –
WARNING! Your
dione water level is
too low.
Water Level
Distinction
[X<5] WL(X) /
Twitter(„WARNING Your dione needs water NOW!“)
[5 < X <= 15] WL(X) /
Twitter(„WARNING Your dione water Level is too low.“)

9. Modeling Concepts for IoT
• State Machines
– Express naturally states of a (physical) system
• Example: Light controller with two lights
– Not easy to do in mashup tool – no hierarche, not multi-­threading
Off On
switchOn1
switchOff2
Lightcontroller1
Off On
switchOn2
switchOff2
Lightcontroller2
Exit

10. Verification Example for two Light Controllers
• Sample properties for automated verification (by model checker tools)
– “During the night both bulb are switched on.”
– “In the morning or evening only one of the two bulbs will be switched on.”
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11. Conclusions
• Mashup tools nicely model data flow
– Very fast prototyping
– Manual coding for components is needed
• Unless there are pre-­defined components
– Describe system architecture + deployment
– Behavior „hidden“ in boxes
• Model-­based techniques
– More expressiveness to model behavior
• E.g. state machines, from which code can be generated.
– Different views
• Separation of model and deployment
– Verification is possible
• Need to combine mashup tools and model-­based approached
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