Software Agents living on cloud as Avatars for physical devices living…in real world!

1. IoT & Microsoft Orleans
Software Agents living on cloud as Avatars for physical
devices living…in real world!
Lorenzo Maiorfi – Innovactive - MVP

2. Grains as Device «Avatars»
Mentally mapping a Grain (type) to a Device (type)
is easy, since:
• Both work in a parallel, asynchronous and independent
way
• Both interact by exchanging structured data by mean of
well-known channels, schemas, formats, messaging
patterns
• Both are replicated in many, many instances, each
producing and consuming an impressive amount of
messages
• Both maintain a persistent state, either in nature
(devices physical state) or through persistent storage
(grains)

3. Orleans Railway Sample
• Sample implements a fictitious railway network,
inspired by real-world systems
• Railway graph is made of nodes (Stations) and
directed edges (Rails), the latter featuring a traversal
cost (simulated by time needed to cross and
represented graphically by color scale: brownslow,
cyanfast)
• Each Train features a “buddy” Grain that represents its
“Avatar” living on cloud
• Sample leverages an external “notification engine”
that processes signals coming from Trains in order to
provide synthetic feedback coming from real-world
(i.e. telling Trains they’re near to a certain Station/Rail
after routing system sends them command to get
there as part of a end-to-end optimal path)

4. Orleans Railway Sample #2
In order to «validate» system with physical devices
we need «Physical Train» :
• Process commands received by its «avatar» Grain
• Raise notifications (through M2M front-end, not
Web one) toward its «avatar» Grain
• Interact with physical world!
• «feel» Stations/Rails proximity
• «move» along railway graph

5. How did we do it?
• Web front-end UI has been implemented with SignalR
(over OWIN) and plain HTML+JavaScript
• M2M front-end has been implemented as an MQTT
client (publishing move commands and subscribed to
proximity messages)
• Proximity detecting for «physical Trains» has been
implemented by BLE iBeacons
• «Physical Trains» device firmware has been
implemented with .NET Micro Framework (by mean of
M2Mqtt and NetMFBLE open projects)
• iBeacon/MQTT gateway, as well as «feedback»
simulator, has been implemented with Node-Red