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Microsoft Orleans & IoT

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Lorenzo Maiorfi
Lorenzo Maiorfi

This document discusses using Microsoft Orleans grains as software agents or "avatars" to represent physical IoT devices in the cloud. An example railway simulation app is described that uses grains to represent trains. Each train has a corresponding "physical train" device that can detect its proximity to stations and rails using Bluetooth beacons and communicates with its grain avatar using MQTT. The frontends use SignalR for the web UI and MQTT for machine-to-machine communication. Physical trains are programmed using .NET Micro Framework and detect beacons using BLE, with an MQTT/iBeacon gateway and feedback simulator implemented in Node-Red.

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IoT & Microsoft Orleans Software Agents living on cloud as Avatars for physical devices livingin real world! Lorenzo Maiorfi Innovactive - MVP
Grains as Device 束Avatars損 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)
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: brownslow, cyanfast) 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 theyre near to a certain Station/Rail after routing system sends them command to get there as part of a end-to-end optimal path)
Orleans Railway Sample #2 In order to 束validate損 system with physical devices we need 束Physical Train損 : Process commands received by its 束avatar損 Grain Raise notifications (through M2M front-end, not Web one) toward its 束avatar損 Grain Interact with physical world! 束feel損 Stations/Rails proximity 束move損 along railway graph
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 束physical Trains損 has been implemented by BLE iBeacons 束Physical Trains損 device firmware has been implemented with .NET Micro Framework (by mean of M2Mqtt and NetMFBLE open projects) iBeacon/MQTT gateway, as well as 束feedback損 simulator, has been implemented with Node-Red

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Microsoft Orleans & IoT

  • 1. IoT & Microsoft Orleans Software Agents living on cloud as Avatars for physical devices livingin real world! Lorenzo Maiorfi Innovactive - MVP
  • 2. Grains as Device 束Avatars損 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: brownslow, cyanfast) 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 theyre 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 束validate損 system with physical devices we need 束Physical Train損 : Process commands received by its 束avatar損 Grain Raise notifications (through M2M front-end, not Web one) toward its 束avatar損 Grain Interact with physical world! 束feel損 Stations/Rails proximity 束move損 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 束physical Trains損 has been implemented by BLE iBeacons 束Physical Trains損 device firmware has been implemented with .NET Micro Framework (by mean of M2Mqtt and NetMFBLE open projects) iBeacon/MQTT gateway, as well as 束feedback損 simulator, has been implemented with Node-Red