Wednesday, December 19, 2018

Emergency Shutoff scenario: Losant IoT Platform

In previous posts we examined how different platforms, such as
LosantSamsung ArtikIBM Watson,  allow implementing a real-time,
dynamic, bi-directional IoT control system: imagine a manufacturing floor
with machines heating up, and a cooling system to cool them down. A sensor
on each machine reports current temperature to the platform, which
if high enough, turns on the cooling system, at which point machines
cool down, and when temperatures are low enough turns the A/C off.

This is all fine in a steady state scenario, where everything behaves
as it should. But when does equipment not break ever?

We used MIMIC MQTT Simulator to create a pathological scenario to make
one of the simulated machines misbehave, and keep heating up even after
the A/C is on.

An emergency shutoff rule on the platform kicks in to prevent the machine
from overheating and shuts off everything, or just the machine in question.

Hope you have tested that in your control system...

This 2-minute Youtube video shows this in action with the Losant IoT platform.


Thursday, December 13, 2018

Real-time simulated control system: Losant

Our minimal standard for a scalable, real-time, dynamic, bi-directional
demonstration of an IoT platform is:

- multiple devices, at least dozens, preferably thousands (it is easy to
provision a few devices, not so easy to provision many);

- arbitrary telemetry (for heterogeneity, the devices dictate the payload,
not the platform);

- easy rules to control your environment, preferably with no programming;

- real-time processing, preferably at the edge;

- bi-directional flow: telemetry generated by sensor-type devices, commands
accepted by actuator-type devices
For this we have come up with a minimal pattern of a control system
like that of a data center with equipment that heats up, causing sensors
to generate telemetry with rising temperature, and a cooling system
controlled by an actuator, to cool them down. The control system receives
temperature values from many sensors, which if any of them cross a high
threshold, turns the cooling system on. Once the equipment cools down
beyond a low threshold, it triggers the actuator to turn off. In the steady
state, the temperatures reported will go up and down predictably.

We have implemented this demo on third-party platforms, like
Samsung Artik  or IBM Watson.

In this post we detail the demo setup for the Losant platform as shown
in this 1-minute Youtube video:

- we setup 2 device recipes, one for our sensors and one for the actuator;

- the payload cannot be arbitrary, but must conform to Losant's template;

- the sensors generate light and temperature data;

- the actuator generates on/off state data, and accepts on/off command;

- once we configured the authentication parameters to our application
context, the devices were able to interoperate with Losant
This screenshot shows the telemetry generated by one of the sensors.
The light value changes randomly, like that of a flickering light. The
temperature value rises and falls within the thresholds. The following
screenshot shows the workflows that control the actuators:


Once the steady-state works, we can implement and test rules that handle
pathological scenarios, such as the equipment not cooling down.

Thursday, December 6, 2018

How to test the accuracy of your IoT applications

While implementing your MQTT-based application's back-end processing,
such as real-time archiving, analytics, edge-processing, graphing, how
do you  verify that it will store and process all received telemetry messages,
with no messages missing, no extra messages, no bytes altered, in the
correct order? Furthermore, it is really hard to make sure that it works at
required scale and speeds, eg. at millisecond granularity.

You need to come up with a performance test that goes beyond simple
load testing and handles each of the test requirements (message integrity,
sequencing and frequency).

To test this case, you can apply the following features found in MIMIC MQTT Simulator:

1. copy the existing Bosch XDK simulation as detailed in an earlier post .
This ships with MIMIC, and generates a realistic JSON payload similar to

{
 "sn":"20:19:AB:F4:00:01",
 "data":{
  "acc":{"x":26,"y":32,"z":1012,"unit":"mG"},
  "gyro":{"x":1220,"y":-6835,"z":-2319,"unit":"mdeg/s"},
  "mag":{"x":40,"y":1,"z":-4,"unit":"uT"},
  "light":{"value":999,"unit":"mLux"},
  "temp":{"value":"100","unit":"mCelsius"},
  "pressure":{"value":98897,"unit":"Pascal"},
  "humidity":{"value":39,"unit":"%rh"}
 }
}


By copying it to a new simulation, you can now customize it without
disturbing the original simulation.

2. change the frequency of the generated messages to 1 msec;

3. change the number of messages to exactly 1000, not one more, not one
less;

4.  change the JSON payload function to return light values in sequence;

5. change the QOS to 2.

This is a total of about 5 lines of change to the existing simulation
configuration.

Once you configure a MIMIC sensor with this simulation, it generates the
1000 messages, and you are able to verify that they are all there in the
correct order. This entire exercise should only take about 15 minutes,
as it did when we tried it


Follow-up tests can be to scale up to run multiple sensors (as many as
required), as detailed in this previous post, and for longer periods of time
(see also this post), with payloads that resemble those in your application
(see this previous post).