Wednesday, August 1, 2018

Video: Monitor end-to-end latency of your IoT Application with 10,000 Sensors

This 5-minute Youtube video shows how to monitor response time to a
MQTT broker in an IoT Application with 10,000 active publishers.


This is not only important in the selection process, but also for ongoing
monitoring / troubleshooting, as outlined in our previous blog post
"IoT Sensors Need to be Managed", and this Gartner report.

We are following the testing methodology outlined in our previous post
"MQTT performance methodology using MIMIC MQTT Simulator" to
minimize the interference between the test equipment and the system
under test.

We are using the open-source Node-RED flows published in our Github
repository to measure and graph end-to-end latency between a publisher
and subscriber once a second for several brokers.

This simulates the latency between your sensors that are publishing
telemetry and your application that is consuming the telemetry.

We are using MIMIC MQTT Simulator to deploy 10,000 publisher clients
to the MessageSight broker on our intranet.

When we turn the switch on, the first 2,000 publishers are starting.
We'll time lapse this process for brevity. Notice how we are graphing
the number of active sensors sending telemetry.

The graphs show the latency for 4 brokers. Only the bottom line for
the MessageSight is being influenced with our active sensors. The
others are purely for control, to make sure our measuring and graphing
is correct. In particular, the mosquitto line should be steady, since
it is doing nothing.

The public broker graphs will be unpredictable.

Notice how the blue MessageSight line is mostly steady around 0
milliseconds.  The white mosquitto line is steady around 50 milliseconds,
and should remain so for the duration of the experiment. (It turns out
the reason for the 50 ms delay is explained here).

If your application has real-time requirements, then response time
is a vital parameter to monitor. Even if not, then response time
degradation can point to problems in your setup, specially at high
scale.

As more sensors become active, the blue latency graph becomes more
erratic. This is expected, as the broker is doing more work. In this
experiment, each sensor is only sending a small message every second,
and you can see the messages per second at the bottom of the MIMICview
graphical user interface.

There are many variables that could impact the latency: the distance
between sensors, brokers and applications, the message profile, that
is the average size of the payload and frequency of the messages, the
QOS of the messages, the topic hierarchy being published to, the number
of subscriber clients and their performance, the retention policy for
messages, and many more. Only your particular requirements would tell
whether the performance is acceptable for you.

(This is a follow-up to our earlier video).
 

Monday, July 30, 2018

Track end-to-end latency to your MQTT broker

Tracking end-to-end latency (response time) is useful in selecting your
MQTT broker, as well as monitoring your IoT platform / broker performance.

Check this real-time latency dashboard at

http://latency.iotsim.io/

which tracks end-to-end response time every second to a number of
public MQTT brokers over the internet. The local intranet mosquitto broker
measurement is for comparison.

The NODE-RED source code is at

https://github.com/gambitcomminc/nodered-mqtt-latency


Thursday, July 19, 2018

MIMIC MQTT Simulator enables rapid prototyping for Telit IoT Platform

If you want to prototype or test your solution with Telit's IoT Platform, then
you can use MIMIC MQTT Simulator to simulate a large number of sensors
and gateways.

In our initial effort we simulate a single sensor generating programmatic,
customizable, predictable, reproducible telemetry to Telit. With MIMIC what
can be done with one sensor can easily be scaled to thousands or even
millions of sensors. Complex scenarios can be setup once, and reproduced
at will thereafter. You can investigate different choices very quickly.

In the screenshot below, the light value changes randomly, but the
temperature value is changed predictably on-demand at run-time.


Friday, June 29, 2018

Testing IoT platform resilience with MIMIC IoT Simulator

With higher scalability of IoT environments in the face of outsider access
over the internet comes the requirement of proving resilience of your
IoT infrastructure. How do you know your IoT application can withstand
expected, and unexpected loads, recover from faults, and resist malicious
attacks?

Even in the absence of hackers, devices break and misbehave all the time.
How does your infrastructure handle the so-called crying baby, ie. a sensor
publishing too frequently, and how can be assured it does not interfere with
legitimate telemetry?

There are plenty of references about this requirement, eg. this post about
Azure throttling:

" We’re already dealing with serious-scale connectivity when we talk about the 
Internet of Things, and we impose the throttling limits on IoT Hub to protect 
against what otherwise looks like Denial of Service (DoS) attacks on the 
service."

or this Gartner report which contains

"... An IoT solution may be made up of hundreds or thousands of
devices. To test all of the devices in their real environments may be prohibitively
expensive or dangerous. However, you also need to ensure that your IoT 
platform and back-end systems can handle the load of all of those devices and 
correctly send and receive data as necessary."



Some platforms document their throughput testing, eg. Solace, or throttling
policies, like for Azure and Amazon.  For others this information is hard to
find.

But even if they are documented, throughput is highly dependent on your
specific application profile, and policies are subject to change at any
time, and you will likely run into some problem in production. Unless you
thoroughly tested before deployment, and can rely on QOS guarantees.

To test your specific performance requirements you will need to setup a
test that recreates your specific message patterns, connectivity patterns,
scale, message processing, etc, not just once, but continually.

MIMIC IoT Simulator is specifically designed for rapid development,
regression testing, continuous tuning, thorough training, compelling
demonstration of large-scale IoT environments.

MIMIC provides a virtual IoT lab with unlimited scale and a flexible,
programmable simulation framework to customize your performance testing.

As a quick example, we did a simple test to run a sensor at 10 messages
/ second to a broker, which ran indefinitely. Then we increased the rate
to 100 messages / second, and in multiple separate tests it was
disconnected after 20 minutes. A look at the packet exchange with
wireshark uncovered an explicit disconnect initiated at the broker.

(Original post at our blog page)

Wednesday, June 27, 2018

Virtual IoT Simulator Lab for Thingworx

In our quest for MIMIC MQTT Simulator to interoperate with a wide variety of
Internet of Things platforms and applications we have completed a scenario
where simulated sensors are feeding real-time, precisely controllable
telemetry via MQTT to PTC's Thingworx Industrial Connectivity.

With MIMIC you can simulate end-to-end large numbers of sensors and
actuators to create the complex scenarios for rapid development,
complete testing, thorough tuning and compelling demo and training
of IoT applications like Thingworx.

This 2-minute Youtube video demonstrates how a simulated sensor is
producing arbitrary and precise telemetry as charted in a hosted
Thingworx Foundation instance. This will now be extended to add
edge processing of a large number of sensors.


Friday, June 22, 2018

MIMIC MQTT Lab for Crouton Dashboard

Crouton is an open-source MQTT subscriber dashboard application based on
Node.js . It relies on its own JSON-based protocol layered on top of MQTT to
deliver dashboard features.

MIMIC MQTT Simulator is a scalable, customizable, predictable, dynamic
simulation platform designed allow rapid development / testing / deployment
/ tuning / training / demonstration of large-scale  Internet of Things
applications.

We have tailored one of our MQTT Labs to interact with Crouton through any
supported public broker. With it you can quickly explore Crouton features,
since you can customize payloads to the Crouton requirements instead of
low-level coding. Since values are dynamically changeable at runtime, you
can investigate dashboard deployment under a variety of scenarios. By
running many sensors, you can determine how scalable Crouton is.

This one-minute Youtube video shows it in action.


Wednesday, June 20, 2018

MIMIC MQTT Lab and IOTA MAM

IOTA is *the* DLT (distributed ledger technology) for the Internet of Things.
The facility for distributing telemetry from sensors is called MAM (Masked
Authentication Messaging), for details see their blog.

We were able to transfer dynamically changing MQTT telemetry generated
from our Bosch sensor simulation in our MIMIC MQTT Lab sent to the public
MQTT broker broker.hivemq.com, then through IOTA MAM to the IOTA
tangle in real-time.

You can check it yourself at this online IOTA MAM Explorer,   enter this
string

MGFJOZACIOWFEKGZARLWYPVUZGSDCAZPVPYACGCZJADGQWHFJLG9ZJGXNFIMVTCXMJUEEXUTEE9DDXHTC

where it says "enter root-address here...", optionally disable the "Pretty
print (JSON)" button, and press the search button.

This is a proof-of-concept meant to show that you can publish end-to-end
any telemetry from sensors to the IOTA tangle in real-time to be consumed
by your IoT application.

See this in action in this Youtube video.

You can also check the transactions on the ledger at https://thetangle.org/.

Friday, June 8, 2018

Free, online MIMIC MQTT Lab for Samsung Artik

We have recently released this free, online MIMIC MQTT Lab for the
Samsung Artik IoT platform and it is available on the Artik marketplace.

In under 3 minutes you get a virtual lab with a simulation of a IoT
control system based on the MQTT standard.

You can investigate:
  • authentication and access of MQTT-based sensors to Artik in your own account
  • telemetry flowing from sensors to defined device types in Artik
  • charting of that telemetry based on device manifest
  • control of actuators from defined rules in Artik
  • pathological conditions that deviate from the normal steady-state

Check out this Youtube video for the entire process from an empty Artik
account.



Thursday, May 24, 2018

MIMIC MQTT Simulator supports Sparkplug IoT standard

Cirrus Link has published an open-source IIoT (Industrial IoT) standard
called Sparkplug which specifies a framework for stateful data exchange
between things (sensors, actuators, gateways) and applications via the
MQTT protocol.

Inductive Automation's Ignition platform with their MQTT Edge module
implements this standard to supply an industrial-strength scalable
IoT application environment. The eco-system of devices that connects
to Ignition via MQTT must support the Sparkplug state machine.

MIMIC MQTT Simulator is designed to perform large-scale development /
testing / proof-of-concept / deployment or training on IoT platforms,
including Ignition. We have applied the open source Sparkplug library
to simulated sensors in MIMIC to integrate a large number of things
into Ignition.

This screenshot shows  10 simulated Bosch sensors publishing unique
temperature values to Ignition.


Monday, March 19, 2018

Gartner Report: A Guidance Framework for Testing IoT Solutions

Take a look at this report  by Gartner's Research VP Sean Kenefick
about  testing IoT solutions. It mentions MIMIC Simulator as a test tool.

"... An IoT solution may be made up of hundreds or thousands of devices. To
test all of the devices in their real environments may be prohibitively
expensive or dangerous. However, you also need to ensure that your IoT
platform and back-end systems can handle the load of all of those devices
and correctly send and receive data as necessary."

MIMIC IoT Simulator enables rapid development, testing, tuning,
deployment and training of large-scale, heterogeneous IoT environments.

Friday, March 9, 2018

Train on Dell OpenManage Network Manager with MIMIC Simulator

The task to set up a lab for training your staff on Dell OpenManage
Network Manager
is expensive and time-consuming. You have to
acquire the equipment, set it up, maintain it and customize it to
reproduce scenarios that you want to practice on. This has been
covered in a previous post.

This is where a network simulator like MIMIC Simulator can help.
Rather than setting up a physical lab that reproduces your production
environment, and scheduling time on this limited resource, you can
import it into MIMIC and multiply it many-fold so that each
of your operators can train on exactly the scenarios you need.



For example, these were scenarios we setup quickly with the simplest
out-of-the-box network that ships with MIMIC: it discovered the
topology shown



Since MIMIC simulates dynamic values at runtime, the performance
monitoring shows realistic values for instrumented resources:



and



We then setup some alarms and they show



To investigate who is generating the top traffic, we could pinpoint
the source of the problems.



Ask yourself how much effort this simple exercise is in your network.
Prepare your staff for contingencies before it costs you much more
in network downtime.

Thursday, March 1, 2018

Integrating Samsung Artik and MIMIC MQTT Simulator via NODE-RED

The Samsung Artik IoT platform exports an API that you can use
to programmatically retrieve and change your device information.

This REST API can then be used in a variety of ways to control your
IoT implementation. In our case, we used it to define the devices that
interact with Artik, eg. for this Youtube video .

One of the ways is through the Node-RED graphical programming
environment. This small setup




allows us to retrieve the device information for our purposes with a
couple of clicks. From an initial state as shown in the Node-RED dashboard



by entering the single Bearer Token of the Artik API, it retrieves the
UserID, registered Device Types and Devices with a couple of clicks on
the trigger nodes.



By saving this information into a file, we can define those devices
as simulated sensors in MIMIC MQTT Simulator

Wednesday, February 14, 2018

Scaling your Node-RED dashboard with MIMIC MQTT Simulator

 

 

Overview

This article shows how you can scale your Node-red visualization to a
large number of sensors. We'll go beyond the tutorials that are readily
available to apply Node-RED to a common Internet of Things (IoT)
scenario. You'll see that what works for one sensor will not work for
many, and a strategy for improving it.

We start by visualizing telemetry for one sensor. It readily shows in
the textual, gauge and chart widgets of the Node-RED dashboard. But,
when we extend it to multiple sensors, the widgets are overrun with
values. We offer one possible solution by changing the problem statement.

The accompanying Youtube video shows this in real-time.

MQTT Lab

We'll be using the MIMIC MQTT Lab accessible on the Internet for free.
20 simulated sensors are publishing MQTT telemetry to the public
iot.eclipse.org broker.  We have seen this lab in previous articles and
videos.


 

 

Single sensor telemetry

We are using Node-RED to visualize our sensor telemetry with the
dashboard plug-in, and I'll go through it in detail.



First, on the left we see a MQTT input node labelled Single sensor
subscribed to a single topic from the public iot.eclipse.org broker.


It feeds to a debug node labelled msg.payload that lets us see what is being received.



In the debug tab we see the JSON payload of our standard simulated Bosch
sensor with telemetry containing acceleration, humidity, pressure and
temperature. Let's focus on temperature.

We can change the temperature at any point in time through the Agent 
Variable Store dialog in MIMIC. Let's do this now. Later we'll visualize the
changes.


We can link the debug node to different stages of our flow and see
what happens.

Next comes a json node, which converts the JSON in the payload to a
Javascript object. Let's link the debug node and see what is in the
object. You can see that the temperature value is accessible at
msg.payload.data.temp.value .


The JSON node then feeds into the Messages node, which is a
counter which counts the messages flowing through it. It feeds into
our dashboard to display the number of messages received. We see it
slowly incrementing.



Let's now link the json node to the rest of the flows to visualize
more of the telemetry.

I'll start by linking the json node to the NOOP node. This is just
a convenience node that will later let me do some easy re-linking.
It just passes the message straight through to some other nodes.


First is the Samples counter node, it counts the number of samples
arriving. For now, this is exactly the same as the number of MQTT
messages received.



We can see it in the Samples dashboard widget that is placed in the
Telemetry group in the Home dashboard.


This is what the dashboard looks like.

The SN number text widget underneath the samples widget extracts
the serial number that was received.


The temperature text widget shows the numeric value of the temperature
received.


The gauge and chart widgets need a single value extracted out of the
payload, which is what the Extract temp node does.




Let's change the temperature again in the MIMIC lab and see the
visualization.


As soon as we click Ok in the MIMIC Agent Variable Store dialog, the
value changes from 20000 mCelsius to 10000.



Many sensors

So far so good for one sensor. But, if you want to use this for many,
it will not work. Let's try by switching from the single sensor input
node to the many sensor input node labelled Bosch sensors.


First, we see many more messages received as shown in the debug
tab. The simulated sensors in our MQTT lab are only generating 1
message every 10 seconds on average, so you can see how easy it is
to overwhelm the collector with messages.



Second, the gauge only shows the value for the last received telemetry,
and the chart now becomes a jumble of lines for the different sensors.
It's hard to discern anything.


Let's now modify this setup to make this scenario a little more useful.
Let's say all the sensors below 80 degree celsius are normal, and
we only want to visualize the sensors that are running too hot.


We can do this with the switch node. It only sends along messages
that have a temperature value higher than 80 degrees.


Let's link it in, and see what happens.

I use an Inject node named clear stats to inject an empty message to
clear the stats.


Even though further messages are being received. No samples are reported.



Let's now change one of the sensors to an abnormally high temperature.


As soon as it reports, it is visualized.



We saw how MIMIC MQTT Simulator helps in scaling your NODE-RED
visualization.





Monday, February 12, 2018

SiteWhere tracks simulated sensors

We have used the SiteWhere JSON API to register a multitude of
simulated Bosch sensors driven by MIMIC MQTT Simulator
and are generating telemetry.
















With MIMIC you can setup large IoT environments very quickly to
test/deploy/tune/train your IoT platform.


Thursday, February 8, 2018

10 simulated sensors driving charts and rules in Samsung Artik

This 1-minute video shows 10 simulated sensors in MIMIC MQTT Simulator
from Gambit Communications as displayed by the Samsung Artik IoT
platform. The sensors are changing temperature in real-time, which is
reflected in the charts. When the temperature goes above a threshold on
any sensor, e-mails are sent.
















With MIMIC you can reproduce any scenario at will for testing, training
or demo of your IoT application.


Friday, January 19, 2018

Real-time simulated control system on Samsung Artik

In this one-minute Youtube video you'll see a real-time simulation in
MIMIC MQTT Simulator of a control system with multiple sensors exporting
temperature values to the Samsung Artik IoT platform. The values increase
until a threshold, at which time the platform rule activates an actuator,
causing the temperature values drop. When low enough, the actuator is
turned off.















This setup quickly simulates what happens in any kind of control system,
be it a data center with heating computers and fans to cool them, dams with
rising water levels and valves to release the water, pressure inside reactors,
etc.

What is missing in this simulation are backup rules in case the temperature
does not respond as expected. Once implemented, the simulation can
verify any pathological scenario, allowing disaster training, etc.

MIMIC MQTT Simulator can prototype solutions in a fraction of the time of
real systems.

Friday, January 12, 2018

Real-time telemetry from simulated sensors to Thingsboard

MIMIC MQTT Simulator can deterministically control multiple sensors'
telemetry values to reproduce any scenario you need.

In this one minute Youtube video we demonstrate how it can be done.
We change the temperature values of 5 simulated sensors that are displayed
in the customized Thingsboard dashboard.

In MIMIC, what you can do with one simulated sensor you can do with
thousands, if not millions.


Thursday, January 11, 2018

Video: Simulated real-time telemetry to Thingsboard IoT Platform

This 30-second Youtube video demonstrates real-time changing telemetry
from a simulated sensor in MIMIC MQTT Simulator visualized in the
Thingsboard dashboard. In MIMIC, what you can do to one sensor you can
do to thousands, if not millions, to allow development, testing, tuning,
training, demonstration of complicated IoT scenarios at large scale.

Tuesday, January 9, 2018

Video: Real-time telemetry simulation to Pubnub

Pubnub has recently updated their MQTT support and we have verified
that indeed it is easy to connect simulated MQTT sensors.

Following those directions we have configured our usual Bosch sensor
simulation in MIMIC MQTT Simulator to publish changing temperature
values and which are detected in a configured function in Pubnub
as shown in the debug console.


This 1-minute Youtube video shows the sensor publishing diverse
telemetry such as acceleration, humidity, temperature, pressure, etc
every 10 seconds. After a couple of intervals we change the temperature
in real-time through the MIMICview GUI from 50 C to 150 C. The configured
function in Pubnub detects the published values and prints them in the
debug console.

What we did interactively through the GUI can be done programmatically
for any number of sensors at will to reproduce any required scenario.