IoT Sensors Need to be Managed

In order to be adopted, the components of the Internet of Things must be
manageable and managed, according to this Gartner report .
Specially in light of the revelations of vulnerabilities in commercial
networking equipment, such as the recent CERT advisory regarding Netgear
routers, CIOs are going to be hesitant of adding to the exposure to hackers
with this new class of networked gadgets.

Whether the sensors themselves have management interfaces, or the
network infrastructure (edge gateways, wireless access points, ...), the IoT
environment consists not just of the data plane, but must consider the
management plane.

MIMIC IoT Simulator provides a comprehensive, integrated framework for
simulating large IoT environments by providing common network
management APIs to simulated devices, such as SNMP, command line
interfaces (CLI), Web services, NetFlow, etc.  By combining the required
interfaces into your simulation you can exercise your management
plane for intrusion detection, fault and performance monitoring while
generating desired payloads to your IoT platform for large data analytics
and anomaly detection.

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MIMIC MQTT Simulator for testing IoT Anomaly Detection

Anomaly detection for IoT is a challenge of both infrastructure monitoring and big data.

For an example of the former, if a sensor fails to PING for a while, it can be
assumed to be down or unreachable. This can be detected with traditional
network management applications, this scenario is shown with MIMIC 
NetFlow Simulator generating flows to ELK.

On the other end of the spectrum is a sensor that is malfunctioning by
generating too much data, as the highlighted green node in the Kibana
graph below.

Data generated by your IoT sensors are a special case data source for
Anomaly Detection.

For reference, check these white papers

https://www.bosch-si.com/internet-of-things/iot-downloads/iot-analytics-white-paper/anomaly-detection.html

https://aws.amazon.com/blogs/iot/anomaly-detection-using-aws-iot-and-aws-lambda/

https://www.oreilly.com/ideas/the-elements-of-anomaly-detection-in-the-internet-of-things

https://software.intel.com/en-us/articles/change-and-anomaly-detection-framework-for-internet-of-things-data-streams

Database techniques can be used to populate your data repository for
priming an anomaly detection algorithm, but only real-time generation
of precisely tailored data verifies that end-to-end processing works
as intended.

MIMIC MQTT Simulator can simulate large numbers of heterogeneous
sensors generating desirable data patterns in real-time over MQTT. For
example, you can have miriads of sensors generating MQTT payloads
containing a "normal" pattern, and instruct a small subset of them to
"misbehave" predictably, then observe how long it take to detect this
anomaly.

By deterministically varying the anomaly patterns in the simulator you are
able to tune and regression test iterations in your detection algorithm.
You are able even to explore boundary conditions of the infrastructure
requirements, such as message rates, failure conditions, etc.

Simulate thousands of Bosch sensors with MQTT Simulator

You can use the Bosch XDK Cross Domain Development Kit to connect your
Bosch sensor implementation to your IoT platform of choice. But how do you
load test with thousands or hundreds of thousands of sensors?

Here is a Youtube video that shows this in real-time.

We simulated the sample sensor in MIMIC MQTT Simulator with the sample
JSON in under one minute by just doing a copy/paste of the message from
the web page into MIMIC.

The "Subscriber" in the screenshot shows the unmodified message received
by the Mosquitto subscriber as the first message.

Then we modified certain fields to return different values. In the screenshot
they are the underlined "sn" and "value" fields.

Starting 1000 sensors to generate those values to the broker took another
minute. The "MIMICview" shows 5000 sensors configured, and 1000 sensors
started. The "Broker" terminal shows the IP addresses of the connecting
clients.