Chaos testing was first pioneered by Netflix and has since become a standard practice across many different software stacks. However, the field of IoT (Internet of Things) has been slow to adopt this practice.

To perform chaos testing in IoT, the testing team must first identify potential failure points in the system. This could include network outages, server failures, or other types of disruptions that could impact the operation of the IoT system. Once potential failure points have been identified, the testing team can create a plan for introducing disruptions into the system.

One approach to chaos testing in IoT is to simulate different types of failures and disruptions in a controlled environment. This allows the testing team to assess how the system responds to different kinds of failures and to identify any vulnerabilities or weaknesses that may need to be addressed. Another approach is to perform “live” chaos testing, where the testing team introduces real failures and disruptions into the system. This can be a more effective way of identifying potential vulnerabilities. Still, it also carries a higher level of risk, as there is a possibility that the system may not be able to recover from the disruptions.

What could possibly go wrong in an IoT System?

1. Device Malfunctions: IoT devices can often malfunction subject to the environmental factors that they are in. This can range from deviant sensor values arising from a change in reference voltage to possibly a corrupted memory sector on the device.
2. Data Pipeline Congestion: Data ingest pipelines can develop long queues if there are insufficient workers to clear the buffer. Such situations can arise from insufficient infrastructural capacity or inadequate architectural planning.
3. Network Reconnects: These can occur for a plethora of reasons. Considering a situation where an Availability Zone went offline for an hour, your application would face a massive influx of traffic when all your devices reconnect.
4. Bottlenecks at scale: Certain bugs or limitations only manifest at scale. Unfortunately, this is often caught by the customers and users, leading to an awful experience. De-risking scaling issues in a pre-production IoT environment can protect your brand value.
5. Device or Application level Misconfiguration: Errors arising from improper configuration can quickly bring down your system. An accidental OTA update increasing the frequency of a heartbeat message can exponentially increase the load on the system.
6. Malicious Actors: An attacker who has gained access to your endpoint can easily create chaos by trying to ingest malformed data. This may even escalate to DDoS attacks.

How to do Chaos Testing for an IoT System?

Suggested Read: ‘Why IoT needs Simulation for Load Testing’

In all the above cases, the testing needs to be done by a custom model or flow. They cannot be a ‘one size fits all’ approach for Chaos Testing. This is where you can leverage the versatile nature of the IoTIFY device simulator for testing your IoT system.

Start by creating an account on IoTIFY at IoTIFY.io

IoTIFY allows you to statefully model your test cases and gather meaningful insights into your system. You can very easily model the payloads sent by your device and capture its stateful and dynamic behaviour.

The critical feature to note here is that once a test has been created, it is run by instantiating several ‘clients’ that run independently with their own memory. Test engineers have been able to develop simulated IoT device models with IoTIFY that are indistinguishable from real hardware as far as their application backend is concerned. You can learn how to create a basic device model of an IoT Lightbulb here