Internet of things (IoT) is a complex network of devices, systems, and sensors that are interconnected to share data. It enables users to manage the data and control devices remotely as per the business need. IoT has gained a lot of momentum in the past few years due to increased availability of high-speed internet, increased computing power and cloud services.
By 2025, there will be over 75 billion IoT devices globally. As a result, the global IoT services market is poised to grow from $163.70 billion in 2021 to $188.80 billion in 2022 at a CAGR of 15.3%.
IoT systems are built to enhance cost optimization and operational performance of a business. Hence, businesses need to validate the efficiency and scalability of their IoT systems. It is crucial to measure its performance through simulation real-world workload.
However, IoT Testing Services are not like the regular performance method. Here are the differences:
- Apart from devices, sensors are also involved
- Data sharing is continuous
- The number of devices could run into millions
- IoT devices automatically raise requests and provide responses
- Business Intelligence (BI) is an integral part of IoT
- New and non-standard protocols are used to communicate
IoT app performance-testing challenges
Due to the above-mentioned differences, performance testing for IoT comes with multiple challenges.
One of the major challenges in IoT performance testing is the technical environment. Performance engineers need to finalize a design scenario – a load-testing product that meets the protocols used in the IoT app. It is advisable to go with a performance testing tool with an SDK. This will eliminate your dependency on the software’s R&D to ensure the required development for your IoT performance testing.
For protocols using low level transport layers (TCP/IP or UDP), leverage the recording engine of your performance testing tool. All the gateways subscribe to different data and do not transmit identical payloads. In such scenarios, you need to correlate and design accurate workflows. Simply capturing the traffic and replicating will not create real-world scenarios and might generate errors.
Sensors and gateways have defined workflow of messages. They keep on transmitting data every second and subsequently receive requests that trigger sensors or gateways to send more messages. Hence, you need to understand the workflow intricately before developing the endpoint.
Like regular performance testing, it is crucial to understand how objects and users interact to develop meaningful useful cases of the IoT architecture – starting from application layer to endpoints. Performance test engineers also need to emulate network issues to address packet loss and latency. Executing tests without a network can restrict you from identifying potential performance bottlenecks. Use wide area network emulation.
Where to focus your load-testing efforts
Analyze the IoT architecture diagram judiciously to mark areas where you need to focus more.
Performance testing of the IoT platform
Performance engineers need to validate the performance, scalability, and sizing of the IoT platform to identify current limitations and suggest architecture upgrades. It is often perceived that IoT platforms don’t face performance issues when they are hosted on the cloud. Even when businesses are leveraging cloud architecture, you need to fine tune thresholds that keep informing the platform about the requirement of new nodes.
IoT platform being the heart, you should main components (API) on a regular basis to identify potential regressions. It can also help you monitor the performance between previous and latest builds.
Performance testing of gateways
Gateways facilitate communication among all the local sensors. Based on the project, there can be hundreds or thousands of objects. In most cases, gateways are a small mobile device or Raspberry Pi with normal hardware. Hence, it is crucial to identify the capability of each device to ensure flawless interaction between objects.
Performance testing of gateways can be challenging as you must use a load testing tool that has a generator to transmit messages over multiple data sessions through Wi-Fi or Bluetooth. You can do it cost-efficiently by building a Raspberry Pi that is compatible with the present protocol.
Install load generator component of a performance testing tool on the Raspberry Pi. Develop the protocol by using SDK related to the performance testing tool. You can also define response time for the IoT application. Calculate the time needed for the message to go through the entire IoT architecture – lesser the better.
Network conditions and geography
To simulate load from multiple geographies, you can leverage JMeter which is integrated with multiple cloud solutions including AWS. Using cloud, JMeter can generate traffic from multiple locations globally to IoT apps to mimic network latency and geographical spread.
Simulating different network conditions can help you gauge the performance of IoT applications in different locations and identify performance bottlenecks.
IoT app monitoring and BI processing
Monitoring IoT applications can help you understand the behavior of systems in real world scenarios. You need to monitor both backend BI systems and IoT applications to understand the processing of data in terms of accuracy and volume.
IoT has now even entered our homes in the form of home assistants and smart devices. With thousands of use cases of IoT devices, possibilities are immense, and every business will have to leverage its efficiency and accuracy to become agile and customer centric. It becomes even more crucial for global businesses who are delivering their products and services in multiple geographies across the world.
IoT performance testing can help organizations unlock the real value of the internet of things, analyze actionable insights, and make more informed decisions while staying cost-efficient.