Imagine a scenario where a software engineering team with rich experience in building traditional software has been given the task of handling design and architecture of a software for an Internet of Things project.
The answer is ‘Yes’. While the pillars of development remain same the intricacy and complexity of an IoT software design is much higher than for traditional software such as e-commerce, gaming, social media etc.
A fully functional IoT software has 4 major elements – Embedded, Frontend, Backend, Data Science, across its lifecycle.
This forms the skeleton of any IoT project since it forms the first layer of interactivity between the device aka the ‘Thing’ and the internet. While previously in most M2M applications the information from the hardware was collected, stored and computed in a local hosting, with IoT, information is moved to the cloud. Apart from pooling, queuing and pinging the data to the internet, the developer needs to ensure that the information stays secure at all times.
Thus, the team has to design embedded software in such a manner that the device can connect and transmit information with the highest levels of security maintained.
If embedded forms the skeleton, back end development forms the ‘backbone’ of any IoT software project. With more and more devices / things getting connected to the internet, the level of interactivity between systems and devices have increased manifold. The value of an IoT project is derived maximum when software is designed such that it has the functionality to communicate with multiple micro services, multiple clients and multiple devices.
To achieve the desired level of dynamism, the backend development team should develop software such that seamless integrations and inter connections are possible. While HTTP was the most commonly used communication protocol in traditional back end development, the team should train itself to work with IoT specific protocols such as MQTT, CoAP etc.
Real time responsivity is also a very important factor that needs to be kept in consideration during the design.
This forms the brain of an IoT application. Analytics modules need to be integrated in an IoT system such that it can process the high volume and velocity of information being transmitted by devices, and synthesize it into useful information. While processing huge data sets is core to any data science module, with IoT, the processing needs to be done in ‘real time’ with minimal latency.
To achieve the above requirement, data scientists have to equip themselves with understanding of tools such as Apache Spark, Flex, Esper etc. to work with time series data.
In a nutshell, developing a robust IoT software might seem a little overwhelming at the onset, but by getting a clear sense of the end objective of the software and, by equipping the team with required skill sets, the development journey can become smooth.
We at EnrichAI have been trying to upskill ourselves every day in our endeavor to provide robust software services to clients across multiple industries.
Please reach out our team of experts, who can help you make a robust application.