Why Open Source is Key to Unlocking IoT Development

Developing an Internet of Things (IoT) project can be a daunting task. From designing a prototype, building code, to launching your product and eventually, deploying globally, how can you ensure your IoT project gets off the drawing board and into the market?

With billions of devices estimated to hit the market in the coming years, closed, proprietary systems can make interoperability difficult. ProgrammableWeb editor-in-chief David Berlind referred to the continued evolution of closed IoT ecosystems as a trainwreck. Making different components and elements of a system work together when they were not designed to do so can require a significant investment of time and effort, increasing the time to deployment and the overall cost. One way the challenge can be mitigated is through open source technologies enabling connected devices to communicate with each other.

There are a lot of commonalities between IoT solutions across different applications – the need for wireless connections, communication between devices and back-end systems, collection and interpretation of data are just few examples. But the proliferation of proprietary systems that are often in silos makes developing and building these solutions more complex and time consuming than needed. It also complicates open communication between different systems, potentially hindering future innovation and adoption.

While the challenges are certainly significant in a fast-moving, fragmented industry, there are solutions available – if we are all willing to work together. Here are a few reasons why open source is key to IoT development:

Open Collaboration and Standards Pave the Way

Establishing and implementing industry standards can help us move towards greater interoperability.  Thoughtful and collaborative standards improve choice and flexibility – developers can use devices from multiple vendors to build a solution to meet their specific needs, and as a result, they can be more innovative and more cost-efficient in building their solutions.

Another complementary approach to standards development is the release of designs and specifications developed by industry ecosystem players into the open source community as open hardware and interface standards for others to adopt. This approach has been growing in popularity, with open hardware reference designs and interface standards becoming more readily available with major industry players collaborating to support them.

Some examples are Arduino, Raspberry Pi, and BeagleBone, which are very popular for quick prototyping. The challenge with these types of open hardware is that although they are great for prototyping ideas quickly, when you want to get to market, you have to start from scratch again, either because the licensing doesn’t allow it or the components are too cheap to use in commercial-grade products. Open hardware platforms are evolving so that some can be used for both prototypes and commercial products. 

Developers should look for a business-friendly open source license, as well as industrial-grade components that have been released as an open standard and a large suite of tools to take IoT ideas from initial prototype to mass-market deployment much more quickly. The fact that so much of the integration, testing, and validation work is already done, they no longer have to invest big money when the time comes to expand on a global scale.

Speed up IoT Development With Open Hardware

As we mentioned, open platforms like the above enable developers with limited hardware, wireless, or low-level software expertise to develop applications in days rather than months. If executed properly, leveraging open source platforms and hardware that communicate with each other can significantly reduce the time and effort to get prototypes from paper to production by ensuring that various connectors and sensors work together automatically without additional coding required. With industrial-grade specifications, these next-generation platforms not only allow quick prototyping but also rapid industrialization of IoT applications, because they can go straight from prototype into production.
Working across multiple vendors and platforms, the door is opened for new possibilities with new third-party partnerships and IoT business ventures. It lays the foundation for a new generation of connected applications that can be developed independently from the devices on which they will run.

Greater Ecosystem Support

Open source solutions ensure a future-proof investment and longevity, so that resources and tools are available and continually enhanced for years to come. Not only does it protect the time and investment made in the development stage of a solution, it also provides simplicity that helps to accelerate innovation and time to market.

On the software side, using widely supported open source software application frameworks and development environments, based on Linux for example, can be extremely helpful. When you work with a proprietary solution, support for its development frameworks rests on the original vendor, whose agenda may not align with your needs. Open source solutions offer a wider development community to help to ensure that you will still be able to find development resources for years to come. This protects your time and investment in the development of a solution.

There are other advantages to working with open source software as well. For example, the broader base of developers working with the code can lead to greater scrutiny, which can often result in a more secure solution. It also allows IoT application developers to tailor the code to meet their specific security requirements.

No one can envision every possible application for IoT technology, but committing to a standards-based and open source strategy will help drive IoT innovation and bring applications to market faster, easier and ensure longevity. Standards ensure technology interoperability while open source projects ensure that both hardware and software components can be reused as product and service platforms evolve. Without it, innovation within the IoT will continue to be stunted.

Be sure to read the next Open Source article: Google Releases gRPC 1.0, an Internet-Scale Production-Ready RPC Framework


Comments (1)


Well try to bespoke one to each customer to start with .. Let's assume a 20k seat call centre looking to migrate to an automation tool for a chunk of the call for instance....Too many people employed to perform the job of instrument matching and tool building are too analytical and not creative enough. With voice, for instance, it is too difficult to measure true sentiment utilising existing tools, so there are floored tools people know very well, but a prejudice towards features not true accuracy. The first step should be to acknowledge the weighting that should be given to each tool in the market place you think is valid for the project and that only a few should be used (and create/blend a new tool if the weightage is too low). Whether it's Big Data or Live Data the tone needs to be adequately recognizable into mood, from there into some phonetic / musical frequency adjudication system for every key consumer segment then then matched out to an agreed group size of that consumer demographic. Psycographic segmentation might need to be established early in the experience through the testing of various voice styles and tones as an introduction to each segmentation. Once a true spectrum of audience is understood you can start cross referencing and refining to establish an even more quantified customer type. Then the challenge is to either a) change the experience for every identified segment or b) cheaper, simply pitch one of 100 researched preferences to the audience who match and then and only THEN starting product development and identification. Perhaps a simpler goal (starting point) would be just the agents in call centre's and taking a measurement of their quantitative test results, KPI's and tests and match that to a database with best phonetic representation of say 7-10 criteria. From there the data sets can commence taking on some form. Each critera tool is used for standard QA scoring based on their own capability and audience, then we see if the difference between the quantitive interpretation and the phonetic judgement tool/s based upon those 7-10 agreed criteria is closely alligned. We establish our best research team based on industry voice and analytic instrument calibration as a panel for show, listening in on calls to further refine and re-calibrate the slice and dice of the call and recognisable variances in pitch at various stages and final mood sentiment score and cost savings against existing and live consumer mood and engagement score.

Voice 2.0 built. Next a client who may use Voice 2.22

This is not even half my pitch. It's already changed and better...

Just spent 20 minutes on it twice and it had an error send, but I'll loosely do it into your category too..


Re-imagine the question to get a better answer? Simple. What else can we really use to unlock....Open source or something will always be there. Just add and move bits better.

Point is we create better tools, by first measuring properly what the existing ones can do when matched with certain agents and certain styles. Look fr mood, timing, outcome of call etc an then find another 3-4 which are real and create them.

Use voice and speech experts, data anaylists and a guy called Serg.

The justification of the integration tool is it's ability to put this stuff together time and again. Sell the sizzle and forget the sausage.