What is IoT Development? | Enterprise Networking Planet

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Developing Internet of Things (IoT) solutions today may prove to be highly complex. Clearly, the expectations of connected technology continue to rise to guarantee security, resilience, and intelligence.

These rising expectations also means IoT professionals are constantly seeking to improve their skills to satisfy the expectations of the solutions they create. At the same time, enterprises are investing in IoT development to tap into gaps in their sectors to maintain their competitiveness.

In short, IoT development is now a growing and highly important IT sector.

IoT development refers to the set of activities, processes, tools, and technologies dedicated to the creation, design, deployment, and support of IoT solutions. It involves the implementation of configuration and coding tasks needed to build and maintain software and hardware aspects of an IoT solution.

The scope of IoT development is vast, as it may cut across security, cloud programming, hardware device programming, networking, systems engineering, and more. This means IoT development is highly collaborative and involves partnering with various professionals under the IoT development banner, as well as other stakeholders, to successfully implement and maintain IoT solutions.

IoT solutions need to be high-quality, robust, scalable, user-friendly, and secure. However, IoT development faces some challenges.

IoT provides a wide attack surface for cyber threats to attempt an attack. It may take compromising just one device out of thousands in an IoT network to expose a whole system.

As cyberattack incidents continue to rise, they highlight the high-stakes nature of IoT security. A lack of robust security for IoT platforms, unsecured interfaces, and unencrypted transmission of information among interconnected devices continues to put IoT networks at risk.

After evaluating the devices they intend to work with, IoT development teams must select operating systems (OSs) suitable to these devices. However, these devices are more constrained in terms of power and memory in comparison to computing systems such as desktops. Selecting the operating systems under such constraints without compromising the effectiveness of the IoT solution can be a challenge.

IoT systems are characterized by the ability of their devices to transmit data across interrelated equipment. The growing complexity of these networks and their implications introduces greater complexity to the task of specifying the numerous levels of interaction between these devices – and then making them more interoperable.

The potential of the Internet of Things and technology as a whole can be overshadowed by a reliance on technology and how it’s trusted with confidential information. The threats plaguing the Internet of Things include data leakages, data and identity theft, man-in-the-middle attacks, social engineering, and more.

Today, the data at risk is attached to legal regulations that can have stringent implications on the developers of IoT systems and the organizations implementing them in case of violating these regulations. Additionally, some implementations of IoT may also be overshadowed by ethical and moral concerns.

The flexibility of IoT implementations means the scope of testing, usability, and compatibility is much wider than that of traditional IT systems. Furthermore, some IoT use cases, such as IoT insulin pumps, leave no room for error, as small errors can be fatal. Constantly ensuring that IoT solutions can maintain quality services in an ever evolving environment is a continuous challenge for development teams.

Also see: Trends Shaping the Future of IoT

IoT development teams also need to understand how to take advantage of the latest IoT technology trends to better leverage effective IoT solutions.

As cloud adoption and migration continues to be a priority for enterprises, at least for the near future, the need for identifying fresh ways to drive efficiency and increase capabilities in the cloud will rise.

Having the cloud as the standard at a platform, software, and infrastructure level is empowering IoT development teams to create and optimize applications for cloud performance and scale. These solutions are increasingly reducing time-to-market for organizations, providing increased reliability, while lowering infrastructure costs and complexities.

IoT in healthcare has been one of the most active sectors of IoT development over the past few years. Healthcare technologies that provide smarter patient care with much less human intervention have increased as a result of the COVID-19 pandemic.

The scope of IoT in healthcare includes use cases such as telemedicine and remote healthcare, lifestyle monitoring through trackers and fitness bands, specialized medical equipment such as heart rate monitors, and more.

These use cases are increasingly focusing beyond the pandemic to enable medical professionals to examine, diagnose, and treat a greater number of patients and extend healthcare services to regions where physical access to medical facilities or professionals is a challenge due to difficulty of access or remoteness.

For the longest time, the need to transmit data to the cloud before selecting relevant data has presented a bottleneck for IoT implementations. However, this paradigm is shifting with greater AI and tiny machine learning (TinyML) at the edge. TinyML scripts are automatically trained to identify valuable data and lower the operational reliance on cloud-side analytics.

With TinyML, organizations and IoT development teams can deploy increasingly efficient AI capabilities for IoT without the need to use specialized AI chips. AI algorithms are also becoming more efficient, as they require much less compute power compared to a few years ago. This is gradually leading to the democratization of AI programming for IoT and the expansion of the number of developers working with AI.

Furthermore, integrating AI with IoT applications is proving to be a serious driver for digital transformation. The COVID-19 pandemic played a part in accelerating deployments of such solutions to make the most of connected technology during such a physically restrictive period. Sectors such as automotive, medical, and industrial are making the most of these AI and IoT synergies, as AI and IoT implementations have been a growing sector. 

Open-source IoT has great promise, provided developers can navigate past its challenges and pitfalls. Its protocols, software, and hardware tools offer an open approach to IoT development, which can eradicate the fragmented nature of IoT ecosystems. Open-source IoT can lower the dependence on locked-down cloud ecosystems. Vendors are dipping their toes into the open-source IoT pool and there’s great potential here. 

As more vendors realize that a model of open innovation and development strengthens the whole IoT ecosystem as opposed to weakening their competitive advantage, we should expect to see more open-source collaboration between vendors, provided it is implemented correctly.

Data analysis skills are becoming increasingly important for IoT developers that intend to manage ever-evolving enterprise networks. A particular data analysis skill IoT developers may require is time series data analysis.

More and more IoT implementations are demanding capabilities to exploit rapidly generated sensor data. Development teams expected to put in place systems to make sense of IoT data should consider learning these data science elements. This will help them avoid being overwhelmed as more data tasks are brought to the edge.

The convergence of the Internet of Things with immersive technologies such as virtual reality, augmented reality, and environment simulation technology opens up endless possibilities for the development of immersive IoT technologies.

However, this convergence will involve large amounts of data. A combination of 5G and edge computing will support this convergence to deliver these immersive products. This will ultimately accelerate the development of immersive applications for industrial and enterprise applications.

Development teams need to be aware of how to guarantee that their IoT deployments are effective. Below are some of the best practices they need to keep in mind.

The volume of data generated by the Internet of Things is indeed massive. Determining where and how to store data will be critical to the usefulness of data generated by an IoT solution.

It is important to determine which data is valuable enough to be transmitted to the core of an enterprise and which should remain on the edge. Quickly separating data according to its usefulness will affect where and how data is stored, which in turn will influence the effectiveness of how the data is used by teams, applications, engines, and more.

The type of platform chosen for IoT deployment has great implications for the IoT solution and its overall usability. Choosing a platform will require a developer to consider the long-term implications of their potential platform. They need to be aware of not only the software but also the hardware aspects of the solution to support their long-term goals.

Taking this into consideration enables IoT developers to choose platforms that help future-proof their solutions, as it will impact the ability to change, revise, and adapt the designs of their solutions when the need arises.

Each device connected to the internet is a potential vulnerability in the eyes of an informed threat actor. Simply knowing that IoT networks can have countless devices highlights the importance of building security into the design of a system from the very beginning. 

IoT solution developers need to follow a secure software development methodology. Such a methodology influences the choice of platform, tools, and languages and helps implement a ground-up approach to security. They should also select open-source software with care, as it provides an option to develop solutions rapidly.

Finally, IoT developers should exercise care during integration, as numerous software security flaws exist at the boundary of application programming interfaces (APIs) and libraries. They need to check all interfaces of components being integrated for flaws.

The malleability of the Internet of Things enables developers to approach products in limitless ways. However, developing IoT products while lacking a clear vision presents a threat to the effectiveness of the product.

Developers need to think about the nature of the systems they are developing and the devices that surround these systems. They should avoid falling into the one-size-fits-all mentality in the context of the Internet of Things.

The only way to guarantee best practices are followed is to test and test continuously. Developers need to test every system endlessly. They should test them with a range of unintended use cases and always prioritize security testing.

IoT developers also need to carry out tests every time they change or implement new features. These tests should be against both predictable and unpredictable use cases. Constant testing can reveal opportunities for product improvements, of which developers must always be ready to explore.