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As Gartner predicts, there will be 20 billion Internet of Things devices by 2020. Such intense IoT-ization will require the talent of IoT developers with the relevant set of skills to power these devices with functional software. So if you are thinking about starting a career as an IoT engineer but are not sure about what technical skills you should hone, carry on reading — in this article, we’ve gathered top skills that are and will continue to be in high demand on the IoT market.

Layers of IoT Architecture and How to Bring Them to Life

The complexity of IoT systems determines the diversity of skills required for development. Corresponding to the number of layers in a typical IoT architecture, there are five key areas of expertise:

A layer of an IoT architectureAn area of IoT expertise
Device layerEmbedded software development for smart sensors and endpoint IoT devices
Communication layerEmbedded software development for field gateways
Data management layerData lake and big data warehouse design, development, and maintenance
Data analytics layerBatch and stream big data processing, implementing machine learning, data visualization
User communication layerUser application development (web and mobile backend development, web design, UI/UX design)

1.  Embedded software development

Endpoint IoT devices combine hardware and software components. With processors being integrated into everything from a toaster to an industrial machine, there is an increasing demand for developers of embedded software. An engineer should be able to create an embedded software that deals with unreliable data (consider sensor drifts) and works correctly in an uncontrolled physical environment.

Since IoT devices usually have limited computing resources, the language of choice for embedded software development is C. It is lightweight, does not require much processing power, ensures fast performance and is suitable for writing low-level code.

However, as C does not have object-oriented capabilities, C++ is usually used as a preprocessor for it. Recently, other languages, like Java SE Embedded, Java ME Embedded, have become more popular with embedded engineers.

Another asset important for embedded software development is experience with GPIO (General Purpose Input Output) and I2C ((Inter-Integrated Circuit — pronounced I-squared-C) interfaces, which are used to establish communication between a micro-controller and a connected device.

2.  Field Gateways

Field gateways serve as a link between IoT devices and the cloud part of an IoT system. Their task is to acquire data from connected devices, filter and preprocess this data, and send it to the cloud for further processing and storage. The code running on field gateways is usually written in C or C++.

3.  Data Management

IoT companies are open to hiring professionals who have experience in working with cloud computing technologies and can design, implement, and maintain reliable and scalable data management solutions for handling intense volumes of streaming IoT data. The tasks an engineer should be able to carry out in order to build this layer include:

IoT devices generate enormous amounts of data. At the data ingestion stage, the data is prioritized and categorized, which makes it flow smoothly to the subsequent components of an IoT system: a streaming data processor and a data lake. Data ingestion is usually carried out with such tools as Azure Event Hubs, Apache Kafka, and Amazon Kinesis, which require the knowledge of Java, Python, and .NET.

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The data streaming component allows processing an array of incoming data records as they arrive and instantly taking a corresponding output action. Data streaming is carried out with the help of such tools as Azure Stream Analytics that uses an SQL-like query language, and Spark Streaming that supports Java, SCALA, and Python.

The data storage component handles the storage and organization of data. Data storage for IoT is most often implemented using open-source frameworks like HDFS (supports Java, SCALA, and Python), Apache Cassandra (Java and Python), and Apache HBbase (Java).

4.  Data Analytics

Data analytics skills are intensely sought after. Employers search for engineers who have experience in implementing data analytics applications and know how to visualize the insights gained with the analysis of IoT data. Typical tasks an IoT engineer should be able to carry out at this level include:

  • Batch Parallel Processing

Batch processing means processing blocks of data that have been stored for a period of time, for instance, processing equipment utilization data at the end of a shift. Apache Hadoop is the most often used framework for batch processing. It uses the MapReduce programming model, which provides engineers with a Java-based programming interface.

  • Processing Complex Events

Complex event processing helps to aggregate many different data records (events) and establish cause-effect relations between them in real time. The time between the point when an event arrives at the database and the moment it is processed is usually not more than a few milliseconds. The most common tool for processing complex events is Apache Spark Streaming. You can choose to write Spark Streaming programs in Scala or Java.

  • Implementing Machine Learning

Machine learning algorithms are applied to real-time or historical data to identify patterns and anomalies in the data and autonomously make decisions based on the identified correlations. Machine learning skills have experienced a 220% increase in demand over 2018 and are at the top of the in-demand IoT skills. The key languages that are commonly used for implementing machine learning and are worth mastering include Python, Java, and R.

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Data visualization is an integral part of IoT. It helps to present the insights gained with data analysis in a form that is easy to percept. Python (and its libraries Seaborn, Bokeh, and Pygal) is considered a prevalent data visualization language. It is used for a wide range of visualization tasks, including visualization of streaming data. For quick, ad hoc visualization, it is also possible to use R.

5.  Web and Mobile User Apps

Web and mobile applications allow users to interact with IoT systems, for example, view insights gained from the analysis of IoT data, monitor and send commands to IoT devices. The web and mobile user applications used in IoT are much like other web and mobile apps in terms of functionality; IoT specific at this level is low.

Web Apps

The back end of web applications is developed using such languages as Java, .NET, and PHP. Frontend web app development requires the knowledge of HTML, CSS, and JavaScript (Angular, React, Backbone, Meteor, etc.).

Mobile Apps

Many IoT applications are controlled by means of smartphones. In this respect, a developers is expected to create high-performing and user-friendly native (iOS, Android, Windows Phone) and cross-platform (Cordova, Xamarin) mobile applications that will reliably communicate with cloud servers and external hardware.

To Recap

As you’ve probably realized, IoT is multilingual — it speaks many programming languages and requires experience with a range of frameworks. However, an IoT engineer is not necessarily a Jack-Of-All-Trades. Being able to work at just one layer of an IoT architecture is already enough to start a successful career in IoT. Here is a short recap of what skills you can choose to hone:

  • For developing embedded software for end-point IoT devices, start mastering C and C++.
  • For cloud development, concentrate on:
    • Gaining experience in design, implementation, and maintenance of data management solutions. For that, a good command of Java is a must.
    • Mastering stream and batch processing. Get used to working with such frameworks as Azure Stream Analytics, Spark Streaming, and Apache Hadoop (Java, SCALA, and Python).
    • Learning how to extract insights from big data by implementing machine learning and visualizing these insights. The prevalent languages for that are Python and R.
  • For user application development, learn how to develop high-performing user-friendly web (Java, .Net, or PHP for back end; HTML, CSS, and JavaScript for front end) and mobile (native and cross-platform) applications.

Feature image via Pixabay.

Source: InApps.net

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