In recent times, the Internet of Things is ubiquitous and is now a popular domain in the developer community. According to research by Statista, there are 6.21 million developers working in IoT and 5.36 million developers planning to work in IoT in next 6 months.
If you want to get started in IoT and are wondering which programming language to start with, here is a list of 11 popular programming languages used in IoT.
C, the language that was first developed to program telephone switches, is a reliable and reasonable choice for embedded system development. It is impressive because of its proximity to machine language.
It is a procedural language and the code is compiled and not interpreted. The code written in C is more reliable and scalable, and processor independence makes it a strong contender for IoT development. Because C is not platform independent, it enables IoT developers for code reuse, which can run on most of the systems.With the help of pointers, accessing and modifying addresses is easy in C.
C++ is a middle-level programming language with imperative, object-oriented, and generic programming features with low-level memory manipulation.
C++ is designed with a bias toward system programming, embedded programming, resource-constrained devices and large systems. The design highlights of C++ are
- Flexibility of use
C++ is a popular choice among the embedded developers coding for Linux systems. Here are a few features that make C++ a popular choice among IoT developers:
- Data hiding
- Stronger typing/ checking
- Multi-peripheral transparency using classes
- Templates (as always if used carefully)
- Initialisation lists
Go is an open source programming language developed at Google. It combines the benefits of compiled language that is performance and security with that of a speed of dynamic language.
It supports concurrent input, output, and processing on many different channels. Coordination of an entire fleet of sensors and actuators is possible when used correctly. The biggest risk is that the different channels don’t necessarily know about one another. If a programmer isn’t careful enough, a system could behave unpredictably because of a lack of coordination between channels.
In GO, gathering and sending data to various sensors and actuators is made easy by adding explicit hash table type.
The biggest advantage of GO is its ability to sort an entire network of sensors and making use of related IoT programming related devices. Go is now available on a wide variety of processors and platforms.
The language, which was developed during a holiday break, went on to become the most preferred language for web development and started gaining popularity in embedded controls and IoT. Python is an interpreted language which can be either submitted for runtime compilation or run through one of the several pre-compilers so that compact executable code may be distributed.
The greatest benefit that Python offers to developers is readability with elegant syntax, without compromising on the size. Python’s clean syntax is apt for database arrangement. If your app requires data to be arranged in a database format and use tables for data control, Python is the best choice.
Python supports a huge number of libraries and modules, so you can get more stuff done with less code. It’s handy in more powerful edge devices, gateways, and also the cloud.
Rust is an open source, general-purpose, multi-paradigm, compiled programming language sponsored by Mozilla. Rust shares many of Go’s qualities and solves race condition problems of Go. Rust includes functions that eliminate race conditions for highly concurrent programs, making it a less risky language. Because of its ability to handle concurrent programming, Rust is now popular among IoT developers.
Rust is a safe, concurrent and practical language, supporting functional and imperative-procedural paradigms. It maintains these goals without a garbage collector. This makes Rust a useful language for the following use cases:
- Embedding in other languages
- Programming with specific space and time requirements
- Writing low-level code, like device drivers and operating systems
Rust is an improvement on current languages by having a number of compile-time safety checks which produce no runtime overhead and eliminates all data races.
Java is an object-oriented language, and there are very few hardware dependencies built into the compiler, which makes it incredibly portable.
The biggest concern in IoT is security; with the Generic Connection Framework 8 (GCF 8), the Access Point API in Java provides the latest security standards and the highest levels of networked encryption and authentication which ensure data privacy.
All the object references in Java are implicit pointers which cannot be manipulated by application code. This automatically rules out the potential risk of memory access violations which can inevitably cause an application to stop all of a sudden.
Connectivity at the application level of the IoT system is also easily handled in Java with a comprehensive set of APIs, both standard and freely available through open source projects.
Assembly language is a low-level programming language and is specific to a particular type of computer architecture. In contrast, to other high-level programming languages, Assembly language is not portable across multiple architectures.
Assembly language is also called symbolic machine code. It is converted into executable machine code by a utility program.
Assembly language is best if you want to make your IoT project compact, minimal, and optimal.
ParaSail stands for Parallel Specification and Implementation Language. It is a compiled, object-oriented language with syntax similar to Java, Python, C#, and Ada.
ParaSail is a language that you must consider if you have a requirement for parallel processing in your IoT system.
ParaSail is capable of both specifying and implementing parallel applications. It supports both implicit and explicit parallelism. Every ParaSail expression is defined to have parallel evaluation semantics.
R is a programming language and environment for statistical computing and graphics. It is widely used by statisticians and data miners for building statistical software and data analysis.
Here are a few statistical and graphical techniques implemented by R and its libraries:
- Linear and nonlinear modeling
- Time-series analysis
- Classical statistical tests
- Classification and others
R is easily extensible through functions and extensions. It has stronger object-oriented programming facilities than most statistical computing languages. Extending R is also made easy with its lexical scoping rules.
B# was designed as a very small and efficient embedded control language. The embedded virtual machine (EVM) that allows B# to run on a variety of different hardware platforms only takes 24 kb of memory. B# is lean enough for 8-bit MCUs.
The syntax of B# looks a bit like C#, with support for the real-time control functions that are critical to make things happen in the real world. The B# code when coupled with a compact virtual machine could be easily ported and reused across multiple hardware platforms.
B# supports writing portable interrupt handlers and device addressing registers in a uniform way.
It also supports modern object-oriented features such as
- Abstract and concrete classes
In addition to these, B# caters to the embedded systems programmer with
- Efficient boxing/unboxing conversions
- Multi-threading statements
- Device addressing registers
- Deterministic memory defragmenter
- Field properties
- Interrupt handlers
Each of these features is directly supported by the constructs of B# and its underlying virtual machine. This helps to create, use, and reuse more portable and decoupled software components across different embedded systems applications.
If your project is going to live on embedded hardware platforms that aren’t as big and complex as a Raspberry Pi, then B# is the best option available.
Forth has been around since 1970 and is designed and optimized for embedded system programming. It is an imperative stack-based language and environment. It includes features such as
- Structured programming
- Concatenative programming
Forth features both interactive execution of commands and the ability to compile sequences of commands for later execution.
Forth is used in the Open Firmware boot loader, in space applications such as spacecrafts, and other embedded systems where interaction with hardware is involved.
HiveQL is based on SQL but does not strictly follow the full SQL-92 standard. It runs on the Hadoop map-reduce framework but hides the complexities from the developers.
HiveQL does not offer extensions in SQL, including multi-table inserts and create table as select. It only offers basic support for indexes. Here are few things which HiveQL can do easily:
- Create and manage tables and partitions
- Evaluate functions
- Support various Relational, Arithmetic, and Logical Operators
- Download the contents of a table to a local directory or result of queries to HDFS directory
Here are a couple of key properties of Pig Latin:
- Ease of programming. With Pig Latin, it is easy to achieve parallel execution of simple and “embarrassingly parallel” data analysis tasks. Complex tasks with multiple interrelated data transformations are explicitly encoded as data flow sequences; this makes them easy to write, understand, and maintain.
- Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing you to focus on semantics rather than efficiency.
Extensibility. Users can create their own functions to do special-purpose processing.
Julia is a high-level, high-performance dynamic programming language for numerical analysis and computational science. Julia language provides
- A sophisticated compiler
- An extensive mathematical function library
- Numerical accuracy
- Distributed parallel execution
What makes Julia’s design unique is its type system with parametric polymorphism, and it types in a fully dynamic programming language. It also has multiple dispatch as its core programming paradigm. Julia allows concurrent, parallel, and distributed computing and direct calling of C and Fortran libraries without any glue code.
Julia’s LLVM-based JIT compiler combined with the language’s design allows it to often match the performance of C.
It does not impose any particular style of parallelism on the user. Instead, Julia provides flexibility to the user by providing a number of key building blocks for distributed computation through which it supports various styles of parallelism.