Wireless Communication in Embedded Systems

Embedded systems are extensively used in wireless and mobile communication systems, from smartphones and laptops to home appliances, industrial automation, and the Internet of Things (IoT). They leverage a variety of wireless communication protocols such as WiFi, BLE, Zigbee, Cellular, and Z-Wave. Collectively, these wireless protocols have brought us closer together and more connected than ever before.

In this blog post, we’ll delve into the essential elements of wireless communication technologies in embedded systems. We’ll explore the differences between low-power and high-power solutions, weigh the strengths of Bluetooth, Wifi, LoRa, Cellular Networks, Z-Wave, and Zigbee, look at the contrasts between long-range and short-range communications, and consider the trade-offs of high and low bandwidth. Furthermore, we’ll talk about communication architectures, differentiating point-to-point communication from mesh networks. We’ll also discuss how to cleverly blend these features to tailor them to the precise needs of your embedded application. So, let’s get started and unravel the world of wireless communication in embedded systems.

Wireless Communication Protocols in Embedded Systems

Embedded systems are like specialized multitaskers. They are built around tiny but powerful computers (microcontrollers, microprocessors) and are programmed to handle specific jobs. To get their work done, these systems need to talk to other devices, whether it’s through wired or wireless connections. This is where communication protocols come into play, which define how data is transferred between devices.

Wireless communication protocols are the backbone of seamless connectivity and data exchange in embedded systems, serving various applications, from IoT devices to industrial automation. Among the popular choices, Bluetooth Low Energy (BLE) shines for low-power, short-range communication, making it ideal for wearables and smart home appliances. Similarly, Zigbee finds its place in applications requiring low data rates, minimal power consumption, and short-range connectivity, often used in industrial control and home automation systems.

Choosing the right protocol depends on application-specific factors such as power efficiency, communication range, data rate, and network architecture. Choice is key to building robust, reliable, and scalable solutions that meet the diverse needs of an expanding embedded systems landscape. These protocols work together to build intelligent and connected automotive embedded ecosystems, enabling features such as real-time traffic updates, and in-car entertainment.

Radio Frequency (RF) transceivers serve as the cornerstone of wireless communications within embedded systems. These devices combine both transmission and reception functions, enabling a two-way flow of data over the airwaves. RF transceivers are versatile, facilitating communication in various protocols and frequency bands like Bluetooth, WiFi, Zigbee, and more.

Now, let’s take a look at some of the common communication protocols and technologies you’ll find in embedded systems:

• Bluetooth

Bluetooth is a short-range wireless technology standard that is used for data exchange between devices over short distances. It uses UHF radio waves of frequency ranging from 2.4 to 2.485 GHz in the ISM (industrial, scientific, and medical) radio band. In the most widely used mode, transmission power is limited to 2.5 milliwatts, giving it a very short range of up to 10 meters. Data can be shared at a maximum data rate of 3 Mbps.

Bluetooth is mainly used as an alternative to wired connections to exchange files between nearby portable devices. That is why Bluetooth Low Energy (BLE) and Bluetooth Classic radios are designed to meet the unique needs of developers worldwide.

– Bluetooth Classic is the original version of Bluetooth technology, which was designed for high-bandwidth applications. Operating over 79 channels in the 2.4 GHz ISM (Industrial, Scientific, and Medical) frequency band, it enables devices like phones and headphones to form personal area networks (PANs) to transmit data over short distances. Bluetooth Classic has become important to daily life, particularly as the trend toward smartphone devices without headphone jacks continues. The process of connecting two devices via Classic Bluetooth is now a common skill.

– Bluetooth Low Energy (BLE) is a version of Bluetooth technology designed for very low-power operation. Transmitting data over 40 channels in the 2.4 GHz ISM frequency band, this version provides developers a tremendous amount of flexibility to build products that meet the unique connectivity requirements of their market. BLE devices can run on a coin cell battery for months or even years. Although originally known for its device communication capabilities, BLE is now also widely used as a device positioning technology to address the growing demand for high-precision indoor location services. It now includes features that allow one device to determine the presence, distance, and direction of another device.

• WiFi

WiFi (Wireless Fidelity) is the most popular IoT communication protocol for wireless local area networks that utilizes the IEEE 802.11 standard through 2.4 GHz UHF and 5 GHz ISM frequencies. 2.4 GHz WiFi can reach a maximum of 600 Mbps in ideal conditions, but in an average home network, a max speed of 150 Mbps is more likely. A 5 GHz WiFi connection can reach up to 1300 Mbps. 2.4 GHz WiFi can reach up to 46 meters indoors and 92 meters outdoors, meanwhile, 5 GHz frequency spans around one-third of the distances of 2.4 GHz WiFi. It has a data rate of up to 600 Mbps maximum, depending on the channel frequency used and the number of antennas. In embedded systems, ESP series controllers from Espressif are popular for building IoT-based Applications.

There are many development boards available that allow people to build IoT applications using WiFi, for example, Raspberry Pi and Node MCU, which allow people to build IoT prototypes and also can be used for small real-time applications.

• LoRa

LoRa (Long Range) is a wireless technology that offers long-range, low-power, and secure data transmission for M2M and IoT applications. LoRa is based on chirp spread spectrum modulation, which has low power characteristics and can be used for long-range communications.

LoRaWAN provides the ability to connect millions of devices with data rates ranging from 0.3 kbps to 50 kbps. The distance for LoRaWAN application is up to 5 km in urban areas and up to 15 km or more in rural areas. With hundreds of millions of devices connected to networks in more than 100 countries and growing, LoRa is creating a smarter planet.

• Cellular Networks

Embedded systems also leverage cellular networks for wireless communication. The final stage of connectivity is achieved by segmenting the comprehensive service area into several compact zones, each called a cell. This protocol is generally used for long-distance communications. The data of larger size and with higher speeds can be sent compared to other technologies. The fifth generation of cellular networks is 5G. Its frequencies are divided between the Sub-6 GHz range, which has been extended to cover potential new spectrum offerings from 410 MHz to 7125 MHz, and the mmWave range, which includes frequency bands from 24.25 GHz to 71 GHz. The trade-off for speed at mmWave frequencies is the limited range of about 600 meters, while Sub-6 GHz frequencies can cover up to 5 km.

• Z-Wave

Z-Wave, low-power RF, is a communication protocol designed for Home Automation products. Smart home products with Z-Wave inside work together, using just one app to connect and control your smart home from anywhere. While Z-Wave has a range of 100 meters in open air, building materials reduce that range, it is recommended to have a Z-Wave device roughly every 10 meters, or closer for maximum efficiency. The Z-Wave signal can hop roughly 180 meters, and Z-Wave networks can be linked together for even larger deployments. Each Z-Wave network can support up to 232 Z-Wave devices allowing you the flexibility to add as many devices as you’d like to make sure your Smart Home is working its hardest. The data packets are exchanged at data rates of 100 kbps maximum and the protocol operates at a frequency of 900 MHz in the ISM band.

• Zigbee

Zigbee, like Bluetooth but with a longer range, relies on a bridge to facilitate internet-bound data from devices, albeit with higher power consumption. Zigbee has a shorter range of about 10-20 meters indoors because it uses less power. This does dramatically increase battery life for Zigbee devices. The data rate to transfer data between communicated devices is around 250 Kbps. It has a large number of applications in technologies like M2M and IoT.

This wireless communication standard, operating on the IEEE 802.15.4 standard, specializes in serving IoT applications. It offers dependable communication with low data rates and power usage, making it an ideal choice for applications where prolonged battery life is crucial. Zigbee finds its niche in domains like home automation, industrial control, and smart energy management systems, catering to various IoT needs.

Point-To-Point vs Mesh Topology in IoT Networking

Point-to-Point Topology and Mesh Topology are two distinct network architectures employed in computer networking. These topologies differ significantly in terms of their structure, connectivity, scalability, fault tolerance, and implementation.

Mesh Topology is a type of networking where all nodes cooperate to distribute data amongst each other. Originally developed over 30 years ago for military applications, mesh networks are now commonly used for various applications, including home automation, smart HVAC control, and smart buildings. Industry standards that rely on mesh network Topology include Zigbee and Z-Wave.

Mesh Topology can be further categorized into two types: Full Mesh and Partial Mesh. In a Full Mesh Topology, every device has a direct link to every other device, creating a fully connected network. In a Partial Mesh Topology, only some devices have direct links to all other devices, while others have links to only a subset of devices.

One significant advantage of mesh Topology is that it has low transmit power and shorter links (<100 ft). This characteristic not only extends the battery life significantly but also facilitates efficient data movement across the network. The other advantage of mesh Topology is its ability to facilitate self-healing networks in the face of node failures. If one node goes down, alternative connections can be established, enhancing fault tolerance. The primary disadvantage of mesh Topology is that the range between two mesh nodes is quite limited, which means that you may have to add additional nodes into your network that aren’t strictly necessary.

While mesh networks are well-suited for connecting multiple devices, Point-to-Point Topology is employed to connect two specific endpoints. Point-to-Point Topology is a network configuration where two endpoints have a direct connection or link. Serial connections between two devices or a USB connection between a computer and a printer are examples of point-to-point connections.

The primary advantage of Point-to-Point Topology is its simplicity, as it involves a direct flow of data either unidirectional or bidirectional between two points. Point-to-Point networks are still relevant in certain applications such as SCADA systems, traffic data systems, or Point-to-Point broadcast systems like police or fire radios. However, they are less suitable for IoT applications, where connecting to multiple nodes is often more practical.

The Future of Wireless Communications in Embedded Systems

The future of wireless communication in embedded systems promises remarkable advancements and innovations. Here are some key aspects that will define the future landscape:

The integration of 5G networks is set to bring a transformative impact to embedded systems. 5G offers significantly higher data rates, lower latency, and the ability to connect massive numbers of devices, revolutionizing real-time applications, ultra-high-definition video streaming, autonomous systems, and large-scale IoT deployments.

Private 5G networks differ from public 5G networks in that they provide restricted access and utilize licensed or unlicensed wireless spectrum within a confined area, such as a manufacturing plant, port, airport, campus, or business park. This allows owners to tailor the network to specific needs and requirements.

The key differences between public and private 5G have to do with restricted access and isolation. Typically, the public 5G networks available through service providers offer equal access rights to all users, sometimes leading to degraded service performance. A higher service availability is fundamental to support always-on operations. 

A private 5G network offers greater control. Unlike public 5G, a private 5G network can be reconfigured to permit different levels of access when certain network activities are deemed more business-critical than others.

Open RAN is a revolutionary shift in mobile network design, allowing service providers to use components from different vendors. This open approach is guided by industry standards, enabling flexibility and innovation in the creation of mobile network equipment. In Open RAN, traditional components like remote radio heads and baseband units are replaced with disaggregated radio units, distributed units, and centralized units. These components can be virtualized or containerized, offering programmable, intelligent, and interoperable functions. The O-RAN Alliance, established in 2018, defines the standards for Open RAN, bringing together global stakeholders in telecommunications. Open RAN represents the future of wireless communication, emphasizing openness, flexibility, and collaboration among suppliers.

As the volume of data generated by embedded devices continues to surge, edge computing will play a pivotal role in optimizing wireless communication. By relocating computation and data processing closer to the network’s edge, embedded systems can reduce latency, enhance real-time decision-making, and alleviate bandwidth constraints.

The concept of mesh networking, where devices communicate with one another to form a network without relying on a centralized infrastructure, holds significant promise for embedded systems. Mesh networks provide increased reliability, scalability, and flexibility. They enable self-healing capabilities, allowing devices to reroute data and ensure continuous connectivity, even in cases of individual node failures.

Conclusion

Choosing the most suitable network connectivity option for your IoT project is a decision that hinges on your device requirements and the ultimate project goals. It’s a delicate balance, primarily involving trade-offs among power consumption, available bandwidth, and network coverage.

In specific scenarios, technologies like LoRa and LoRaWAN can prove to be fantastic choices. For other cases, options like WiFi or Ethernet might be clear and straightforward. Furthermore, modern cellular solutions, exemplified by innovations like Notecard, have brought global IoT connectivity into the realm of reality.

The IoT landscape continues to expand and evolve, offering a diverse range of connectivity options serving various applications. By carefully assessing your project’s unique demands and keeping the trade-offs in mind, you can make an informed choice that aligns perfectly with your IoT objectives.

Interested to know more? Get in touch with us for details.

 

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As technology evolves, the automotive industry is constantly seeking ways to make driving safe, reliable, and autonomous. In this blog post, we’ll explore the features, functionality, and the impact that a platform based on dual NVIDIA’s AGX Orin modules offers for the future of vehicle safety during operation. Additionally, we will elaborate on the concept of safety-critical systems and highlight the distinctions between safety-critical functionalities and ADAS (Advanced Driver Assistance System).

The Jetson AGX Orin is designed for advanced robotics and AI edge applications for manufacturing, logistics, retail, service, agriculture, smart city, healthcare, and life science. Dual Orin (2 Orin devices on the same motherboard) offers system redundancy, which refers to the presence of backup or duplicate components that can take over in the event of a failure in the primary system.

ADAS provides driver assistance and convenience, but it is not solely responsible for critical functions that impact safety. Safety-critical functions encompass components directly involved in critical functions such as braking and collision avoidance. Safety-critical systems follow strict standards to ensure reliable operation.

What is Orin?

The NVIDIA Jetson Orin solution is a SOM (system-on-module) with CPU, GPU, memory, power management, and various high-speed interfaces embedded on a single board. NVIDIA Jetson brings accelerated AI performance to the edge in a power-efficient and compact form factor. The Jetson family of modules all use the same NVIDIA CUDA-X™ software, and support cloud-native technologies like containerization and orchestration to build, deploy, and manage AI at the edge.

NVIDIA’s Orin platform (SoC) has three series for its Jetson products:

• Jetson AGX Orin series
• Jetson Orin NX series
• Jetson Orin Nano series

NVIDIA Jetson Orin modules provide 275 TOPS of AI performance and which increases the performance 8 times compared to Jetson Xavier for multiple concurrent AI inference pipelines, in addition to high-speed interface support for multiple sensors.

One of the major features of NVIDIA Jetson Orin is the DLA (Deep Learning Accelerator) which supports next-generation NVDLA 2.0 with 9x the performance of NVDLA 1.0. It enables the GPU to run more complex networks and dynamic tasks.

A Comparison of Orin with Traditional CPU/GPU

Now, let’s delve into a comparison between traditional processors and Orin by examining the following key features:

Architecture

NVIDIA Jetson Orin is designed specifically for autonomous machines and edge computing. Jetson AGX Orin modules feature the NVIDIA Orin SoC with a NVIDIA Ampere architecture GPU, Arm® Cortex®-A78AE CPU, next-generation deep learning and vision accelerators, and a video encoder and a video decoder making it highly optimized for tasks like computer vision, deep learning, and robotics.

Traditional CPUs (Central Processing Units) and GPUs (Graphics Processing Units) are more general-purpose processors designed for a wide range of computing tasks, including running operating systems, executing applications, and performing graphics rendering.

Power Efficiency

NVIDIA Jetson AGX Orin series modules are designed with a high-efficiency Power Management Integrated Circuit (PMIC), voltage regulators, and a power tree to optimize power efficiency. It strikes a balance between performance and energy consumption, allowing for longer battery life and reduced power requirements in embedded systems.

While traditional CPUs and GPUs can offer high computational power, they are generally more power-hungry compared to specialized SoCs like Jetson Orin. They are commonly found in desktops, servers, and workstations where power consumption is less constrained.

AI Performance

The NVIDIA Jetson AGX Orin series provides server class performance, delivering up to 275 TOPS of AI performance for powering and managing autonomous systems. Its high performance is ideal for tasks like object detection, image recognition, natural language processing, and autonomous navigation.

Traditional CPUs and GPUs can also handle AI workloads, but they do not provide the same level of performance or efficiency as AI-focused modules like Jetson Orin. GPUs, in particular, have been utilized for parallel processing in deep learning tasks, but they are less power-efficient compared to specialized AI chips.

In addition, the Jetson Orin modules are extremely compact, enabling the compute platform to have reduced size and weight – critical for autonomous robots and UAVs.

Software Ecosystem

NVIDIA Jetson Orin is part of NVIDIA’s Jetson platform, which offers a comprehensive software stack, including drivers, libraries, and frameworks specifically optimized for AI and autonomous applications. It supports popular AI frameworks like TensorFlow, PyTorch, and CUDA, providing developers with familiar tools and resources.

Traditional CPUs and GPUs also have a mature and extensive software ecosystem with support for a wide range of operating systems, development tools, and programming languages. They are compatible with various software frameworks, including those used for AI, but may require additional configuration and optimization for specific AI workloads.

Key differences between NVIDIA Orin and Xavier

NVIDIA Jetson AGX Xavier and NVIDIA Jetson AGX Orin have the same physical footprint and are pin compatible while also being in the same price range with one major difference that the Orin offers much higher performance.

The biggest change change is moving from Nvidia’s Carmel CPU clusters to the ARM Cortex-A78AE on Jeston AGX Orin.
The Orin CPU complex is made up of 12 2.2 GHz cores, each with 64KB Instruction L1 Cache and 64KB Data Cache, and 256 KB of L2 Cache. This enables x1.85 performance increased compared to the eight core Carmel CPU on Jetson AGX Xavier.

Jetson AGX Orin modules deliver an AI performance that can reach 275 TOPS with up to 64 GB of memory, compared to 32 TOPS with up to 32 GB of memory for Jetson Xavier.

Jetson AGX Orin 64GB has 2048 CUDA cores and 64 Tensor cores with up to 170 Sparse TOPS of INT8 Tensor compute, and up to 5.3 FP32 TFLOPs of CUDA compute, while Jetson Xavier has only up to 1.4 FP32 TFLOPs of CUDA compute. Ampere GPU brings support for sparsity, a fine-grained compute structure that doubles throughput and reduces memory usage.

DLA 2.0 provides a highly energy efficient architecture. With this new design, NVIDIA increased local buffering for even more efficiency and reduced DRAM bandwidth. DLA 2.0 additionally brings a set of new features including structured sparsity, depth wise convolution, and a hardware scheduler. This enables up to 105 INT8 Sparse TOPs total on Jetson AGX Orin DLAs compared with 11.4 INT8 Dense TOPS total on Jetson AGX Xavier DLAs.

The 12-core CPU on Jetson AGX Orin 64GB enables 1.85 times the performance compared to the 8-core NVIDIA Carmel CPU on Jetson AGX Xavier. Customers can use the enhanced capabilities of the Cortex-A78AE including the higher performance and enhanced cache to optimize their CPU implementations.

Jetson AGX Orin modules bring support for 1.5 times the memory bandwidth and 2 times the storage of Jetson AGX Xavier, enabling 32GB or 64GB of 256-bit LPDDR5 and 64 GB of eMMC. The DRAM supports a max clock speed of 3200 MHz, with 6400 Gbps per pin, enabling 204.8 GB/s of memory bandwidth.

The combination of NVIDIA’s processing capabilities and power efficiency, along with its safety-critical features, makes it the ideal solution for autonomous applications.

Safety Critical Software in Automotive Safety

Functional safety in processor-based systems is particularly critical in automotive applications. Apart from the ongoing shift towards autonomous vehicles, cars are increasingly dependent on microprocessors to carry out essential operations and must have redundant systems to enable safety in the event of a component failure.

ISO 26262 serves as the globally recognized standard for ensuring functional safety in the automotive industry. This international standard encompasses both the hardware and software components of a vehicle’s electrical and electronic (E/E) systems. Throughout the development process, ISO 26262 outlines specific requirements that must be fulfilled to ensure the safety-related functionality of the system, along with the corresponding processes, methodologies, and tools. By adhering to the ISO 26262 standard, manufacturers can ensure that sufficient safety measures are implemented and maintained throughout the entire lifespan of the vehicle.

ISO 26262 offers comprehensive guidelines on determining acceptable risk levels for systems or components and documenting the testing process. It encompasses the following key aspects:

• Defines an automotive safety lifecycle that covers management, development, production, operation, service, and decommissioning stages, allowing for customization of activities during each phase.
• Implements an automotive-specific risk-based approach for classifying risk levels known as Automotive Safety Integrity Levels (ASILs).
• Utilizes ASILs to specify the required safety measures for achieving an acceptable residual risk.
• Establishes requirements for validation and confirmation measures to ensure the attainment of a satisfactory level of safety.y

Dual AGX Orin Controller Overview

The Dual AGX Orin system offers superior computing power compared to a single Orin solution, making it preferable for specific applications that require higher computational power and redundancy.

The Dual Orin Controller’s computational capacity enables it to handle multiple complex tasks simultaneously. This capability is particularly valuable in scenarios where there is a need for concurrent processing of multiple data streams from various sensors, making it suitable for advanced autonomous machines, commercial vehicles, unmanned distribution vehicles, and unmanned cleaning vehicles.

In safety-critical applications, redundancy is essential to ensure system reliability. The Dual Orin Controller’s utilization of two AGX Orin modules provides a level of redundancy and failover capabilities. If one module encounters an issue, the other can continue functioning, minimizing the risk of critical system failures and improving the overall reliability of the autonomous machine.

Tauro Technologies TT300 Dual AGX Orin Controller

Tauro Technologies’ TT300 Dual AGX Orin compute platform provides exceptional computing power, low energy consumption, in a compact form factor.

With up to 400/550 TOPS of AI performance this product can be used in autonomous vehicles, UAVs and robotics. The product is designed for high reliability and redundancy, provides multi-sensor clock synchronization with sub-nanosecond accuracy and millisecond latency for precise timing.

Let’s take a closer look at TT300 key features:

Dual Orin Controllers 550 TOPS

The TT300 board is equipped with two powerful Orin controllers, delivering combined processing power of 550 TOPS. This immense computing power enables lightning-fast data processing and analysis, making it ideal for handling complex AI workloads.

Infineon TC397 Safety MCU

Ensuring the highest levels of safety and reliability, the TT300 board incorporates the Infineon TC397 safety microcontroller to support safety requirements up to ASIL-D. This MCU plays a crucial role in safeguarding the system against potential hazards and maintaining the integrity of critical operations.

100Base-T1/1000Base-T1 Ethernet

To facilitate efficient and reliable data communication, the TT300 board is equipped with both 100Base-T1 and 1000Base-T1 Ethernet interfaces. These interfaces enable fast and secure data transfer, ensuring smooth integration into existing vehicle network infrastructures.

Wi-Fi/4G/5G

TT300 board supports Wi-Fi, 4G LTE and 5G connectivity, enabling seamless wireless communication and remote access. Whether you need to stream data, receive updates, or control the board remotely, these connectivity features have you covered.

• GMSL2 Interface for Hi-Res Cameras

The TT300 board features a GMSL2 interface, enabling reliable connection with high-resolution cameras. This interface supports the transmission of data between the controller and cameras, ensuring high-quality image and video feed for AI applications such as ADAS, object detection, tracking, and recognition.

GMSL cameras are becoming a defacto standard in automotive industry where high data rates and long-distance support is required, addressing the need to transport higher video data rates in automotive video systems.
In addition to high bandwidth transmission, long-distance support, and low latency, GMSL cameras also come with the following features:

• Virtual channel support
• GMSL1 and GMSL2 backward compatibility
• Video duplication
• Automatic Repeat Request (ARQ) feature
• Compatibility with ARM platforms like the NVIDIA Jetson series

I/O Capabilities

TT300 is powered by two NVIDIA Jetson AGX Orin modules and Infineon TC397 safety MCU enables the design to meet ASIL-D highest reliability requirements. The I/O capabilities of the product include automotive as well as industrial ethernet interfaces, USB, wireless connectivity over 4G/5G and Wi-Fi, GMSL camera and LVDS radar interfaces for ADAS applications, as well as CAN and LIN interfaces for automotive and robotics applications routed to CMC connector. Wide selection of interfaces and customization options makes this device easily adaptable to various use cases and application scenarios.

Conclusion

Tauro Technologies’ TT300 is one of the industry’s first platforms to offer the NVIDIA Jetson Orin AGX in a redundant safety-critical setting. This is an ideal system for self-driving vehicles in automotive, mining, and defense sectors as well as autonomous robots and UAVs that require exceptional performance and functional safety certification.
We can customize the I/O as well as the product packaging to fit your application requirements – contact us for details.

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The demand for embedded systems across various industries and technology fields today is as high as ever before. Embedded systems are essential to many electronic devices and automated solutions that we are increasingly relying upon. So it comes as no surprise that the global embedded systems market is rapidly growing. According to a recent study, the total size of the embedded systems market is expected to reach $116.2bn by 2026 from $86.5bn last year, growing at a CAGR of 6.3% from 2021 to 2026. 

Even the COVID pandemic and global economic turbulence caused by this healthcare crisis weren’t able to disrupt the consistent growth of the embedded systems market. As the authors of an older market study noted, even though low demand for consumer electronic devices due to COVID lockdowns had its negative impact, it was balanced by the increasing need for various embedded hardware components for the healthcare industry. 

Six most interesting embedded systems industry trends in 2022 

The abundance of technological and market development trends is another sign that we have all the reasons to feel optimistic about the evolution of the embedded systems industry going forward. 

 Let’s take a closer look at some of the most interesting and noteworthy trends that, in our opinion, will influence the embedded systems industry in 2022 and over the next few years. 

Automotive industry driving embedded systems market growth

When it comes to the applications of embedded systems, the automotive industry today is one of the main drivers of market growth. This trend will most likely further increase in 2022 as well, fueled by continuously rising demand for electric and hybrid vehicles across the globe. The manufacturers of electric and hybrid vehicles rely on embedded systems in a variety of smart electronic components such as advanced driver-assistance systems (ADAS), power control units, engine cooling systems, etc. Additionally, automotive and mobile robotics industries are also rapidly adopting autonomous technologies and further require the integration of LIDAR, camera, sensor and power subsystems. All these components rely on embedded systems for centralization and coordination on processes. 

Explosive demand for military embedded systems 

As you may know, embedded systems play a vitally important role in many devices and electronic machine components used for military applications. The demand for weaponry and advanced military equipment has already been on the rise in recent years as a result of escalating regional tensions and geopolitical rivalry around the globe. And we can expect the boost in development of the military embedded systems market as most NATO countries are significantly increasing their defense budgets. Military embedded systems are used in land, sea, and air warfare theaters for a large variety of applications, including unmanned vehicles, counter UAV systems, surveillance systems, weapons guidance systems, communication equipment, command and control solutions, satellite communications, etc. 

Following the evolution of military systems, we can clearly see that not just the commercial sector companies are looking to implement AI, 5G, cloud computing, and other technological innovations. Many defense contractors are also interested in tech innovations as a way to produce more advanced systems. 

Wider AI and ML integration 

Artificial intelligence (AI) has been one of the most significant technology trends in recent years. AI and ML (machine learning) solutions continue to gain momentum and spread across a variety of industries and market segments. 

The embedded systems are not an exception, even though AI and ML solutions traditionally have been challenging to implement in embedded systems due to their hardware and framework limitations. But new hardware solutions along with innovative techniques used for inference processing, data curation and performance acceleration help to overcome these obstacles. In 2022, we expect to see even more new embedded implementations leveraging AI and ML technologies. 

NVIDIA products are widely used for training and inferencing applications in many AI systems, and Tauro Technologies has been building these systems from their early days. As the industry evolves, other silicon and software solutions are emerging that promise to offer better price–performance ratio for many machine vision applications.

Embedded security and defense against cyber threats 

Cyberattacks and information security breaches have been on the rise for a number of years now. And it’s not a secret that embedded systems are known to be vulnerable to hacker attacks and cybersecurity threats of various kinds. There are multiple reasons why embedded systems often fail to provide the appropriate level of protection against cyber threats: poor access control or authentication settings, no regular security updates, remote deployment, reliance on legacy hardware, etc. 

This is why the development of embedded security software and hardware is on the rise in recent years, as well as the standards for the security level in embedded hardware designs. Specifically, we have noticed a rise of embedded systems that implement TPM, AES encryption, and FIPS 140 technologies on hardware platforms.

 5G technologies and 5G-based embedded systems 

The ongoing deployment of 5G infrastructure is expected to be a major growth driver for a variety of technology fields, mainly telecommunications, industrial automation, internet of things (IoT), automotive, etc. The demand for embedded systems based on 5G architecture will also be increasing along with overall 5G implementation progress. Growing communications and processing speed, achieved with 5G architecture, without a doubt will be very helpful to solve the performance issues typical for embedded systems based on communication standards of previous generations.

Virtual and augmented reality with embedded systems

Virtual reality (VR) and augmented reality (AR) is another major tech industry niche that has been trending for a while, keeps gaining momentum year after year, and received an additional boost thanks to COVID pandemic and increasing global turbulence overall. VR/AR solutions have a wide range of cost-saving and efficiency-improving applications. Modern-day feature-rich virtual environments cannot function without complex high-performance embedded systems. They allow VR/AR solutions to match movements of the user with rendering of graphics, sound and text in real time. We have seen early applications of VR/AR in skills development and training both for industrial and military purposes, which is why we expect the demand for such complex VR/AR embedded systems to increase in 2022 as well. 

Final thoughts 

Some of the other notable embedded systems industry trends that we didn’t mention in this article are the rapidly growing real-time segment of the market, rising popularity of Python as the main programming language for embedded systems software, related IoT development trends, and more. 

What’s also worth mentioning, all six industry trends described above are connected and, in many ways, are fueling each other’s growth. For example, the demand for autonomous vehicles, robotics and AI technologies in commercial and military systems drives the demand for faster 5G communication that can enable the network speed required to fully implement these tech innovations.

Based on the foregoing, it is safe to say that the demand for embedded systems across market niches and applications will be on the rise at least for the next ten years or so. In this increasingly complex and competitive business environment, the importance of professional approach to IoT and embedded systems design starts to play an even more important role. 

The Tauro Technologies’ team of electronic engineers and designers has a proven track record of successfully designing custom hardware for various kinds of products in multiple technology fields. Drawing on the specific needs of our clients, we select and apply various engineering methods to electronic product development and manufacturing in order to achieve the desired result. Utilizing our in-house PCB assembly and debug expertise, we are able to build and evaluate your prototypes before high-volume manufacturing, rapidly and cost-efficiently. 

Interested to know more? Get in touch with us for details.

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