Navigating the mega-trends reshaping industrial IoT

By Jeremiah Stanek, Segment Marketing Manager, Ezurio

As more devices come online and more data is processed, engineers are faced with new challenges. What was once considered cutting-edge smart technology is becoming the standard, and new trends are transforming the way manufacturing, automation, and logistics operate.

Product engineers play a critical role in this transformation. These emerging mega-trends in IIoT will drive new strategies for design, implementation, and optimisation. From robotics and artificial intelligence (AI) to the rise of autonomous manufacturing environments, engineers need to rethink their strategies to stay competitive. In this article, we’ll dive into the biggest trends that are reshaping the future of IIoT and explore what engineers need to know to stay ahead of the curve.

1. The growth of AI/machine learning powered robotics

Robotics have been a part of the industrial landscape for decades now, performing pre-programmed tasks like assembly line work, welding, and material handling. However, the next generation of robots, powered by AI and machine learning, are now capable of analysing data in real-time, learning from their environments, and adapting to improve performance. This opens up new possibilities to autonomously navigate facilities to track inventory, adjust production processes based on sensor data, and even collaborate with other machines and robots.

What engineers need to know:

• Wireless technologies: as robots become more autonomous and collaborative, wireless communication will be critical for real-time data exchange and communication. These robots utilise the latest Wi-Fi 6/6E and 5G technologies to ensure high bandwidth, low-latency connectivity to instantly share sensor data, receive instructions, and collaborate with other devices
• Edge computing: to meet the real-time demands of IIoT, engineers must enable their devices to perform Edge computing, allowing robots and other devices to process data and make decisions locally rather than relying solely on centralised processing in the Cloud. This reduces latency and
  improves decision-making speed

Key takeaway: engineers need to focus on optimising wireless technologies for low-latency, high-bandwidth communication, especially as the number of IIoT devices and their interconnectivity continues to grow. Additionally, allowing devices to perform their own processing and decision making can cut latency by centralising local evaluation and intelligence on the device itself.

2. Predictive maintenance becomes standard practice

Predictive maintenance allows industries to shift from a reactive to proactive repair strategy based on real-time sensor data. This is driven by advancements in wireless technologies, sensors, data analytics, and machine learning models that predict equipment failure before it occurs. Integrating this technology can be difficult due to the complexity of designing systems that need to continuously monitor and analyse large amounts of data with uninterrupted connectivity.

What engineers need to know:

• Wireless and support: selecting the right wireless technologies to have data flow from sensors to Cloud-based or Edge computing platforms is important, but device management is needed in order for predictive maintenance initiatives to be successful. Engineers need to build systems that support over-the-air (OTA) updates, enable remote configuration, and provide real-time monitoring of device health and performance
• Futureproofing: devices and networks need to be designed for scalability and adaptability, allowing systems to add more devices as the facility changes. This includes making sure that hardware and software can handle future protocols and technology upgrades

Key takeaway: aside from choosing the right sensors and field devices, there is additional need to support security, scalability, and device management to handle the increasing numbers of IIoT devices, while supporting remote updates and continuous monitoring. Choosing hardware that’s upgradeable over a long-term roadmap can help OEMs keep their devices fresh and current as new wireless standards and compute capabilities emerge.

3. IoT becomes mission critical

As IoT becomes deeply ingrained into industrial facilities, it has moved from a supplementary tool to an essential one. IoT devices are now critical for real-time data collection, process automation, and decision-making in sectors like manufacturing, warehousing, and logistics. The ability to gather and act on data from connected devices is game-changing and allows for smooth, uninterrupted processes.

What engineers need to know:

• Low-latency networks: many IIoT applications, like factory automation and safety monitoring, require instant data transmission to prevent any disruptions. For example, in a chemical processing plant, sensors monitor pressure, temperature, and gas levels to detect any dangerous fluctuations. A delay in this data could lead to equipment failure or safety risks. Using the latest wireless technologies, such as Wi-Fi 6, offers increased bandwidth, lower latency, and ability to handle simultaneous device connections for these critical applications, even in crowded environments with hundreds of devices
• Scalable network design: as industrial environments use more connected devices, engineers must build scalable networks capable of handling these additional devices. Working with a connectivity partner that can provide the necessary testing, support, and compliance is key to avoid any strategic or design mistakes

Key takeaway: partnering with reliable connectivity experts is crucial to addressing the challenges of new and increasing device ecosystems while maintaining real-time performance and security. And utilising pre-built, pre-certified, ruggedised wireless modules and SOMs can accelerate IoT initiatives while also ensuring future proofing of IoT devices in industrial environments.

4. Autonomous manufacturing on the rise

Manufacturing once relied on human oversight and input for line-level production adjustments. Now, these systems communicate seamlessly to adjust production in real-time. This shift from manual to autonomous requires a robust IIoT infrastructure to monitor, control, and improve processes, making connectivity and data management vital for successful use.

What engineers need to know:

• Interoperability: often times, machines from multiple manufacturers are installed across the factory floor, which forces engineers to design for open standards and compatibility. Integration issues are common across devices and systems, especially when new technologies are introduced
• Security considerations: as autonomous systems become more interconnected, they become more vulnerable to cyber threats. It’s critical to implement strong security protocols, such as encrypted communications and real-time threat detection, to protect data from breaches and to stop bad actors from hijacking devices. Engineers must think ahead, not only defending against current risks, but also adapting to new threats, since a single vulnerability can bring an entire facility to a halt

Key takeaway: as previously discussed, while adding connectivity for real-time processing is important, interoperability and security cannot be overlooked. Working with experienced partners who can scale, future-proof, and secure your products while ensuring adaptability will help avoid potential troubles to production.

5. IoT in warehousing

The rise of e-commerce and demand for fast delivery is reshaping the way warehouses operate. Smart warehouses rely on IoT devices to track inventory, manage logistics, and improve workflow. As IoT becomes increasingly used in warehousing, networks have to accommodate higher volumes of data while keeping seamless communication between devices, such as robotics and other peripheral devices.

What engineers need to know:

• Network bandwidth: warehouses are filled with many connected devices, from sensors to tablets, and even personal devices, all of which crowd the 2.4GHz band. Since both Wi-Fi and Bluetooth commonly operate in this frequency, this leads to congestion, reduced performance, and slower data transfer. By utilising the 5GHz and new 6GHz spectrums, engineers can offload traffic from the crowded 2.4GHz band, increasing available bandwidth and reducing interference
• Lifecycle support: as IoT devices grow in scale, engineers need to prioritise full lifecycle support for their systems. This involves designing with long-term availability in mind, making sure the hardware, software, and firmware can be updated and maintained over time. Engineers should work with partners that offer lifecycle management, including firmware updates, technical support, and regulatory approvals/certifications

Key takeaway: when designing a system, it’s critical to address current requirements while also planning for future data volumes and device communication. Engineers must build networks with scalability in mind, leveraging wireless protocols and strong security measures to meet both current and
future IIoT demands.

6. AI and ML: the future of data-driven insights

We already covered how AI and machine learning are transforming robotics, but their impact goes far beyond that, particularly in the way data is analysed. As the number of IoT devices used in facilities grows, AI and machine learning process these large datasets in real-time, giving information that helps decision-making and pattern recognition. These technologies not only streamline operations but can anticipate future trends based on continuous data analysis, making a smarter, more efficient and forward-looking system. For example, in a manufacturing plant, AI can analyse data from hundreds of IoT sensors monitoring equipment performance. AI can analyse production data over time to identify inefficiencies and adjust to improve future performance.

What engineers need to know:

• Data management and optimisation: AI and machine learning need to handle large datasets efficiently. Engineers need to work closely with data scientists to develop algorithms capable of processing industrial data at scale. The focus needs to be on smooth and secure data flow while avoiding bottlenecks that could damage AI-driven analysis
• Edge and Cloud integration: AI and machine learning often use a hybrid approach, with some data processed locally at the Edge for real-time decision-making, while other data is sent to the Cloud for deeper analysis. A well-designed system can prioritise high-priority tasks to be handled locally and send the rest out for large-scale processing in the Cloud

Key takeaway: AI and machine learning are changing IIoT, but their success depends on reliable connectivity, Edge and Cloud integration, and data handling. Designing a system that is scalable, future-proof, and capable of supporting new demands is key to building a data-driven AI system.

Concluding thoughts

As Industrial IoT devices progress, engineers need to adapt to the rapid changes driven by AI, machine learning, and autonomous systems. To avoid strategic mistakes, they must focus on future-proofing designs with scalable networks, robust connectivity, and integration of Edge and Cloud technologies. Above all, they must do this with security in mind to safeguard systems and protect vulnerable processes. By adopting these trends and preparing for increased data demands, engineers can guarantee their systems remain adaptable and ready for tomorrow’s IoT landscape.

This article originally appeared in the December'24 magazine issue of Electronic Specifier Design – see ES's Magazine Archives for more featured publications.