With increased remote work, experts predict the use of IIoT technology in automation, wireless connectivity and AI algorithms will continue to grow across industries in 2021.
Organizations in many industries implement industrial IoT products, but with the onset of the COVID-19 pandemic, they might accelerate the adoption of some IIoT trends.
IIoT is often part of a strategy to increase the efficiency and automation in manufacturing. The development of embedded connected devices with sophisticated domain-specific microprocessor capabilities that can support real-time industrial processing make IIoT possible.
These capabilities may include support for machine learning applications used in real-time data processing in industrial settings, such as factories, warehouses and shipping vehicles. IIoT devices often integrate various sensors plus processing, network and memory components. These sensors include various types of cameras, temperature or pressure sensors, or humidity and gas detectors.
IIoT installations can serve as integral parts of a facility and, generally, are self-sufficient. As IIoT installations become more common, standards should emerge. Continual advances in nano-technology, microprocessors, networking, memory and storage make future IIoT implementations more sophisticated and provide greater levels of control. IIoT will be necessary to make many future products and serves as an essential element for organizations to remain competitive and optimize manufacturing.
As IT teams consider the pandemic and strategic planning for a new year, experts predicted organizations will follow three trends.
IIoT will be necessary to make many future products and serve as an essential element for organizations to remain competitive and optimize manufacturing.
1. Increasing automation
IIoT implementation and a move to greater factory and warehouse automation is a strategy component to decrease the number of people needed in a manufacturing or warehouse facility and, thus, their exposure to possible infection or, in some cases, to reduce human contamination sources.
Many semiconductor manufacturing facilities already implement IIoT devices to remove people from the manufacturing floor and prevent contamination of wafers, thin slices of a semiconductor or substrate material used in microelectronic devices.
2. Greater use of wireless connectivity options
Organizations typically connect a factory or facility management system with wired Ethernet or local Wi-Fi. For some use cases, such as tracking product shipments, organizations are increasing use of advanced wireless networks such as 4G or 5G to connect with IIoT devices in transit. The network's capability depends on the type of communicated data. Video streaming from a camera requires a lot of bandwidth, while simple machine control data requires very little bandwidth. IIoT applications in factories can use local power outlets, but mobile applications will likely run on batteries or harvested power. For power-constrained applications, organizations need low-power electronics that can limit the use of electrical energy.
3. Growing AI adoption
Organizations adopt various AI algorithms for many IIoT applications to make real-time decisions. This IIoT trend will continue to grow with data scientists training algorithms in larger data centers and in the cloud, but some degree of training is possible in the field. After training the AI model, an organization can implement the algorithm on domain-specific processors with inference engines embedded in the IIoT devices locally in an industrial facility to perform functions, such as image, character and voice recognition.
Industrial organizations can then use the AI algorithm to make real-time decisions, such as control a manufacturing process or assembly robots, transport parts, move products to be shipped, track a shipment, monitor manufacturing processes and call for maintenance. IIoT devices can offer facility security to detect infrastructure or equipment intrusion or tampering.
Training AI models that run on IIoT devices requires data sets that represent the actual operating ecosystem. Similar to consumer-facing AI, whoever curates the training data set must find and eliminate potential sources of bias, which could lead an AI algorithm to make the wrong decision and cause problems.
AI implementation in an IIoT-equipped facility likely requires experts' assistance to install and certify the application and occasional assistance to update and modify the application as the facility or products change.
