Organizations seek ways to optimize operations and gain competitive advantages as the industrial Internet of Things (IIoT) becomes more common. Combining edge computing and Industrial IoT offers such solutions.
What could business leaders gain by implementing these technologies? More importantly, what do they have to lose if they ignore them? Companies should consider implementing edge computing for several reasons to gain a competitive advantage.
The Value of Edge Computing for Industrial IoT Implementation
Edge computing moves data processing and analysis away from centralized systems and toward the network’s boundary. Instead of sending IoT-generated information from the factory floor to the cloud and back, it stores everything on-device or in nearby servers to perform necessary operations locally.
This technology is vital for digitalization because it makes deploying and managing an interconnected network of devices much more manageable. This may be why experts estimate its global market will reach approximately $140 billion by 2030, up from $12 billion in 2020. These figures represent a 1,066 percent increase in a single decade.
Edge computing’s value extends beyond possible financial gain. Facilities that leverage it could optimize their operations and resolve many implementation-related pain points. Those who ignore its potential will likely experience poorer success than originally envisioned.
Potential Industrial Applications for Edge Computing
Several potential industrial applications for edge computing and IIoT exist.
Generating Real-Time Insights
Sending information to the cloud and back for remote analysis requires tedious transfers, meaning delays happen frequently. Edge computing enables companies to process IIoT-generated information locally, allowing them to produce data-driven insights in real-time. This way, they don’t have to wait minutes or hours to receive critical details.
Leveraging Predictive Maintenance
Decision-makers can use the edge to monitor machine health in real-time instead of waiting until something breaks to repair it. Predictive maintenance can extend equipment life span and optimize performance, mitigating unplanned downtime.
Running Artificial Intelligence
Facilities adopting AI need a robust infrastructure since it is resource-intensive. They’d struggle to run their workloads on-site without powerful storage systems and computing resources. However, edge computing can significantly reduce latency and improve bandwidth.
Automating Industrial Systems
Automating industrial systems requires analyzing large datasets. Companies that leverage edge computing for IIoT can reduce processing delays and improve equipment performance, enabling them to automate more extensively.
Managing Assets Remotely
Combining edge computing and IIoT enables business leaders to remotely monitor equipment in real-time. Without local processing power, their updates would be somewhat delayed — which isn’t ideal when dealing with assets like an autonomous fleet. A few seconds could mean the difference between smooth operations and a critical failure in these situations.
Why Ignoring Edge Computing Jeopardizes IIoT Success
Decision-makers should understand that ignoring edge computing could jeopardize their IIoT implementation and utilization success. As their company’s internet-connected devices grow, so does the strain on infrastructure and computing resources. Standard IoT technology won’t be able to handle it and will perform slower as a result.
The amount of IoT-generated data is increasing at an unprecedented rate. Experts estimate it will reach 79.4 zettabytes — the equivalent of nearly 80 trillion gigabytes — by 2025. Business leaders must recognize this growth as a potential obstacle. Unless they leverage edge technology, they risk having too much information to process or analyze in time.
Smaller companies — or those with small-scale IIoT infrastructure — should still be concerned about volume. After all, organizations use less than 20 percent of the information they generate due to latency challenges and transfer expenses. Edge computing could resolve both of these issues, enabling them to leverage data-driven decision-making fully.
Security is another reason why ignoring edge computing could hamper facilities’ IIoT success. Industrial sectors embracing digitalization are becoming larger targets for cybercriminals. Unfortunately, standard IoT defenses are lackluster — these internet-connected devices are vulnerable to man-in-the-middle and distributed denial-of-service attacks.
Since edge computing moves processing and analysis on-device instead of in the cloud, attackers are prevented from launching these attacks during data transfers. Moreover, securing devices locally is easier because it gives cybersecurity professionals greater visibility and control. This way, they can protect employees using wearables and workplaces using IIoT.
Competitiveness is also a driver for IIoT success that decision-makers may lose out on if they choose not to combine edge computing and IIoT. Early adoption would likely grant them an edge, giving them a vital advantage during a critical period of industrywide digitalization.
The Benefits of Embracing Edge Computing and IIoT
Edge computing substantially improves processing speeds because it doesn’t require lengthy transfers. It lowers end-to-end latency to 10 milliseconds, down from 250 milliseconds, compared to device-to-cloud speeds. This time adds up quickly in a large-scale IIoT infrastructure, ensuring companies receive their insights significantly faster.
Bandwidth optimization offers a similar benefit. Processing information on local devices reduces the volume of data transfers, significantly lowering bandwidth usage and making network operations more efficient. As a result, downloading, sending, and receiving are streamlined, reducing delays and performance issues.
While businesses can still rely on the cloud for its scalability and ease of use, they’re no longer forced to. Collecting, processing, and transferring information at the network’s border provides greater flexibility and granular control over IIoT-generated information. Leaders can be selective with implementation.
Data residency is another benefit of leveraging edge computing and IIoT. Laws like the European Union’s General Data Protection Regulation require companies to follow strict security practices if they operate in or use information from a certain place. Local processing offers a loophole, enabling them to reduce their compliance limitations.
The Bottom Line
Combining edge computing and Industrial IoT could streamline data analysis, optimize computational resource usage, improve device security, and create new business opportunities. Decision-makers who ignore these technologies may find themselves underperforming or overspending compared to their competitors.
Implementation alone doesn’t guarantee success. Business leaders must consider how to strategically deploy their IoT infrastructure alongside their edge technologies to make the biggest impact.
They should consider recording their baseline and evaluating their growth to identify and resolve implementation-related gaps early on. This way, they can make the most of their investment.
