As the Internet of Things (IoT) evolves, the volume of data collected by sensors increases exponentially, and processing capabilities must scale to accommodate.
The amount of data produced by modern IoT systems is phenomenal: IBM claims that the average oil rig generates 2TB of data daily from 80,000 sensors, and advanced self-driving cars could generate 40TB per hour.
Building systems that generate large amounts of data is all very well, but that data needs to be processed and analyzed. IoT data is typically fed back to a central cloud or data center, but this introduces latency bottlenecks that make ultra-fast systems difficult. By contrast, edge computing moves processing closer to where the data is collected, accelerating processing speeds and reducing system latency.
Edge computing is unlocking new opportunities to process ultra-high-bandwidth IoT data with ultra-low latencies. So what benefits does Edge IoT offer? And is now the right time for companies to invest?
Pushing data processing to the limit
Business investment in edge computing is skyrocketing, with an expected CAGR of 37% over the next 5 years. Gartner predicts that around 75% of enterprise data will be processed at the edge by 2025.
So what is edge computing in the context of IoT? In a conventional IoT system, sensors send raw data to a cloud or data center, which processes the data and sends a response back to the device if necessary.
This entire process typically takes less than a second, but factors such as slow internet connections and server response time can affect latency, especially if the data requires complex processing and analysis, for example with AI models.
Also, Internet connections aren’t as reliable as we’d like, and high-speed coverage can be patchy. And then companies rely heavily on public cloud providers if they’re handling business-critical IoT data.
Edge computing solves some of these problems by physically processing data closer to where it is collected, reducing or eliminating the need for external processing.
IoT combined with edge computing is intrinsic to modern low-latency technologies that reliably handle complex data in milliseconds.
How companies benefit from the IoT edge
1: Latency
Edge computing saves time and optimizes resources. By time, we’re talking milliseconds, but if a driverless car hurtles toward a cyclist at 60 mph, every millisecond counts.
AVs must equal (or preferably exceed) our own biological nervous system’s reaction time of approximately 100ms to be safe. In that short period of time, the sensors must deliver complex data to decision-making models that return the required results to the throttles, steering systems, etc. In high-stakes scenarios that require split-second decision making, you can’t rely on server-side processing.
Ultra-low latency performance is also required for Industry 4.0 applications, such as instantly triggering alerts once sensitive equipment shows signs of impending failure. The same is true for other security alert systems that require lightning-fast processing.
2: scalability
As businesses equip themselves with more IoT sensors, the load placed on endpoints increases exponentially.
Cloud storage involves constant and ongoing costs that don’t always scale economically. On the other hand, lightweight edge options like NVIDIA’s Jetson module, an edge AI device capable of performing 21 trillion operations per second, cost only $500 or so.
While edge computing involves upfront costs, running complex workloads solely on cloud architecture can be more expensive in some scenarios.
3: Security
IoT presents security concerns around the collection and transport of sensitive data over vulnerable networks.
While edge computing still relies on servers that are vulnerable to hacking attempts, it benefits from being more localized, which helps with data control and security assurance.
Additionally, edge devices can transform and discard data before it reaches a network, and local processing reduces the amount of data exchanged wirelessly, reducing the potential for interception.
Additionally, edge devices process IoT data on-premises, circumventing some of the regulatory complexities of data storage and transfer. For example, BMW uses edge devices to process video data on-site without the risk of moving it to the cloud.
Combining IoT and edge devices
IoT platforms such as PTC ThingWorx, Microsoft Azure IoT, Hitachi Lumada, and Cumulocity by Software AG have already implemented cutting-edge services and solutions for customers and customers.
Companies must determine which IoT workloads are worth augmenting with cutting-edge techniques.
There are a few things to consider:
- Location: Edge IoT suits use cases where connectivity is spotty or low latency processing is paramount (or both). For example, a ship or oil rig may lack a reliable connection to a cloud or data center, requiring processing at the edge to harness IoT data for more than just monitoring. An IoT sensor connected to an artificial intelligence system could optimize technology in situ on the oil rig.
- Control logic locally: Edge IoT allows companies to control the logic close to the technology. For example, an autonomous vehicle needs to make ultra-fast decisions without relying on responses from AI models deployed in the cloud.
- Integration with existing systems: Edge Computing can fit into existing IoT infrastructure. Businesses can prioritize the systems that are most likely to benefit from edge computing and scale up the requirements as they realize the benefits.
The ability for edge devices to “snap” into existing infrastructure, including legacy systems, is proving to be an advantage for adoption.
It’s relatively simple for enterprises to test the waters by deploying edge devices on high-priority applications, measure the benefits, and adjust accordingly. Edge IoT solves many problems associated with processing large amounts of complex data and using it to gain insight or make decisions with low latency.
With edge-enabled IoT, enterprises can build ultra-fast systems responsive to the millisecond while solving security and regulatory issues and reducing dependency and burden on cloud architecture.
