Edge computing is quickly finding its way into a variety of industries as internet of things (IoT) devices become more commonplace. One of the most promising edge computing use cases is in the industrial manufacturing sector, where new technologies could potentially lead to massive productivity gains.
Although industrial automation has been taking place for decades, annual productivity gains have been relatively small since the 1980s and 1990s. The average factory in the United States is 25 years old and filled with machinery that’s almost a decade old. As companies look at investment opportunities and areas to upgrade, a new generation of smart machines powered by edge computing are becoming appealing options for those hoping to bring their operations into the digital age.
Traditional cloud networks form a centralized system for collecting and processing data. Information is gathered at the edge of the network by connected devices and transmitted back to a central cloud server. The data is then processed by the server’s computing resources and sorted and stored for later use. In some cases, the server delivers instructions back to the device on the edge of the network.
Cloud-based computing involves a lot of data in motion. All of that data moving across limited bandwidth channels combines with the delays resulting from the distance it has to travel to produce latency, which slows the whole system down. In some cases, delays are minor inconveniences. In others, they can cause serious problems. A self-driving car can’t afford to wait for several milliseconds for the cloud to analyze its sensor data and tell it to stop before it runs a red light.
Edge computing is a distributed, open-ended architecture that decentralizes the processing load. Rather than transmitting all data gathered on the edge of the network, devices process the data locally and much closer to the source. For devices that need to make rapid decisions, processing data locally allows them to respond much faster. Devices that merely collect data can still reduce the load on networks by analyzing data locally and transmitting only relevant information back to the cloud server.
To give one of the simpler edge computing use cases, imagine a camera recording cars driving down a toll road. In a cloud computing architecture, the camera takes a picture of a car’s license plate and transmits the entire photo back to the cloud, where a program processes the image, identifies the license plate number, and records that number into a billing system to issue the toll to the vehicle owner. Under this arrangement, there’s a lot of data being transmitted through the network due to all the images being delivered.
In edge computing applications, the camera processes the image immediately, identifies the plate number, and transmits only that number back to the cloud to begin the billing process. Far less data is flowing through the network, which frees up bandwidth for other applications. It also allows the camera to continue to analyze data should its connection with the server go down for any reason.
The potential edge computing use cases for industries are significant. Edge computing can greatly reduce the complexity of interconnected systems, making it easier to collect and analyze data in real time. It can also allow devices to gather critical information in remote sites where network connectivity is inconsistent or not cost effective. Data can be gathered and analyzed locally, with only critical information being transmitted back to the central network when connections are possible. The combination of edge computing and IoT devices will make it easier to streamline industrial processes, optimize supply chains, and create the “smart” factory.
Edge computing companies will enable industrial equipment to make autonomous decisions without human intervention. Sensor data can monitor the condition of machinery, speeding up or slowing down operations to optimize usage. Smart factories equipped with motion, temperature, and climate sensors can adjust lighting, cooling, and other environmental controls to make the most efficient use of power. This is just one of a range of IoT edge computing examples. Predictive analytics can identify when components are about to fail, ensuring that they can be replaced with minimal loss of productivity.
For companies expanding operations or starting up new manufacturing ventures, the decentralized nature of edge computing applications can greatly reduce startup time and costs. Smart machines will be able to function without the assistance of a massive, central data center running cloud-based applications. Since data can be gathered and analyzed locally, mobile equipment can be set up on-site with a minimal data infrastructure footprint, which will help to shorten supply chains and create opportunities in otherwise hard to access markets.
Edge computing also forms the backbone of the machine learning network that makes automatic manufacturing driven by robotics possible. Robots gathering and transmitting data through an edge network can identify irregularities and eliminate inefficiencies much more quickly than they could through a cloud-based architecture. The distributed nature of this system also makes it much more robust, ensuring better levels of uptime productivity.Industrial manufacturing is on the brink of a revolution and represents one of the most exciting edge computing use cases. Combined with a new generation of smart IoT devices, edge computing applications will completely transform manufacturing in the coming decades to drive better efficiency and productivity while also controlling c