# Edge Computing
Edge Computing is Crucial for IIoT
Fog and edge computing will be key to the widespread and well-managed deployment of IoT. They both bring data processing closer to the edge of the network, where the data originates. For a breakdown on the differences between these two types of computing read our blog: Fog vs Edge Computing: What are the differences and do they matter?
Lower Latency Leads to Increased Safety and Precision
Many industrial IoT applications require incredibly low latency to function safely. By processing information closer to the device it slows down the time required. If data needs to be sent to the cloud, processed, and an action returned, then valuable seconds can be lost. Some mechanical processes may be incredibly delicate and detailed. In these cases, low latency is absolutely crucial.
A Reduction In Data Sent Leads to Improved Security
By processing more of the information at or near the edge of the network less of that data is sent over the public internet to the cloud. Less data being sent to, and stored in, the cloud means less risk of that data being hijacked. Industrial IoT can be particularly vulnerable to attacks due to the value of either seizing data or disrupting manufacturing processes.
Legacy equipment being retrofitted with more advanced IoT components can add additional risk. These older devices were designed to only be operated through internal network connections. Due to this authentication security controls were negligible, or often absent entirely. These legacy systems are rife with vulnerabilities which could be exploited now that they are connected to the internet.
Processing data at the edge and only sending small packets to the cloud will decrease the security risk of IIoT applications. Although, of course, it’s still important to ensure that any IIoT deployment is as safe as possible and to update legacy equipment with the latest security.
One of the most effective ways to do this is to secure the network. Many IoT devices have a very basic interface, which makes simple security measures such as changing your password or updating the operating system difficult. These devices have limited computing power; they are often not able to support antivirus software or two-factor authentications. By securing the network you can secure IoT devices which would not be able to support sufficient themselves. It also makes scalability easier and more economical. Once the network is secure it’s not important whether you have 10 IoT devices or 10,000.
Decisions Can Be Made Faster
Many IIoT applications deploy sensors. These include proximity, infrared, piezo (pressure), temperature, optical and image sensors. They may be constantly monitoring but there is no reason for them to be sending all that data to the cloud. If the reading shows that the level is within the required parameters and there’s no action that needs to be taken then there’s no reason for this information to be sent to a central cloud server.
With edge or fog computing this data can be analyzed close to the edge of the network and only sent further up the chain if there is something out of the ordinary. Or if the fix is a simple matter of adjusting temperature controls then this action can be taken at the edge without any requirement to send information anywhere. This makes decisions simpler, quicker, safer and allows them to continue uninterrupted even if there is a loss of connection.
A Reduction in Costs
Another advantage of sending less data to the cloud is a reduction in costs. For the same reasons stated above, if you are monitoring something and there is no action necessary then this information does not need to be sent to the cloud. Sending, for instance, video to the cloud uses a huge amount of data. If you can avoid doing that then you’ll save bandwidth and money.
Edge computing can also be beneficial for industries operating where bandwidth is low or non-existent. Offshore oil rigs can utilize edge computing to gather, monitor, and process data without having to depend upon a data center located far away.
This will be very useful in the energy sector as well. New York-based renewable energy company Envision manages 20,000 wind turbines which, between them, have 3 million sensors. It can process as much as 20 terabytes of data at a time and previously data analysis time could be 10 minutes. By using fog computing the company has reduced this to mere seconds. With this, they’ve achieved a 15% productivity improvement. Definitely something worth attempting to replicate.
By processing data closer to the edge of the network industry can improve productivity, reduce costs, decrease security risks and begin to embrace the full potential of Industry 4.0.