Edge computing is about processing data as close to the source as possible, which reduces both latency and bandwidth use. This concept is seen as critical for furthering the Internet of Things and for driving the development of autonomous vehicles.
What is edge computing?
Edge computing is a decentralized approach to computing applied to networks (the opposite to cloud computing’s centralized approach). The concept relates to how a network stores its information. In edge computing, most data on a network is moved away from physical computers. For businesses, data is moved onto a private server.
Edge computing is especially useful in cases where a lot of data is generated. The approach allows for the successful triage of data locally so that some of it is processed locally, reducing the backhaul traffic to the central data repository. This is very useful in cases where many devices are connected together, as with the Internet of Things.
Edge computing helps to make the Industrial Internet of Things possible. This is an area of great value. McKinsey & Co. calculate that the Industrial Internet of Things will generate $7.5 trillion in value by 2025. The advantages here are to connect people to machine data that accelerate digital industrial transformation.
How can edge computing benefit business?
The advantages of edge computing are that it takes less time to move data and there are fewer are less hardware limitations and that hardware limitations are easily addressed. With conventional storage systems, hardware is normally required, and this can create a bottleneck that places a restriction on how much memory can be moved at any time point. The use of hardware also leads to slower data transfer speeds.
Furthermore, the costs of operating and maintaining the hardware are relatively more expensive.
Security is also stronger with edge computing, making edge computing systems harder for hackers to penetrate. This is because data is continually moving between network modes.
When data are moved throughout a network, they go through different security layers to ensure hackers cannot get into the system, but edge computing goes beyond this. More security layers are used because, instead of the data moving between the network nodes, the data moves from the Internet into the servers and onto the nodes. This provides an opportunity for creating additional firewalls and antivirus scans.
How are businesses using edge computing?
Businesses can derive many advantages from the edge computing concept. The edge process enables analytics and data gathering to occur at the source of the data. This enables companies to leverage resources from devices that are not necessarily continuously connected to a network like laptops, smartphones, tablets and sensors.
Autonomous vehicles and edge computing
Among the more specific examples is autonomous car technology. These are, in a sense, datacenters on wheels, and here edge computing plays a key role. To collect the high volumes of data, edge computing provides an advantage. In terms of data, Intel estimates that autonomous cars, with their many on-vehicle sensors, generate over 40 terabytes of data for each eight hours of driving. Given that this level of data cannot be easily sent to a cloud (and this also presents a safety risk in terms of delayed reactions), the use of edge computing becomes a necessity.
Security cameras and edge computing
A second example is with security systems. If a large complex is served by dozens of high-definition Internet of Things video cameras where data is continuously streaming that signal to a cloud server, these systems can be slow to respond. This is especially so if the security protocol is designed to respond to motion-detection. This set-up places a major strain on the building’s Internet infrastructure, with a high proportion of the bandwidth becoming consumed by a high volume of video footage.
With the edge concept, each camera would have an independent internal computer to run the motion-detecting application and then sent footage to the cloud server as needed. This improves efficiency and lowers bandwidth use.
Fleet management and edge computing
Edge computing also helps to improve the efficiency of fleet management. While a large volume of key performance data needs to be collected – wheels, brakes, battery, electrical – where such data requires a response, such as a potential brake failure, then some of this data needs to be collected and stored locally on the edge in order to minimize the risk of vehicle breakdown or accident.
An example of edge computing applied to fleet management is with trailer temperature. With most fleet monitoring systems, only temperature readings that are outside of a set range are reported back to fleet managers through telematics. The fleet manager then needs to assess whether or not there is a problem. However, with edge analytics, temperature readings can be analyzed onboard a vehicle and notified to the driver, empowering the driver to take steps to mitigate the temperature fluctuation.
