The development of fog computing has had a transformative impact on enhancing edge intelligence. In particular, it has significantly improved data processing in hybrid IT architectures. Here is a quick guide to what you need to know about it.
Hybrid IT is a form of computing architecture that combines both real-world and virtual infrastructure. Initially, the hybrid IT landscape was very much dominated by centralized processing solutions. These are still very important today. Now, however, businesses are increasingly realizing the value of balancing centralization with localized processing.
One of the main drivers behind this change is the desire to minimize data-transfer speed. As the distance between end users and processing sites reduces, so does the time needed for data to travel between the two points. This means that services and applications can work more quickly.
In some cases, this speed boost is mainly a convenience. In others, however, it’s a necessity. For example, many internet-of-things (IoT) applications only deliver meaningful value when the data they use is processed in real-time (or very close to it). This has led increasing numbers of businesses to implement solutions that move processing closer to end users.
Before you can understand fog computing, you need to understand edge computing. As its name suggests, edge computing is a computing model in which data is processed as close as possible to the edge of a network. Edge computing is based on the use of edge devices. These are devices that can collect, process, and/or store data using only their own resources.
While edge computing opens up many exciting opportunities, it also creates challenges. One of those challenges is that on their own, edge devices have very limited processing capabilities.
There are essentially only two ways to resolve this challenge. The first is to use more edge devices so that each device does less work. The second is to use a complementary solution that boosts the power of edge devices. This is where fog computing comes in.
Fog computing essentially adds an extra layer of computing infrastructure that sits on top of the edge infrastructure and boosts its capabilities. If the edge deployment is connected to the cloud, the fog computing layer sits in between the edge and the cloud. In either case, the fog computing layer amplifies the edge deployment while still keeping processing very local.
Increased edge intelligence has a whole range of benefits for businesses and the customers they serve. Here are just five examples of how increased edge intelligence is improving business operations.
By increasing edge intelligence, businesses increase their ability to make robust decisions at the network’s edge. This could have profound implications for many IoT applications, especially those related to safety and/or health.
Fog nodes process and filter data locally, sending only relevant information to the cloud. This optimization minimizes bandwidth usage and enhances overall network efficiency. It can therefore help to reduce data-processing costs.
The hierarchical structure of fog computing accommodates a growing number of edge devices seamlessly. Fog nodes efficiently manage the increasing data load by distributing tasks and resources. This ensures that the network can handle a diverse range of IoT devices without compromising performance.
Fog nodes can host lightweight machine learning models, enabling edge devices to learn and adapt based on local data without relying on continuous connectivity to central servers. This capability is especially valuable in applications like predictive maintenance particularly in industrial settings.
Fog nodes can function without continuous network connectivity. This makes them ideal for mission-critical applications which need to be able to keep running no matter what. Fog nodes are particularly useful in harsh environments as these tend to be places where network connectivity is unreliable.
Here are five real-world examples of how fog computing is already being effectively used.
Fog nodes process data from sensors and cameras locally, reducing latency in critical decision-making processes, such as obstacle detection and route planning.
Fog computing enhances edge intelligence by processing data from sensors and machinery locally. This localized approach improves real-time monitoring, predictive maintenance, and process optimization.
Fog computing plays a pivotal role in smart cities by optimizing data processing for applications like traffic management and surveillance. This enhances the overall efficiency of urban management.
Fog computing is applied in healthcare monitoring systems to process and analyze patient data from wearable devices locally. This enables real-time health monitoring and ensures timely responses to critical health events.
Fog nodes enable real-time analysis of customer behavior, inventory management, and personalized marketing. This enhances the efficiency of retail operations by providing timely insights and improving the overall customer experience.
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