Some industrial and business processes are so critical that relying on cloud computing will slow down the productivity and efficiency of the whole system. Here, fog computing comes to your rescue.
The technology space is changing rapidly, and new IT technology dominates the market. According to the Gartner Hype Cycle 2022, currently, many computation and storage-related technologies are creating massive hype in a short period.
For example, Computational storage, Industry cloud platforms, Cybersecurity mesh architecture, etc., are some. In Industry cloud operation, an emerging technology is Fog computing. It creates a bridge between blazing-fast edge computing and intermediate-speed cloud computing.
If your business is into supercritical tasks that must take place quickly with high security, then you choose edge computing. But what if edge computing hardware cannot process the amount of data the machines are producing onsite? You use fog networking.
Read on to learn fog computing from the foundation level, along with some high-quality further learning resources to master fog computing in a business or professional capacity.
What Is Fog Computing?
Fog computing is a decentralized network of computing infrastructure or processes of data crunching in which computing resources are located between the device or data source and any central data center or third-party cloud computing infrastructure.
Cisco came up with the term fog computing in 2012 to define an alternative to cloud computing that will stay closer to the machines or apps where data processing speed must be faster, or the process may slow down or fail.
Later in 2015, top hardware and software developers like Intel, Microsoft, Dell Technologies, ARM Holdings, and Cisco Systems established the OpenFog Consortium to promote the growth of fog computing.
Currently, fog computing is a popular name in industrial niches where high-speed data processing must be done at the edge of the cloud network. It has a few synonyms, as mentioned here:
Fog networking is a bridge between edge computing and cloud computing. To save on bandwidth costs and increase processing speed, IoT-enabled manufacturing processes, home automation, security systems, etc., can deploy an added layer of computational power called fogging.
This computational layer will have storage, processing capability, and analytics apps. Depending on a pre-configured set of instructions, data will go directly to a fog computing infrastructure closer to the IoT device or sensors that collect data from the operational environment.
It is the same place where you keep your edge computing system. So you can consider edge and fog computing as closer to each other, and cloud computing stays much farther away.
If edge computing can not process data, collected data goes to a fog networking system. It will either process the data and instruct the IoT systems with some decisions. Later, it will store the processed data on the cloud for archiving purposes.
How Does Fog Computing Work?
A fog networking framework consists of various hardware components and software functions depending on its industrial application.
It often includes computing gateways that collect data from on-site intelligent machines and data sources. Also, a fog network can accept data from various collection endpoints like switches and routers connecting digital assets within a network.
The working principle of a fog computing system primarily means transporting data from and to IoT or sensor devices in an IoT environment. Here is how the data transfer takes place during fogging:
An automation controller monitors read signals from networked IoT devices, sensors, and other intelligent machines.
The automation controller runs a programmed app or algorithm that, in turn, automates the IoT equipment.
This pre-configured program uses a standard OPC Foundation server to transport data to the next device in the fog networking pipeline. OPC Foundation is otherwise known as Object Linking and Embedding for Process Control (OLEPC) or Open Platform Communications (OPC).
The program can use any other gateways as well.
A machine converts this data into a data protocol that various internet communications standard understands, like HTTPS or MQTT (MQ Telemetry Transport).
Now, the internet or intranet network can easily send the converted data to one or more fog nodes on the edge of the cloud for analysis.
Fog nodes will immediately instruct the connected IoT devices on what to do by analyzing the environmental signal.
Later, the fog node stores the data on a distant cloud server for audit, analysis, and archiving purposes.
Now, let’s figure out the brief difference between fog and edge computing.
Fog Computing Vs. Edge Computing
The primary difference between edge and fog networking is the location of the computing power.
In Edge computing, the computation and decision-making power could be built-in on the IoT device. For example, facial recognition-capable smart security cameras that are connected to a local area network (LAN) as well as to cloud storage.
Sometimes, multiple smaller IoT devices like actuators, temperature sensors, fluid sensors, motion sensors, etc., connected to an edge computing hub are also possible architecture for an edge computing network.
Contrarily, fog computing places intelligence or computing power on the LAN using a fog node or fogging hub. The hub collects real-world signals from IoT devices and processes and then instructs the connected, intelligent machines on what to do. The fog node is also responsible for sending data to the central cloud server for in-depth analysis, which is not critical for real-time decision-making.
However, some IoT experts consider that fogging is just the Cisco standard of edge computing.
Cloud computing creates a centralized hub for all computational and storage needs. It makes a network unintelligent. Contrarily; fog networking brings intelligence to the edge of a network that is also connected to a cloud.
Such edge intelligence reduces the load on cloud computing and internet resources.
Components of Fog Computing
Different IoT companies use different ways to set up a fog networking system. Hence, you could find multiple architectures in the fog networking ecosystem. However, the following are the common components in any standard fogging architecture:
#1. Virtual and Physical Nodes
These are simply end-user devices like mobile phones, various sensors in a production line, smart speakers, smart lights, and many more that generate data and execute instructions.
#2. Fog Devices or Nodes
These are generally fog servers, fog gateways, and fog devices. Fog devices store data, while a fog gateway analyzes data from multiple fog devices. Finally, fog gateways take care of data routing and re-routing.
#3. Monitoring Services
These APIs ensure fog nodes and IoT devices are not stalled and always communicating.
#4. Data Processing Programs
These programs run on a fog server to filter, process, cleanse, reconstruct, and finally store data on the cloud.
#5. Resource Managing Systems
It functions as the load-balancing unit and supervises the utilization of all fog nodes.
#6. Security Apps and Tools
Data encryption in transit and at rest is always needed to ensure secure fog computing. These components ensure robust encryption of digital data.
#7. GUI, Software, and Apps
These are apps and tools that human users or plant operators use to control the entire system.
Why and When Do You Need Fog Computing?
Fog computing enables IoT-based businesses to scale up their operations. They could not rely on cloud computing because when you see an uptick in traffic or user, cloud computing might just fail.
Cloud computing is a good source of low-cost computational power, programming platform, and mass storage. However, you can not just rely on the cloud or virtualization when it comes to processes that require a supercritical level of accuracy and speed.
When talking about virtually zero latency in an IoT system-based factory or smart city, you must set up one or more fog networking systems depending on the size of the IoT environment.
Some other notable reasons to implement fogging are:
Your IoT systems are collecting too much data, and you do not need all this data. Hence, fogging can help you with data filtration.
Networked IIoT equipment needs to react within a millisecond from detecting an anomaly. Such speed can only happen either with edge or fog computing.
Next, let’s explore the benefits of fog computing.
Benefits of Fog Computing
Find below the potential of fog networking in smart city, home, and industrial automation:
If latency matters a lot for your business, fog computing is an ideal choice. It performs data analysis at a point close to the data source. Therefore, companies can expect minimum latency compared to the other technologies.
Especially in industries like manufacturing and energy, where every second is valuable, fog networking can offer faster alerts ensuring less time lost.
Less Bandwidth Use
In fog computing, data analysis does not involve moving the data into a cloud server. Therefore, there is no need for an extensive amount of network bandwidth. It not only reduces internet dependency but also costs companies less.
Though the connected devices keep generating data for analysis, the tasks are performed at the nearest point. As a result, most data do not need transportation.
While data privacy is critical in the present world, fog computing takes care of that. Whenever businesses need to apply a certain level of privacy, they can use fog networking.
All mission-critical data is locally analyzed as an IT team carefully supervises and supports the device. Only the data subsets needing higher-level analysis are sent to the cloud server.
For this reason, the data processed by fog computing are comparatively safer from privacy invaders.
Cost is often a major concern for all kinds of organizations. If they choose fogging, the overall cost to the company reduces. Since this type of computing needs less network bandwidth, the operational cost gets cut significantly.
All the data generated by the IoT should be protected from unauthorized access and cyber criminals. In fog computing, fog nodes can be monitored and protected using the same controls and policies companies have for the rest of their IT environment. As a result, the data stays safe in transit and at rest.
In most cases, IoT devices have to function in difficult environmental conditions. Fog networking can improve the reliability of the data even under these harsh conditions while reducing the need for data transmission to the cloud.
Companies that use fog computing also have access to data analytics in real time. They can capitalize on this feature to stay ahead of their competitors.
Manufacturing and financial companies need to make instant decisions using analytics data. They can benefit from fogging with its speedy transfer of real-time data.
#1. Fog Computing: Concepts, Frameworks, and Applications
Looking for a text-book style book to learn fogging from basic to advanced levels? Try this book on fog computing by CRC Press on Amazon.
This computing conference summary book is available on Amazon in hardcover and paperback versions.
The Internet of Things and the Industrial Internet of Things is growing rapidly. According to Statista, there were 8.6 billion active IoT and IIoT devices in 2019. The number grew to 15.14 billion in 2023. A carefully researched forecast by the same statistical analysis company says that the world will see approximately 29.42 billion active IoT devices by 2030.
These huge numbers of IoT devices at home, smart city municipalities, and industries will require petabytes of internet bandwidth if they plan to work on cloud computing infrastructure.
Not to mention that some critical IoT processes will never attain faster processing speed if they stick to the cloud. Fog computing is a reasonable solution between the cloud and edge, and you can explore business opportunities or high-paid professional positions when you learn and master fog computing.
Tamal is a freelance writer at Geekflare. After completing his MS in Science, he joined reputed IT consultancy companies to acquire hands-on knowledge of IT technologies and business management. Now, he’s a professional freelance content… read more