What is fog computing? The concept of fog computing was developed to combat the latency issues that affect a centralized cloud computing system. The boom of consumer and commercial IoT devices and technologies has put a strain on cloud resources. The cloud, which is the data center, is too far away from the data source (IoT devices); sending information and data to the data center for analysis results in a latency that undermines the agility of IoT technologies.
Fog computing can optimize data analytics by storing information closer to the data source for real-time analysis. This works in tandem with edge computing. Data can still be sent to the cloud for long-term storage and analysis that doesn’t require immediate action. Let’s get a better understanding of the underlying principles behind fog computing and see the ways it can help large, dispersed networks process data.
How Does Fog Computing Work?
Fog computing works by utilizing local devices termed fog nodes and edge devices. Raw data is captured by IoT beacons. This data is sent to a fog node close to the data source. This data is analyzed locally, filtered, and then sent to the cloud for long-term storage if necessary. Edge devices can be several different types of device, including:
- Routers
- Cameras
- Controllers
- Switches
- Embedded servers
In reality, any device with computing, storage, and network connectivity can act as a fog node. When data is collected by IoT devices and edge computing resources, it is sent to the local node instead of the cloud. Utilizing fog nodes closer to the data source has the advantage of faster data processing when compared to sending requests back to data centers for analysis and action. In a large, distributed network, fog nodes would be placed in several key areas so that crucial information can be accessed and analyzed locally.
The potential benefits of a decentralized computing structure are plentiful. However, a good example to illustrate the importance of rapid data analysis is alarm status. Many security systems rely on IoT technology to detect break-ins, theft, etc., and notify the authorities. If the alarm warning triggered by the IoT security system needs to be sent all the way to the data center to be analyzed and acted on, it could act too late, rendering the entire IoT security system more or less useless.
Time-sensitive data like alarms, fault warnings, and device status greatly benefits from the speed of edge computing. This data needs to be analyzed and acted upon quickly in order to prevent major damage or loss. The cloud is great for decentralized access to resources and data, but cloud computing struggles to keep up with the speed and efficiency demanded by the influx of information provided by IoT technology.
The Benefits of Fog Computing
The main benefits of fog computing come down to increasing the efficiency of an organization’s computing resources and computing structure. In many organizations, especially large ones, a lot of key information is generated at the edge of the network. Edge computing applications can benefit more than data analytics-related processes. Below are some of the key benefits of establishing a fog computing architecture.
Latency
We’ve already highlighted the latency issues that plague network connections in large cloud computing networks. Fog computing eliminates the need to send data to the cloud to be processed. Removing the issues of cloud latency from your data processes makes them more efficient. The cloud can still be utilized for data storage, but you don’t need to rely on the cloud for processing too. Latency issues may not be a major factor in your organization, but for others, they could cause serious issues and damages.
Reduced Bandwidth
Cloud computing requires a ton of network bandwidth, especially if you have an organization’s worth of IoT devices and technologies communicating with the cloud and sending data back and forth. You can increase your computing power by eliminating constant cloud communication and handling data locally. Your devices and network will perform better with a reduction in the bandwidth being used by cloud computing.
Distribution
Fog networking relies on a network of connected devices instead of a centralized cloud. This means you can distribute your network across a wider range of locations than cloud or traditional computer networking. Decentralized networks lead to better User Experiences for end-users in your distributed network. Fog computing allows your organization to get better computing power out of remote, distributed locations than any other networking solution.
Flexibility
This is closely related to the ability to distribute network resources to a wide range of locations and users. However, fog networking is also flexible because it can be quickly scaled up or down depending on the needs of your organization. You can always add, remove, or move fog nodes as needed to meet the current needs and challenges of your organization. Fog computing facilitates the ability to move your computing resources as they are needed.
Real-Time Data Analysis
We’ve already highlighted some instances where real-time data analysis is crucial in the examples of IoT security. Real-time data analysis is also an important resource for Machine Learning applications. If you’re relying on Machine Learning technology in your organization, you cannot afford to wait for the latency of the cloud. You need real-time data in order to maximize the efficiency and accuracy of the insights provided by Machine Learning. Edge computing applications help deliver real-time data.
The Disadvantages of Fog Computing
Like any technology, fog computing applications also have disadvantages. So far, we have only really looked at the benefits and the upside to fog computing. Let’s get a better understanding of some of the limitations of fog computing and edge devices and the concerns you may have.
Security
The major concern anyone should have about any technology or application before adoption should be data security. Fog computing applications do present unique security challenges. Since fog computing is decentralized, you will need to rely on the people near your network edge to maintain and protect your fog nodes. It will also be difficult to maintain any centralized security control over your fog nodes.
End-user privacy is one of the main security concerns associated with fog computing. Fog nodes collect a lot of information, and if those nodes are accessed by an intruder, there is little that can be done to stop them. The important step that should be taken is keeping the location of your fog nodes secret.
Fog computing is also vulnerable to cyberattacks since most of the devices connecting to the fog node are not authenticated. Attackers can access your nodes using your own devices against you. Large organizations utilize multiple devices, and it is a nearly impossible task to authenticate all of them. Plus, restricting access to the fog nodes detracts from the whole purpose of fog computing. Encryption can help mitigate this vulnerability, and user behavior profiling using Machine Learning can help you find irregularities in user behavior that could signal an attack.
Location
Another limitation to fog computing is that it is location-based. You can access the cloud from anywhere, but on a decentralized fog computing system, you need to be in the local area of your fog node in order to access the network. There is no centralized access to fog nodes. That is why many organizations use fog computing in addition to the cloud. This layered approach adds another layer of difficulty.
Layered Complexity
Fog computing is usually used in tandem with traditional networking and cloud computing resources. The combination of these technologies can get very complex very quickly. This complex network architecture needs to be maintained and secured from cyberattacks. The bigger the organization and the more systems to organize and maintain, the more difficult the task becomes.
What Industries Rely on Fog Computing?
Fog computing is becoming more popular with industries and organizations around the world. However, the main industries that take advantage of this technology are the ones that require data analytics close to the network edge and use edge computing resources. One major industry that relies heavily on fog computing is healthcare. IoT in MedTech has grown substantially with smartwatches and other wearable devices. The sheer amount of data collected in these apps every day is too massive to process without the aid of fog computing.
Other industries that need to collect large amounts of data or compile open data in apps also use fog and edge computing to deliver the best service possible. These industries include:
- Oil and gas
- Retail
- Government
- Military
- Hospitality
- Agriculture
Final Thoughts
Fog computing is a powerful technology used to process data, especially when used in tandem with the cloud. With the sheer amount of data being collected by IoT devices, many organizations can no longer afford to ignore the capabilities of fog computing, but it is also not wise to turn your back on the cloud either. Edge and fog computing doesn’t have the capability to expand connectivity on a global scale like the cloud. To really get the most out of your computing resources, combining cloud and fog computing applications is a great option for your IoT architecture.
Now that we have covered this topic, you won’t have to ask, “What is fog computing?”
