Fog computing is a decentralized infrastructure that places storage and processing components at the edge of the cloud.
The definition of fog computing is that it is a decentralized infrastructure that places storage and processing components at the edge of the cloud, where data sources such as application users and sensors exist. This article explains fog computing, its components, and best practices for 2022 in detail.
What is Fog Computing?
Fog computing is a decentralized computing architecture that extends the capabilities of cloud computing by placing storage, processing, and networking resources closer to the edge of the network, such as end-user devices or IoT sensors. This allows for faster data processing, lower latency, reduced bandwidth usage, and improved security and privacy. Fog computing is particularly useful in scenarios where large volumes of data are generated at the edge of the network and real-time processing and decision-making are critical, such as in industrial automation, smart cities, healthcare, and transportation.
According to Domo’s ninth annual ‘Data Never Sleep’ infographic, 65% of the world’s population — around 5.17 billion people — had access to the internet in 2021, or 79 zettabytes of data were consumed globally. By 2025, experts project that data consumption will exceed 180 zettabytes. The advancement of wireless technology has significantly improved the computing capabilities of mobile device users. As a result, users can now access information and perform tasks from anywhere, at any time. Additionally, the widespread availability of high-speed internet connections has facilitated the rapid transfer of data and made cloud computing increasingly popular. These developments have created new opportunities for businesses and individuals alike, allowing for greater flexibility and productivity.
Enterprises across all industries are experiencing a flood of data from consumers, whether it’s through the Internet of Things (IoT) or other means. This data explosion is driving the need for customer experiences that rely on large amounts of data, from smart electric grids to fitness trackers and beyond. The dynamic processing and storage of such a huge amount of data are due to cloud computing and Artificial intelligence. By leveraging this data, organizations can make informed decisions and safeguard themselves against vulnerabilities that exist at both the business and technological levels.
Assuming a temperature sensor in a factory line sends data every second to the cloud to check for temperature fluctuations, a smarter approach to storing this information would be to check for any variations within the past few seconds. This would enable more efficient data storage and retrieval as only relevant data is stored and used for analysis or decision-making. With the advancement of wireless technology providing mobile device users with significant computing capabilities, implementing this smarter approach would optimize the use of available resources. To ensure the proper functioning of the production line, data is immediate. While temperature data may occupy minimal storage space, this scenario is also typical for devices such as CCTV cameras that generate vast amounts of audio and video data.
Fog computing refers to the practice of storing and processing data on devices with limited processing power before sending it to the cloud. Furthermore, this approach helps to reduce the cloud’s workload by distributing it to devices at the edge of the network. Therefore, the main objective of fog computing is to utilize the cloud primarily for long-term and computationally intensive analytics. On the other hand, devices are located at the edge of the cloud. Where the organization’s system interacts with external systems, is responsible for time-sensitive and short-term analytics, such as fault alerts, alarm status, and other critical tasks.
“Specifically,” edge computing is a specialized form of fog computing that focuses on processing data at the source of its creation. Edge computing commonly uses devices such as sensors, cameras, routers, switches, embedded servers, and controllers. Thus, this approach locally stores and analyzes data generated by these devices, eliminating the need to transfer it to the cloud. The primary objective of edge computing is to reduce latency and optimize bandwidth, which enhances overall system performance. This is achieved by processing data at the device level, closer to where it is generated.
Fog computing adds an intermediate layer between the cloud and edge devices. This layer comprises a collection of small computing servers located in close proximity to the edge devices, rather than being located on the devices themselves. Furthermore, the seamless flow of information is facilitated by interconnected servers that are linked to centralized cloud servers. Collaboratively, these small units perform pre-processing of data, provide short-term storage, and rule-based real-time monitoring. The architecture of fog computing helps to minimize the amount of data transmitted across the system, resulting in enhanced efficiency and performance.
Fundamental Elements of Fog Computing
There are too many ways to implement a fog computing system.
Below are the explanations of the common components across these architectures.
1- End Devices(Physical & virtual nodes)
The devices that contact the real world are known as end devices. Here are some examples:
- Sensors and actuators
- Cameras and surveillance systems
- Industrial equipment and machines
- Wearable devices and personal assistants
- Smartphones and mobile devices
- Internet of Things (IoT) devices
- Intelligent traffic management systems
- Environmental monitoring devices
- Autonomous vehicles and drones
- Smart home devices, such as thermostats, lighting, and security systems
These devices are the primary sources of data and can encompass a wide range of technologies. Therefore, they may possess varying degrees of storage and processing capabilities, as well as different underlying software and hardware.
2. Fog nodes
Moreover, self-contained devices, known as fog nodes, collect the generated information. They fall under the 3 categories;
- fog devices
- fog servers
These devices store crucial data, and the fog server also computes this data.
3. Monitoring services
APIs oversee system performance and resource availability in monitoring services, ensuring that end devices and fog nodes are functioning properly and communication is uninterrupted. Furthermore, monitors can not only provide real-time data on resource usage but can also leverage this information to predict future resource needs. By performing regular audits of the system, monitors can identify potential bottlenecks and address them before they cause delays or other issues. Therefore, this proactive approach helps ensure optimal efficiency and reduces the risk of downtime.
4. Data processors
Data processors run on fog nodes and filter, trim, and correct faulty data from end devices. The determination of whether data should be stored locally or in the cloud and the homogenization of information for seamless transportation is done by them. Processors may also use historical data to fill in missing information and prevent application failures.
5. Resource manager
In fog computing, independent nodes must work in a synchronized manner. The resource manager is responsible for assigning and releasing resources to nodes, as well as scheduling data transfers between nodes and the cloud. It also takes care of data backup, ensuring zero data loss.
Additionally, to ensure high availability, fog components share some of the SLA commitments of the cloud. The resource manager works with the monitor to determine demand and avoid redundancy in data and fog servers.
6. Security tools
Security must be an integral part of the fog computing system as it interacts with sensitive data sources directly. Encryption is mandatory due to the wireless nature of communication, and user and access management are crucial for end users who directly request data from fog nodes.
Applications in fog computing utilize data from the system to deliver services to end-users. To ensure cost-effectiveness and efficient service delivery, a common interface and protocols with an abstraction layer are needed. Typically, the use of web services like APIs accomplishes this.
Examples and Use Cases of Fog Computing
fog computing is just coming up to address the various latency issues that plague IoT devices.
- Smart Grids: In smart grids, fog computing can monitor power generation, distribution, and consumption in real-time. This can help in identifying and resolving power failures quickly.
- Autonomous Vehicles: Real-time processing of sensor data to assist in navigation, object recognition, and decision-making in autonomous vehicles can use fog computing.
- Healthcare: Remote patient monitoring, real-time health data analysis, and personalized healthcare services can use fog computing.
- Smart Buildings: Fog computing can be used to monitor and control smart buildings’ HVAC systems, lighting, and other systems to optimize energy consumption and improve comfort.
- Industrial IoT: Real-time monitoring of equipment, predictive maintenance, and automation of processes in industrial IoT can use fog computing.
- Smart Retail: Real-time inventory tracking, personalized marketing, and customer analytics in smart retail can use fog computing.
- Agriculture: Precision farming, real-time monitoring of crops, and intelligent irrigation systems can use fog computing in agriculture.
- Disaster Management: Real-time monitoring of weather conditions, early warning systems, and emergency response can use fog computing in disaster management.
- Smart City: In smart cities, one can use fog computing for real-time traffic management, intelligent waste management, and energy-efficient street lighting.
- Sports Analytics: Real-time data analysis, player tracking, and fan engagement can use fog computing.