Fog Computing | What is Fog Computing?

• Fog Computing, sometimes also called Edge Computing, is a distributed computing model that processes data locally, near the user or at the "edge," rather than in the cloud.
• The purpose of Fog Computing is to solve problems that arise in cloud computing due to the long distance and latency of data travel. In this model, data is processed on local devices, such as routers, gateways, or IoT (Internet of Things) devices, which eliminates the need for data to reach the cloud.
• Fog Computing was developed by Cisco, a leading networking and IT company. Its goal is to assist in applications that demand real-time data processing, such as smart cities, industrial automation, healthcare, and smart transport systems.
• Fog Computing is an emerging technology that helps overcome the limitations of cloud computing. Its main objective is to process data closer to the users, which reduces latency, increases security, and improves network efficiency.
• Although it has some challenges, its applications and benefits make it a powerful technology with the potential for increased use in various industries in the future.

• Low Latency: With Fog Computing, data is processed locally, resulting in faster response times. This is especially important for applications where real-time data processing is needed, such as autonomous vehicles or smart traffic light systems.
• Data Security: Since data is processed locally, it does not need to be transferred to the cloud over the internet. This increases data security, as sensitive information remains within the local network.
• Distributed Resources: In Fog Computing, resources are distributed, allowing data to be processed on the nearest device. This process is more efficient than cloud computing and reduces the load on the network.
• Real-Time Processing: Fog Computing facilitates immediate decision-making and data processing. This is highly necessary for time-sensitive applications, such as monitoring industrial machinery or health equipment.
• Cloud Integration: Although Fog Computing processes data locally, it can still be coordinated with the cloud. This is useful in situations where long-term data storage and analysis are required.

The architecture of Fog Computing works at several levels, with each level having its own function and role. Its architecture is based on the following key elements:

• Device Layer: This layer consists of IoT devices that collect data. For example, sensors, cameras, smartphones, and other connected devices are in this layer. These devices generate a large amount of data, which is processed by fog nodes.
• Fog Nodes: Fog nodes are the local devices or gateways that process the data. These nodes can be routers, switches, or servers that process the data before sending it to the cloud.
• Cloud Layer: This is the highest level of the Fog Computing architecture, where long-term storage and analysis of data take place. The cloud has extensive data storage capacity and is useful for situations where large-scale data processing and storage are necessary.

• Low Latency and Faster Processing: Since data is processed close to the user, it takes very little time to process the data. This is important for applications that require immediate decision-making, such as autonomous driving or medical emergencies.
• Saves Network Bandwidth: In Fog Computing, data is processed locally before being sent to the cloud, which reduces the amount of data sent to the cloud. This reduces the load on the network and saves bandwidth.
• Better Data Security: Through Fog Computing, sensitive data is processed locally, which reduces the risk of cyber attacks. There is no need to send the data to the cloud, keeping the information secure.
• Flexibility and Scalability: In Fog Computing, resources are distributed, allowing the system to be scaled as needed. Adding new devices or nodes is simple, and it is capable of managing large amounts of data.
• Local Decision-Making Capability: Many situations require quick decisions, such as in smart traffic lights. With the help of Fog Computing, such decisions can be made immediately at a local level, making the process fast and efficient.

Fog Computing has many applications that are being used in various fields. Some key applications are as follows:

• Smart Cities: In smart city projects, Fog Computing is used to process various types of data locally, such as traffic management, water management, and air quality monitoring. It makes traffic lights smart, which can control traffic and save energy.
• Industrial Internet of Things (IIoT): Fog Computing is used for monitoring and controlling industrial equipment and machinery. It allows for real-time data processing, which helps in running the production process smoothly and preventing machinery failure.
• Healthcare: In medical devices, Fog Computing is used to monitor a patient's vital information, such as heart rate, blood pressure, etc. It processes the data locally, allowing for immediate decisions, especially in emergency situations.
• Autonomous Vehicles: Autonomous vehicles demand real-time data processing. Through Fog Computing, vehicles can analyze their surroundings and make immediate decisions, such as applying brakes or changing direction.
• Smart Homes: In smart homes, Fog Computing is used to control various devices in the home such as smart thermostats, cameras, and security devices. It can process data within the home, giving the homeowner quick responses.

• Security and Privacy: Since data is processed locally in Fog Computing, the risk of security threats is also higher. Every node or device can be a potential source of threat, so security measures are needed.
• Managerial Complexity: Fog Computing involves many devices and nodes, which can be challenging to manage. Ensuring communication and coordination between these nodes can be a complex process.
• Need for Advanced Hardware: To run Fog Computing, high-performance hardware is required that can process data locally. This can increase costs and make it difficult for small businesses to adopt.