Fog Computing Applications

1. Introduction

Fog computing is one among many layers of the IoT computing paradigm that sits between the cloud and mist layer. There are several factors and areas where fog computing brings in significant value. The devices involved in the fog computing paradigm are often commonly available devices like personal computers or micro-computers. This keeps the geographical proximity close to the edge devices which helps take load off the cloud by processing data in the fog computing layer as well as reducing latency. The fog computing paradigm is implemented at the edge of the IoT network and is near the edge devices. Instead of sending raw data directly to the cloud for processing, it can be processed in the fog layer which has a significant improvement in the speed of communication and processing subsequently resulting in a lower latency. This is a critical factor for time-sensitive applications where small amounts of time can have a notable impact. (Habibi et al., 2020)

There is an enormous amount of data being generated by edge devices in an IoT architecture. A cloud infrastructure usually handles the data for processing and analysis, followed by which the data is often sent back to the devices carrying some instructions or information for decision making. This large amount of data can sometimes congest the network bandwidth and might have a fatal impact on time-sensitive applications. Fog computing not only reduces the amount of raw data being sent but processes the raw data to send only relevant and processed data to streamline the processing and optimize data transmission.

Fog Computing is an extension that aims to move the processing and execution closer to the source of the data. It helps in improving the service and delivery time while reducing the bandwidth of data and processing sent to the cloud. Fog computing serves as a bridge layer between the edge devices and cloud computing infrastructures by utilizing the computational power from consumer devices like personal computers, or micro-computers — referred to as fog nodes — within the edge network to handle and process small amounts of critical data to reduce the latency and provide more robust and sustainable service. Another significant benefit fog computing provides is reducing the data traffic between the edge nodes and cloud datacentres. In the instance when many IoT devices initiate data-driven interaction with the cloud, there is a high probability of network congestion which can have fatal repercussions on the delivery of processed data or commands which can further lead to possible failures (Mahmud et al., 2021).

The objective of this book chapter is to provide a comprehensive understanding of fog computing by exploring its novel applications and services, advancing the state of fog computing with emerging technologies and fog computing applications: case studies and future research directions. the chapter begins with an introduction to fog computing with its recent trends. novel fog applications are then explored for smart homes, video surveillance and education followed by an in-depth examination of advancing the state of fog computing with emerging technologies that focuses on the application of fog computing on iot-based and blockchain-based applications in smart cities context. in addition, the topic explores mobile computing under fog computing and the significant impact in combining fog computing and 5g technology. The chapter also discusses fog computing applications in depth with some case studies on smart agriculture, autonomous driving, and healthcare.