Increased agricultural activity is intended as intelligent agriculture or precision agriculture to be of critical importance. The fast growth of networking has resulted in IoT-based farm management systems. Increased agricultural activity is intended as intelligent agriculture or precision agriculture to be of critical importance. The established cloud-based platforms cannot cope with the huge quotas and diverse data provided by the connected IoT devices, which are based on conventional cloud models. It is important to get data processing closer to the origins of their output to reduce lag in aiding real-time decision-making based on the data generated. The adoption of fog-based models will solve this and will be discussed in this chapter. To ensure optimum bandwidth usage and low latency for real-time decision-making, an IoT-Fog farm management system may be more capable.
TopIntroduction
In Fog Computing, the data, computer, store, and applications are situated anywhere between the data source and the cloud, which is a decentralized computing infrastructure (Whig et al., 2022). Fog computing, like edge computing, brings the cloud's benefits and power closer to where data is produced and acted upon. Because both entail moving intelligence and processing closer to where the data is produced, the phrases fog computing and edge computing are sometimes used interchangeably. This is frequently done to increase productivity, but it might also be done for security or compliance concerns (Anand et al., 2022).
Fog computing extends the cloud's power closer to where information is generated and used. In other words, more people may remain on the internet simultaneously (Alkali et al., 2022). It provides the same networking and cloud services, but with additional safety and compliance.
1 Dean Research, Vivekananda Institute of Professional Studies
2 Assistant Professor, Jazan University Saudi Arabia
3 Researcher, Director Equifax, Atlanta USA
4 Research Scholar, University of Cumbersome, USA
Characteristics of FOG Computing
According to IDC, by 2025, 45 percent of all data will be created at the network edge, with 10 percent of that data coming from edge devices like phones, smartwatches, connected cars, and so on (Chopra & WHIG, 2022). The only technology that can survive the test of time is considered to be Fog Computing and it will even trump Artificial Intelligence, IoT App, and 5G in the next five years.
It delivers highly virtualized storage, computing, and networking services from traditional data centers and end devices in the cloud. Low latency, location awareness, edge location, interoperability, real-time data-cloud connection, and support for online cloud interaction are all characteristics of fog computing (Chopra & Whig, 2022b).
Instead of batch processing, fog apps require real-time interactions and typically interface directly with mobile devices (Chopra & Whig, 2022a). Fog nodes have also been used in diverse contexts with different form factors. The Basic of Fog Computing is shown in Figure 1.
Although a lot has been published and investigated on fog-computing, how various fog actors will align in the future is not simple to say. Based on the nature of major services and applications, it is nonetheless clear to conclude: subscriber models will have an extensive role in fog computing [smart grid, clever cities, linked cars, etc.] (George et al., 2021).
Suppliers of worldwide services are expected to collaborate. New holders, including transport providers, vehicle manufacturers, government authorities, etc., will enter the fog domain. Some recognized Fog Players are cloud-based providers like Apache CloudStack7, OpenStack6, and OpenNebula8 (Mamza, 2021).
Fog Vs. Edge Computing
The cloud allows consumers to quickly and easily access computer, networking, and storage options, but it is a centralized resource (Bhargav & Whig, 2021). This can lead to performance problems and delays for data and devices situated distant from the cloud.
The objective of cutting-edge computing is to bring data sources and equipment closer together, eliminating processing time and distance. In principle, this, in turn, enhances application and device performance and speed (Khera et al., 2021).