8.1.1 Difference Between Fog and Edge Computing

The cloud enables users to access computer, networking, and storage choices quickly and conveniently [9]. It might lead to reduced productivity or latency for devices that are not linked to the internet.

Leading-edge calculation aims to reduce processing time and distance by bringing data sources and equipment closer together. In principle, this enhances both applications’ and smartphones’ efficiency and performance [10].

Fog computing (FC), to use a Cisco tenure, also brings the computer to the net’s control. It mentions the requirements aimed at this procedure’s optimal performance. Figure 8.2 depicts a pyramidal model of fog, cloud, and edge computing.

Process delay is eliminated or considerably reduced by locating storage and computing systems as close to the programs, parts, and systems that make them as feasible. That’s also especially crucial for IoT devices, which create large volumes of data. Because they are nearer to the source of data, these gadgets have a significantly lower delay in fog computing [11, 12].

The difference between cloud, fog, and edge computing is shown in Table 8.1.

To distinguish between edge devices and fog nodes, fog is the model that enables consistent, well-structured, scaled execution inside the editing environment. Information is compiled, analyzed, and saved close together and in edge computing, it’s a kind of cloud computing. Edge processing, and also the network and application connections required for data transit, are all part of sensor networks [13].

That’s because, with both fog and portable edge calculation, the goal is to decrease inactivity and boost efficiency while processing data in diverse locations. Cutting edges are most common when sensors are linked to equipment and data is collected—there is a bodily link between the information basis and the dispensation place. Fog computation reduces the remoteness between the dispensation site and the information basis by performing edge computing on or within the LAN processors connected to an IoT or fog node [14]. This causes a physically longer processing distance between the sensors and no additional delay [15].

Merits of Fog Computing

Some merits/advantages of fog computing is described below.

Latency

System breakdowns, line shutdowns, and other significant challenges can be avoided by keeping analysis closer to the data source, especially in vertical systems that count every second. Faster warnings imply less danger to users and less time lost, allowing for real-time data processing.

8.1.1.1 Bandwidth

Maintain adequate bandwidth in the network. Many information analysis responsibilities, smooth the most serious ones, organize not necessitate the use of fog-based storing and handing out. Connected devices continually create more data for analysis. To free up bandwidth for other important tasks, the majority of this massive amount of data is delivered via fog computers [16].

Costs of operation are lowered. Operational costs are lowered as a result of local processing and network bandwidth retention. Increase the level of safety. It is critical to protect IoT data throughout transmission and storage [17]. Users may monitor, protect, and enable fog nodes throughout the whole IT system using the same controls, policies, and processes. The merits of fog computing is shown in Figure 8.3.

8.1.2 Relation of Fog with IoT

IoT and end-users are becoming more powerful. The cloud now handles a large amount of data in real-time. IoT devices using fog computing [21] are shown in Figure 8.4. Furthermore, fog computing provides several benefits to the IoT app development process:

Agility for Business

With the right tools, you can design and deploy fog apps. In the hands of the user, the device can function similarly to these programs.

Safety

Fog computing performances are a substitution for strategies that limit their resources and keep their security software and credentials up to date. It uses fog nodes in various parts of the IT infrastructure, all of which follow the same policies, processes, and controls. Data processing is a complex distributed system that uses a large number of nodes to assess the security condition of almost connected devices.

Delay

Have you noticed how quickly Alexa asks? This is because fog computing has low latency. Also because fog is closer to all users (and gadgets), it responds quickly. This device is ideal for any operation that requires a quick response.

Bandwidth

Fog control allows speedy and effective information dispensation founded on available applications, computer capitals, and schmoozing. Instead of being sent through a single path, information is blended at numerous points. This decreases the quantity of information that must be moved to the fog, preserving net capacity which helps in lowering costs.

Services

Even if the network connection to the cloud is limited, fog computing can operate independently and deliver uninterrupted services. Furthermore, due to a large number of connected channels, connection loss is almost impossible.

User Experience

Stronger protocols, such as Zigbee, Z-Wave, or Bluetooth, are used by edge nodes. Edge computing offers immediate connectivity between mobile and home consumers, independently of the network, improving user experience.

8.1.3 Fog Computing in Agriculture

The agriculture industry has gained and been changed as a result of fog computing. The SWAMP project, which stands for Smart Water Management Platform, is an excellent example in this regard [22]. Water, which accounts for 70% of freshwater use, is a critical component of the agricultural industry, making it the most significant consumer. Leaks in distribution and irrigation systems in-field application methods frequently result in resource waste.

Surface irrigation waste has high-water content since it only watered areas where plants are not favorable. Local irrigation here provides for more effective and efficient use of water, which eliminates the need for irrigation or irrigation. The fundamental issue is that farmers provide enough water to avoid under-irrigation. It not only reduces output but also increases the waste of a critical resource [23]. As a result, farmers required a technique to deal with these events and provide an effective response. And it is at this point that the SWAMP project identifies and addresses IoT, data analysis, stand-alone devices, and so on.

SWAMP develops a smart water system idea for agriculture with the help of fog computing, ensuring that water wastes are reduced to a minimum [24]. Fog computing also allows the system to collect and analyze sensor data from the field to improve water distribution. An example of fog computing in agriculture is shown in Figure 8.5.

The SWAMP project has published an essay about the concept of a smart agricultural environment, in which data is collected and stored in real-time for analysis. The technique [25] discusses two different approaches to using fog to filter data. The experiment filters the methodologies and employs a real-time data package that includes temperature and moisture readings. Another use of fog computing in agriculture is Agrifog, or smart agriculture or precision agriculture, which has enabled IoT-based agricultural systems [26]. iFogSim was used to construct the program.

Through data processing, it aims to reduce latency in real-time decision- making. IoT-Fog is a low-cost, comparative study of data gathered from the cloud and fog-based technologies. As a platform, fog computing has transformed the agriculture and agricultural sectors, allowing farmers to reduce waste and interpret and analyze processed data to find methods to profit from it. Fog computing in healthcare.

New technologies are frequently employed in the medical field to improve services and solutions. In addition, similar to earlier technological breakthroughs [27], fog computing was leveraged to its advantage. One of the most important uses of fog computing in healthcare is eHealth an example shown in Figure 8.6. eHealth is an online and print platform that gracefully guides health workers across the healthcare continuum, which is characterized by frequent and exciting changes as a result of escalating technical and other structural changes [28].

They employ a network mix that connects medical devices to cloud platforms. The application organizes, transmits, stores, and records data relevant to the treatment, payment, and recording processes. Because professionals have access to electro-medical records (EMR), which comprise documents such as X-rays, ultrasounds, CT scans, and MRIs, fog computing makes diagnosis and evaluation procedures easier. It also keeps data safe on a private cloud [29].

Instead of keeping a physical duplicate, the application may preserve confidential data on many networks and monitor it via fog computing. The recorded information enables a physician to quickly access and diagnose the patient’s condition, as well as get the patient’s medical records. Fog computing also allows eHealth to provide quick responses to critical medical requirements [30]. Similarly, wall, a separate health solution, creates a smart home environment with a fog computer by creating a customized sensor-based context-conscious application.

8.1.4 Fog Computing in Smart Cities

Intelligent cities are urban areas that gather data from residents who are able or unable to live there utilizing technological gadgets. The data then adds to the town’s overall quality of life. People opt to stay and make such smart cities their home since they have more job opportunities and better living circumstances. Fog computing develops cost-effective, real-time, and latency-sensitive surveillance technologies to protect residents’ and visitors’ privacy as shown in Figure 8.7 [31]. Fog computing has already worked wonders in several locations, reducing traffic congestion. People are tracked down, and GPS technology predicts traffic and suggests other routes and arrival times.

Another fascinating use of fog computing is driverless vehicles, which require the processing of large amounts of data. Fog computing is critical for linking low-level sensors and enabling high bandwidth real-time processing.

Intelligent waste management solutions must be addressed in smart cities that are safe and considerate of their residents’ demands. Sensor data and improvised garbage control strategies can be used by the local council here. Waste management solutions that are intelligent are employed. Smart cities are advancing at a faster rate every day, thanks to the availability of new technological solutions. The amount of data that can be captured and analyzed is unlimited with a fog computing platform.

8.1.5 Fog Computing in Education

The education business has evolved as a result of technological advancements, particularly in light of COVID-19 [32]. The whole industry was heavily reliant on electronic gadgets, and many professionals who wished to further their careers used online programs to do it. The fog computing Platform facilitates communication while also ensuring network data storage and administration. It improves scalability, flexibility, and redundancy for education systems to safeguard privacy and safety as shown in Figure 8.8.

Computers and Entertainment for Fog

In recent decades, the entertainment business has gone a long way. Both customers and producers have reported high demand. Consider sports, where all live broadcasts of events covering wide fields, such as ESPN, NBA TV, NBC Sports Network, and others, are expected to provide high-quality and accurate coverage of every game minute [34].

8.1.6 Case Study

With 10% of all data created and processed outside of the cloud or cen-tralized data centers, edge computing is steadily gaining traction in several industries [33]. However, considering that the ratio will reach 75% by 2025, we may expect rapid growth in the use of edge computing. Figure 8.9 depicts the smart agriculture model. For good reason, edge computing in agriculture has already become a major IoT trend. In terms of speed and efficiency, edge computing is gaining ground on cloud infrastructure [35, 36].

While the benefits of IoT applications in agriculture cannot be overstated, smart farming technologies, particularly those that rely on the cloud, might present some challenges. There are a few major roadblocks to IoT cloud computing. Agriculture is both clever and intelligent [37].

Case1

Security issue: When data is sent from a field device to the cloud, the chances of a data breach are relatively significant. Furthermore, every device or sensor in the IoT network might be a potential vulnerability point [38].

Possible answer: Continuous computing reduces the risk of data infringement or theft when data is being captured – on the device itself.

Case 2

Speed Concern: Data gathering, transmission, and analysis are all time- consuming tasks. As a result, some businesses may face the dilemma of deciding between the depth and speed with which to absorb information- derived insight. This is especially true in the case of distant agricultural tools in the field [39, 40].

Simple solution: Edge computing solves this problem by improving network and data processing efficiency. The acquired data and input may be processed in each network device, resulting in faster processing rates and more insight [41].

Case 3

Cost difficulty: The expenses of cloud computing are generally based on the volume of data generated by the objects and transferred across the network. Given the number of devices used by a single smart farming system and the amount of data it produces, cloud computing costs might quickly rise. Using cutting-edge computers in agriculture eliminates the need to overcrowd or shift your warehouse with unneeded and worthless data. Solution: As a result, both storage and bandwidth expenses in your cloud may be decreased [42, 43].

There are many instances of intelligent farming, from monitoring climate change and the monitoring of crop/cattle conditions to automatic greenhouse processing and even end-to-end agricultural management solutions that are IoT-enabled. The application of edge calculations for smart agriculture is a key opportunity within the so-called “precision agriculture.” Farmers that use this technique rely on data to better manage their businesses, improve their operational efficiency, and lower their operating costs [44].

Conclusion and Future Scope

This chapter discusses how fog computing is linked to IoT and how it benefits many industries, notably the agricultural sector. The Internet of Things (IoT) is changing the world by offering a range of approaches and technologies, such as cloud, fog, and edge computing. The cloud has existed in our universe for a long time, and the fog and the edge are fresh modernizations. Both of these are comparable, with the exception that the edge is quicker and has a larger latent value than just the fog and cloud. Computing has been used in the cattle industry and others. Agriculture has not been updated and continues to use antiquated practices. If done properly, edge computing has the potential to transform things and give the agriculture industry a huge boost. For researchers in the same subject, this chapter is quite beneficial. With the progress of technology, we will soon see a new revolution in agriculture called agriculture 4.

References

1. 1. Schaffers, H., Komninos, N., Pallot, M., Trousse, B., Nilsson, M., Oliveira, A., Smart cities and the future internet: Towards cooperation frameworks for open innovation. The Future Internet Lect. Notes Comput. Sci., vol. 6656, pp. 431–446, 2011.
2. 2. Cuff, D., Hansen, M., Kang, J., Urban sensing: Out of the woods. Commun. ACM, 51, 3, 24–33, Mar. 2008.
3. 3. Dohler, M., Vilajosana, I., Vilajosana, X., Llosa, J., Smart cities: An action plan. Proc. Barcelona Smart Cities Congress, Dec. 2011, pp. 1–6.
4. 4. Vilajosana, Llosa, J., Martinez, B., Domingo-Prieto, M., Angles, A., Vilajosana, X., Bootstrapping smart cities through a self-sustainable model based on big data flows. IEEE Commun. Mag., 51, 6, 128–134, Jun. 2013.
5. 5. Hernández-Muñoz, J.M., Vercher, J.B., Muñoz, L., Galache, J.A., Presser, M., Hernández Gómez, L.A. et al., Smart cities at the forefront of the future internet. The Future Internet Lect. Notes Comput. Sci., vol. 6656, pp. 447–462, 2011.
6. 6. Mulligan, C.E.A. and Olsson, M., Architectural implications of smart city business models: An evolutionary perspective. IEEE Commun. Mag., 51, 6, 80–85, Jun. 2013.
7. 7. Walravens, N. and Ballon, P., Platform business models for smart cities: From control and value to governance and public value. IEEE Commun. Mag., 51, 6, 72–79, Jun. 2013.
8. 8. Lynch, J.P. and Kenneth, J.L., A summary review of wireless sensors and sensor networks for structural health monitoring. Shock Vib. Dig., 38, 2, 91–130, 2006.
9. 9. Nuortio, T., Kytöjoki, J., Niska, H., Bräysy, O., Improved route planning and scheduling of waste collection and transport. Expert Syst. Appl., 30, 2, 223–232, Feb. 2006.
10. 10. Al-Ali, A.R., Zualkernan, I., Aloul, F., A mobile GPRS-sensors array for air pollution monitoring. IEEE Sens. J., 10, 10, 1666–1671, Oct. 2010.
11. 11. Maisonneuve, N., Stevens, M., Niessen, M.E., Hanappe, P., Steels, L., Citizen noise pollution monitoring. Proc. 10th Annu. Int. Conf. Digital Gov. Res.: Soc. Netw.: Making Connec. Between Citizens Data Gov., pp. 96–103, 2009.
12. 12. Li, X., Shu, W., Li, M., Huang, H.-Y., Luo, P.-E., Wu, M.-Y., Performance evaluation of vehicle-based mobile sensor networks for traffic monitoring. IEEE Trans. Veh. Technol., 58, 4, 1647–1653, May 2009.
13. 13. Lee, S., Yoon, D., Ghosh, A., Intelligent parking lot application using wireless sensor networks. Proc. Int. Symp. Collab. Technol. Syst., 19–23 pp. 48–57, May 2008.
14. 14. Kastner, W., Neugschwandtner, G., Soucek, S., Newmann, H.M., Communication systems for building automation and control. Proc. IEEE, 93, 6, 1178–1203, Jun. 2005.
15. 15. Fielding, R.T., Architectural styles and the design of network-based software architectures, pp. 76–85, Ph.D. Thesis, University of California, Irvine, 2000.
16. 16. Efficient XML Interchange (EXI) format 1.0, in: World Wide Web Consortium, Feb. 2014.
17. 17. Castellani, A.P., Bui, N., Casari, P., Rossi, M., Shelby, Z., Zorzi, M., Architecture and protocols for the Internet of Things: A case study. Proc. 8th IEEE Int. Conf. Pervasive Comput. Commun. Workshops (PERCOM Workshops), pp. 678–683, 2010.
18. 18. Castellani, A.P., Dissegna, M., Bui, N., Zorzi, M., WebIoT: A web application framework for the Internet of Things. Proc. IEEE Wireless Commun. Netw. Conf. Workshops, 2012.
19. 19. Li, Y. et al., IoT-CANE: A unified knowledge management system for data-centric internet of things application systems. J. Parallel Distrib. Comput., 131, 161–172, 2019.
20. 20. Atzori, L., Iera, A., Morabito, G., The internet of things: A survey. Comput. Netw., 54, 15, 2787–2805, 2010.
21. 21. Bellavista, P., Cardone, G., Corradi, A., Foschini, L., Convergence of MANET and WSN in IoT urban scenarios. IEEE Sens. J., 13, 10, 3558–3567, Oct. 2013.
22. 22. Laya, A., Bratu, V.I., Markendahl, J., Who is investing in machine-to- machine communications? Proc. 24th Eur. Reg. ITS Conf., Oct. 2013, pp. 20–23.
23. 23. Whig, P. and Ahmad, S.N., A novel pseudo NMOS Integrated ISFET device for water quality monitoring. Act. Passive Compon. Hindawi, 1, 1, 1–9, 2013, article i.d 258970.
24. 24. Bhatia, V. and Whig, P., Modeling and simulation of electrical load control system using RF technology. Int. J. Multidiscip. Sci. Eng., 4, 2, 44–47, 2013.
25. 25. Whig, P. and Ahmad, S.N., Development of economical ASIC for PCS for water quality monitoring. J. Circuits Syst. Comput., 23, 6, 1–13, 2014.
26. 26. Whig, P. and Ahmad, S.N., CMOS integrated VDBA-ISFET device for water quality monitoring. Int. J. Intell. Eng. Syst., accepted Publ., 7, 1, 5–14, 2014.
27. 27. Whig, P. and Bhatia, V., Performance analysis of multi-functional bot system design using microcontroller. Int. J. Intell. Syst. Appl., 02, 69–75, 2014.
28. 28. Whig, P. and Ahmad, S.N., Development of low power dynamic threshold PCS system. J. Electr. Electron. Syst., 3, 3, 1–6, 2014.
29. 29. Whig, P. and Ahmad, S.N., Novel FGMOS based PCS device for low power applications. Photonic Sens. (Springer), 5, 2, 1–5, 2015.
30. 30. Whig, P. and Ahmad, S.N., Impact of parameters on the characteristic of novel PCS. Can. J. Basic Appl. Sci., 3, 2, 45–52, 2015.
31. 31. Ruchin, Mahto, C., Whig, P., Design and simulation of dynamic UART Using Scan Path Technique (USPT). Int. J. Electr. Electron. Comput. Sci. Eng., 1, 6–11, 2015.
32. 32. Sharma, A., Kumar, A., Whig, P., On the performance of CDTA based novel analog inverse low pass filter using 0.35µm CMOS parameter. Int. J. Sci. Technol. Manage., 4, 1, 594–601, 2015.
33. 33. Whig, P. and Ahmad, S.N., Simulation and performance analysis of low power quasi floating gate PCS model. Int. J. Intell. Eng. Syst., 9, 2, 8–13, 2016.
34. 34. Whig, P. and Ahmad, S.N., Ultraviolet Photo Catalytic Oxidation (UVPCO) sensor for air and surface sanitizers using CS amplifier. Glob. J. Res. Eng., 16, 6, 1–13, F 2016.
35. 35. Whig, P. and Rupani, A., Novel economical social distancing smart device for COVID19. Int. J. Electr. Eng. Technol., 2, 2, 1–10, 2020.
36. 36. Velu, A., The spread of big data science throughout the globe. Int. J. Sustain. Dev. Comput. Sci., 1, 1, 11–20, 2019.
37. 37. Velu, A., A stable pre-processing method for the handwritten recognition system. Int. J. Mach. Learn. Sustain. Dev., 1, 1, 21–30, 2019.
38. 38. Whig, P., Exploration of viral diseases mortality risk using machine learning. Int. J. Mach. Learn. Sustain. Dev., 1, 1, 11–20, 2021.
39. 39. Whig, P., A novel multi-center and threshold ternary pattern. Int. J. Mach. Learn. Sustain. Dev., 1, 2, 1–10, 2019.
40. 40. Velu, A. and Whig, P., Protect personal privacy and wasting time using NLP: A comparative approach using AI. Vivekananda J. Res., 10, 42–52, 2021.
41. 41. Velu, A., Influence of business intelligence and analytics on business value. Int. Eng. J. Res. Dev., 6, 1, 9–19, 2021.
42. 42. Khera, Y., Whig, P., Velu, A., Efficient effective and secured electronic billing system using AI. Vivekananda J. Res., 10, 53–60, 2021.
43. 43. Velu, A. and Whig, P., Impact of COVID vaccination on the globe using data analytics. Int. J. Sustain. Dev. Comput. Sci., 3, 2, 1–10, 2021.
44. 44. Khera, Y., Whig, P., Velu, A., Framework of Perceptive Artificial Intelligence using Natural Language Processing (P.A.I.N). Artificial & Computational Intelligence, Published Online: July 2021.