The proliferation of internet-connected devices has led to the accumulation of vast volumes of data, known as “big data.” While cloud computing has been effective in managing and processing this data, it falls short in meeting the demands of real-time, low-latency applications and network constraints. To address these limitations, a new computing paradigm called “fog computing” has emerged. Fog computing aims to enhance speed, adaptability, throughput, cryptography, and confidentiality by bringing computation, connection, and memory closer to edge devices and end-users. This chapter provides an overview of the network infrastructure, key technologies, applications, challenges, and unresolved issues associated with fog computing.
TopLiterature Survey
Fog computing is an emerging technology that extends the cloud computing paradigm by providing computing, storage, and networking services to the edge of the network. This approach is particularly useful in the context of the Internet of Things (IoT), where billions of devices generate vast amounts of data that need to be processed in real-time.
In recent years, many researchers have investigated various issues related to fog computing, such as security, privacy, resource management, and application development. In this literature survey, we will review some of the most significant contributions in this field.
M. Satyanarayanan et al. (2017) provides an overview of fog computing and its potential applications. The authors discuss the main principles behind fog computing, such as latency reduction, mobility support, and location awareness. They also present several fog computing architectures and use cases, such as smart cities, industrial IoT, and healthcare.
M. H. Rehmani et al. (2017) explores the use of fog computing in smart cities. The authors review the main challenges and solutions in deploying fog computing in smart cities, such as data management, resource allocation, and service provisioning. They also present several use cases for fog computing in smart cities, such as smart traffic management, energy management, and public safety. Finally, they discuss the future research directions in fog computing for smart cities.
H. Zhang et al. (2018) investigates the resource management issues in fog computing. The authors review various resource management techniques, such as load balancing, task scheduling, and energy management. They also analyze several performance metrics, such as response time, throughput, and energy consumption. Finally, they identify some of the challenges in resource management in fog computing, such as resource heterogeneity, mobility, and scalability.
R. Buyya et al. (2018) provides a comprehensive overview of fog computing, including its architecture, enabling technologies, applications, and challenges. The authors review the different layers of the fog computing architecture and highlight the key technologies used in fog computing, such as edge computing, virtualization, and SDN. They also present several application domains for fog computing, such as smart cities, healthcare, and transportation. Finally, they discuss the main challenges in fog computing, such as security, interoperability, and resource management.
S. Aazam et al. (2018) focuses on the security and privacy challenges in fog computing. The authors review the main security and privacy threats in fog computing, such as data breaches, denial-of-service attacks, and identity theft. They also discuss various security and privacy solutions for fog computing, such as encryption, access control, and trust management. Finally, they highlight the future research directions in security and privacy for fog computing.