Zaigham Mahmood (editor) - Fog Computing: Concepts, Frameworks and Technologies

Fog Computing is a distributed computing environment that extends the Cloud Computing paradigm to fully support the Internet of Things (IoT) vision. It aims to push the intelligence, processing and storage of data closer to the Edge of the network to provide computer-related services more immediately and near to the interconnected smart things that form part of the IoT. For this reason, Fog Computing is sometimes also referred to as Edge Computing. The two terms are often used interchangeably; however, there are subtle differences. Fog Computing market is currently valued at $22.3 million in 2018; it is expected to expand at an explosive rate to grow to around $205 million over the next five years. With such high level of connectivity of smart devices, Fog Computing appears to be the next big thing for the IoT vision.

Although the Cloud paradigm is highly attractive for data-intensive processing requirements, there are a number of inherent limitations that the Fog vision aims to resolve: by reducing the Internet/service latency, increasing the context awareness, conserving the network bandwidth, improving the quality of provision, enhancing the operational efficiency, providing the improved user experience and speeding up the real-time processing as well as storing of sensitive data close to where the data are generated. Fog paradigm also provides better support for mobility and the use of mobile communication.

In brief, whereas in Cloud Computing, response times are in minutes and often days, data storage periods are more longer time and often permanent, and location coverage is too wide and often global; in Fog Computing, response times are in milliseconds or sub-seconds, data storage periods are transient, and location coverage is local and with increased awareness. These benefits are mainly due to the localization of nodes in the Fog architecture that supports vertically isolated latency-sensitive applications by providing ubiquitous, scalable, layered and federated network connectivity. Whereas the Cloud environments are often geographically a long way away from the end-user devices and rely heavily on the wider Internet bandwidths, the Fog services are much closer to end users with dense geographical distribution . Fog Computing is currently being successfully deployed in numerous commercial and industrial application scenarios that require both Fog localization and Cloud globalization, including: smart grids, smart homes and cities, connected vehicles, self-drive cars and trains, traffic light system, healthcare management and numerous other cyber-physical systems.

With this background, this book aims to investigate development approaches, architectural mechanisms and measurement metrics for building smart adaptable environments and discuss the key-related topics such as device connectivity, security and interoperability and communication methods. Thirty-four researchers and practitioners of international repute have presented latest research on frameworks, technologies, current trends and suggestions for further enhancement of the IoT, Fog Computing and related paradigms.

Hopefully, Fog Computing: Concepts, Frameworks and Technologies will fill a gap with respect to Fog Computing environment and architecture, and extend the existing body of knowledge in this field.

There are thirteen chapters in Fog Computing: Concepts, Frameworks and Technologies. These are organized into three parts:

Part I: Concepts and Principles

This section has a focus on concepts and underlying principles relating to the Fog Computing vision. There are four contributions. The first chapter introduces the concepts and principles of Fog Computing and discusses the related IoT paradigms such as Cloud Computing. The second contribution presents a detailed literature analysis and outlines the characteristics, models, theories and different application scenarios relating to Fog Computing. The next chapter presents the key issues surrounding the Cloud and Fog paradigms and proposes emerging dimensions discussing the Fog Computing landscape in Africa. The final contribution explores the contributing factors for the adoption of Fog Computing, using the TOE framework approach, with a special focus on developing countries.

Part II: Frameworks and Technologies

This part of the book comprises five chapters that focus on methodologies and technologies. The first chapter provides an analysis of sensor data formats in the context of smart cities and develops a data retrieval tool for filtering the open datasets. The next contribution discusses the performance issues and presents means of achieving high performance in the case of Fog environments. The third chapter analyses users expectations in terms of QoE aspect of Fog environments and suggests User Expectations Metrics to determine the QoE. The next contribution proposes software engineering approaches for Fog-based architectural design using UML and the four-step-rule-set method. The final submission presents a data utility model for developing data-intensive application s for Fog environments.