The Internet of Things (IoT) paradigm promises to make “things” such as physical objects with sensing capabilities and/or attached with tags, mobile objects such as smart phones and vehicles, consumer electronic devices, and home appliances such as refrigerators, televisions, and healthcare devices as part of the Internet environment. In cloud‐centric IoT (CIoT) applications, the sensor data from these “things” is extracted, accumulated, and processed at the public/private clouds, leading to significant latencies. Fog computing addresses this issue in developing real‐time IoT applications, by mainly utilizing proximity‐based computational resources across the IoT layers such as gateways, cloudlets, and network switches/routers. A similar approach of utilizing proximity resources in the telecommunication domain is mobile edge computing.

To realize the full potential of fog and edge computing and similar paradigms, researchers and practitioners need to address several challenges and develop suitable conceptual and technological solutions for tackling them. These include development of scalable architectures, moving from closed systems to open systems, dealing with privacy and ethical issues involved in data sensing, storage, processing, and actions, designing interaction protocols, and autonomic management.

The primary purpose of this book is to capture the state‐of‐the‐art in fog and edge computing, their applications, architectures, and technologies. The book also aims to identify potential research directions and technologies that will facilitate insight generation in various domains from smart home, smart cities, science, industry, business, and consumer applications. We expect the book to serve as a reference for larger audiences such as system architects, practitioners, developers, new researchers, and graduate‐level students. This book also comes with an associated website (hosted at http://cloudbus.org/fog/book/) containing pointers to advanced on‐line resources.

Organization of the Book

This book contains chapters authored by several leading experts in the fields of IoT, cloud, and fog computing. The book is presented in a coordinated and integrated manner, starting with the fundamentals and followed by the middleware and technological solutions to implement fog and edge‐related applications.

The contents of the book are organized into three parts:

1. Foundations
2. Middlewares
3. Applications and Issues

Part I focuses on Foundations and is made up of five chapters. The first chapter, “Internet of Things (IoT) and New Computing Paradigms,” discusses the IoT paradigm along with CIoT limitations. The relevant technologies and new computing paradigms that address these limitations such as fog computing, edge computing and mist computing, are discussed along with their main advantages and basic mechanisms. The hierarchy of fog and edge computing environments is discussed, and the opportunities and challenges offered by fog and edge computing are discussed thoroughly. The challenges along with their future research directions are further structured into networking, management, and resource and modeling challenges, in Chapter 2, “Addressing the Challenges in Federating Edge Resources.” The use of modelling techniques and the relevant literature to represent and evaluate an integrated cloud‐to‐things system comprising cloud computing, fog computing, and the IoT is reviewed in Chapter 3, “Integrating IoT + Fog + Cloud Infrastructures: System Modeling and Research Challenges.” The state‐of‐the‐art literature on network slicing in 5G, edge/fog, and cloud computing is reviewed in Chapter 4, “Management and Orchestration of Network Slices in 5G, Fog, Edge, and Clouds.” Part I concludes with a discussion of generic conceptual framework for optimization problems in fog computing, based on consistent, well‐defined, and formalized notation for constraints and optimization objectives, in Chapter 5, “Optimization Problems in Fog and Edge Computing.”

Part II focuses on Middlewares and is made up of five chapters. Chapter 6, “Middleware for Fog and Edge Computing: Design Issues,” discusses different aspects of the design of middleware for Fog and Edge computing along with a proposed architecture. Chapter 7, “A Lightweight Container Middleware for Edge Cloud Architectures,” discusses the core principles of an edge cloud reference architecture that is based on containers as the packaging and distribution mechanism. The chapter also provides experimental results with Raspberry Pi clusters to validate the proposed architectural solution. Chapter 8, “Data Management in Fog Computing,” proposes the conceptual architecture for the data management in fog computing environments. The chapter also provides a review of the fog data management, along with future research directions. Chapter 9, “Predictive Analysis to Support Fog Application Deployment,” discusses FogTorchΠ prototype that supports application deployment in the fog. The prototype permits expression of processing capabilities, predicts QoS attributes, and estimates operational costs of a fog infrastructure, along with processing and QoS requirements of an application. Chapter 10, “Using Machine Learning for Protecting the Security and Privacy of Internet of Things (IoT) Systems,” reviews the machine learning (ML) techniques for defending IoT devices, along with a discussion on scope of ML in fog computing.

Part III focuses on Applications and relevant issues and is made up of seven chapters. Chapter 11, “Fog Computing Realization for Big Data Analytics,” discusses a fog‐engine prototype that can be deployed in the traditional centralized data analytics platform to realize the data analytics in the fog environment. Smart home and smart nutrition monitoring system case studies are provided, which conceptually utilize the fog‐engine. Chapter 12, “Exploiting Fog Computing in Health Monitoring,” discussed fog computing services in smart e‐health gateways. The proposed system is implemented and evaluated with a remote ECG (electrocardiogram) monitoring case study. Chapter 13 discussed “Smart Surveillance Video Stream Processing at the Edge for Real‐Time Human Objects Tracking.” The computations and algorithms used at the fog and edge levels to create such automated surveillance system are discussed and compared. Chapter 14, “Fog Computing Model for Evolving Smart Transportation Applications,” identified the computing needs of the data‐driven transportation architecture and devised a fog‐assisted cloud‐based computational platform for smart transportation applications, in the context of intelligent traffic management system (ITSM) use case. Chapter 15 discussed and reviewed “Testing Perspectives of Fog‐Based IoT Applications,” in the smart home, smart health, and smart transport domains. Chapter 16, “Legal Aspects of Operating IoT Applications in the Fog,” classified fog/edge/IoT applications, analyzed the latest restrictions introduced by the General Data Protection Regulation (GDPR), and discussed how these legal constraints affect the design and operation of IoT applications in fog and cloud environments. Another critical issue related to fog application development is that it is very costly due to the fact that the fog computing environment incorporates IoT devices, fog nodes, and cloud datacenters, along with a huge amount of IoT data. To address this, Chapter 17 discussed “Modeling and Simulation of Fog and Edge Computing Environments Using iFogSim Toolkit.” iFogSim simulator components are discussed and installation details are provided, along with detailed guidelines to model the fog environment.