Cover
List of Contributors
Preface
1. Organization of the Book
Acknowledgments
Part I: Foundations
1. 1 Internet of Things (IoT) and New Computing Paradigms
  1. 1.1 Introduction
  2. 1.2 Relevant Technologies
  3. 1.3 Fog and Edge Computing Completing the Cloud
  4. 1.4 Hierarchy of Fog and Edge Computing
  5. 1.5 Business Models
  6. 1.6 Opportunities and Challenges
  7. 1.7 Conclusions
  8. References
2. 2 Addressing the Challenges in Federating Edge Resources
  1. 2.1 Introduction
  2. 2.2 The Networking Challenge
  3. 2.3 The Management Challenge
  4. 2.4 Miscellaneous Challenges
  5. 2.5 Conclusions
  6. References
3. 3 Integrating IoT + Fog + Cloud Infrastructures: System Modeling and Research Challenges
  1. 3.1 Introduction
  2. 3.2 Methodology
  3. 3.3 Integrated C2F2T Literature by Modeling Technique
  4. 3.4 Integrated C2F2T Literature by Use‐Case Scenarios
  5. 3.5 Integrated C2F2T Literature by Metrics
  6. 3.6 Future Research Directions
  7. 3.7 Conclusions
  8. Acknowledgments
  9. References
4. 4 Management and Orchestration of Network Slices in 5G, Fog, Edge, and Clouds
  1. 4.1 Introduction
  2. 4.2 Background
  3. 4.3 Network Slicing in 5G
  4. 4.4 Network Slicing in Software‐Defined Clouds
  5. 4.5 Network Slicing Management in Edge and Fog
  6. 4.6 Future Research Directions
  7. 4.7 Conclusions
  8. Acknowledgments
  9. References
5. 5 Optimization Problems in Fog and Edge Computing
  1. 5.1 Introduction
  2. 5.2 Background / Related Work
  3. 5.3 Preliminaries
  4. 5.4 The Case for Optimization in Fog Computing
  5. 5.5 Formal Modeling Framework for Fog Computing
  6. 5.6 Metrics
  7. 5.7 Optimization Opportunities along the Fog Architecture
  8. 5.8 Optimization Opportunities along the Service Life Cycle
  9. 5.9 Toward a Taxonomy of Optimization Problems in Fog Computing
  10. 5.10 Optimization Techniques
  11. 5.11 Future Research Directions
  12. 5.12 Conclusions
  13. Acknowledgments
  14. References
Part II: Middlewares
1. 6 Middleware for Fog and Edge Computing: Design Issues
  1. 6.1 Introduction
  2. 6.2 Need for Fog and Edge Computing Middleware
  3. 6.3 Design Goals
  4. 6.4 State‐of‐the‐Art Middleware Infrastructures
  5. 6.5 System Model
  6. 6.6 Proposed Architecture
  7. 6.7 Case Study Example
  8. 6.8 Future Research Directions
  9. 6.9 Conclusions
  10. References
2. 7 A Lightweight Container Middleware for Edge Cloud Architectures
  1. 7.1 Introduction
  2. 7.2 Background/Related Work
  3. 7.3 Clusters for Lightweight Edge Clouds
  4. 7.4 Architecture Management – Storage and Orchestration
  5. 7.5 IoT Integration
  6. 7.6 Security Management for Edge Cloud Architectures
  7. 7.7 Future Research Directions
  8. 7.8 Conclusions
  9. References
3. 8 Data Management in Fog Computing
  1. 8.1 Introduction
  2. 8.2 Background
  3. 8.3 Fog Data Management
  4. 8.4 Future Research and Direction
  5. 8.5 Conclusions
  6. References
4. 9 Predictive Analysis to Support Fog Application Deployment
  1. 9.1 Introduction
  2. 9.2 Motivating Example: Smart Building
  3. 9.3 Predictive Analysis with FogTorchΠ
  4. 9.4 Motivating Example (continued)
  5. 9.5 Related Work
  6. 9.6 Future Research Directions
  7. 9.7 Conclusions
  8. References
5. 10 Using Machine Learning for Protecting the Security and Privacy of Internet of Things (IoT) Systems
  1. 10.1 Introduction
  2. 10.2 Background
  3. 10.3 Survey of ML Techniques for Defending IoT Devices
  4. 10.4 Machine Learning in Fog Computing
  5. 10.5 Future Research Directions
  6. 10.6 Conclusions
  7. References
Part III: Applications and Issues
1. 11 Fog Computing Realization for Big Data Analytics
  1. 11.1 Introduction
  2. 11.2 Big Data Analytics
  3. 11.3 Data Analytics in the Fog
  4. 11.4 Prototypes and Evaluation
  5. 11.5 Case Studies
  6. 11.6 Related Work
  7. 11.7 Future Research Directions
  8. 11.8 Conclusions
  9. References
2. 12 Exploiting Fog Computing in Health Monitoring
  1. 12.1 Introduction
  2. 12.2 An Architecture of a Health Monitoring IoT‐Based System with Fog Computing
  3. 12.3 Fog Computing Services in Smart E‐Health Gateways
  4. 12.4 System Implementation
  5. 12.5 Case Studies, Experimental Results, and Evaluation
  6. 12.6 Discussion of Connected Components
  7. 12.7 Related Applications in Fog Computing
  8. 12.8 Future Research Directions
  9. 12.9 Conclusions
  10. References
3. 13 Smart Surveillance Video Stream Processing at the Edge for Real‐Time Human Objects Tracking
  1. 13.1 Introduction
  2. 13.2 Human Object Detection
  3. 13.3 Object Tracking
  4. 13.4 Lightweight Human Detection
  5. 13.5 Case Study
  6. 13.6 Future Research Directions
  7. 13.7 Conclusions
  8. References
4. 14 Fog Computing Model for Evolving Smart Transportation Applications
  1. 14.1 Introduction
  2. 14.2 Data‐Driven Intelligent Transportation Systems
  3. 14.3 Mission‐Critical Computing Requirements of Smart Transportation Applications
  4. 14.4 Fog Computing for Smart Transportation Applications
  5. 14.5 Case Study: Intelligent Traffic Lights Management (ITLM) System
  6. 14.6 Fog Orchestration Challenges and Future Directions
  7. 14.7 Future Research Directions
  8. 14.8 Conclusions
  9. References
5. 15 Testing Perspectives of Fog‐Based IoT Applications
  1. 15.1 Introduction
  2. 15.2 Background
  3. 15.3 Testing Perspectives
  4. 15.4 Future Research Directions
  5. 15.5 Conclusions
  6. References
6. 16 Legal Aspects of Operating IoT Applications in the Fog
  1. 16.1 Introduction
  2. 16.2 Related Work
  3. 16.3 Classification of Fog/Edge/IoT Applications
  4. 16.4 Restrictions of the GDPR Affecting Cloud, Fog, and IoT Applications
  5. 16.5 Data Protection by Design Principles
  6. 16.6 Future Research Directions
  7. 16.7 Conclusions
  8. Acknowledgment
  9. References
7. 17 Modeling and Simulation of Fog and Edge Computing Environments Using iFogSim Toolkit
  1. 17.1 Introduction
  2. 17.2 iFogSim Simulator and Its Components
  3. 17.3 Installation of iFogSim
  4. 17.4 Building Simulation with iFogSim
  5. 17.5 Example Scenarios
  6. 17.6 Simulation of a Placement Policy
  7. 17.7 A Case Study in Smart Healthcare
  8. 17.8 Conclusions
  9. References
Index
End User License Agreement

List of Tables

Chapter 2
1. Table 2.1 Network challenges, their causes and potential solutions in federating...
2. Table 2.2 Management challenges, the need for addressing them, and potential sol...
Chapter 3
1. Table 3.1 Summary of systematic search results.
2. Table 3.2 Articles about modelling of IoT, fog, and cloud integration.
3. Table 3.3 Scenarios presented in articles.
4. Table 3.4 Metrics observed in articles.
Chapter 4
1. Table 4.1 Acronyms and abbreviations.
2. Table 4.2 Network‐aware virtual machines management.
3. Table 4.3 Virtual machine migration planning.
4. Table 4.4 Virtual network functions management projects.
Chapter 5
1. Table 5.1 Notation overview.
2. Table 5.2 Classification of the work of Do et al. [21] according to the presente...
3. Table 5.3 Classification of the work of Sardellitti et al. [22] according to the...
4. Table 5.4 Classification of the work of Mushunuri et al. [23] according to the p...
Chapter 6
1. Table 6.1 Middleware features in fog and edge architectures.
Chapter 7
1. Table 7.1 Speed and power consumption of the Raspberry Pi cluster. Adapted from ...
2. Table 7.2 Approximate costs of the Raspberry Pi cluster.
3. Table 7.3 Time comparison – listing the overall, the mean, and the maximal time ...
4. Table 7.4 Comparison of the power consumption while idling and under load.
5. Table 7.5 Power consumption of the Raspberry Pi cluster while idling and under l...
Chapter 9
1. Table 9.1 Hardware specification for different VM types.
2. Table 9.2 QoS profiles associated to the communication links.
3. Table 9.3 Eligible deployments generated by FogTorchΠ for Q1 and Q2.¹⁴
4. Table 9.4 Result of FogTorchΠ for the VR Game.
Chapter 10
1. Table 10.1 OWASP IoT attack surface areas.
2. Table 10.2 Categorization of ML solutions for IOT security.
3. Table 10.3 Categorization of ML solutions for outlier detection.
4. Table 10.4 Where data should be processed.
5. Table 10.5 ML Use cases for fog computing.
6. Table 10.6 Machine‐learning algorithms at different fog layers.
Chapter 11
1. Table 11.1 Data analytics using a fog‐engine and the cloud.
2. Table 11.2 Comparison of various schemes with and without a fog‐engine, where ba...
3. Table 11.3 List of IOT solutions from five major cloud providers.
Chapter 12
1. Table 12.1 Energy consumption of the sensor node with and without running AES.
Chapter 14
1. Table 14.1 Application use cases for data‐driven intelligent transportation appl...
2. Table 14.2 Performance comparison of cloud and fog computing models in smart tra...
3. Table 14.3 Computing requirements of intelligent traffic light management (ITLM)...
Chapter 15
1. Table 15.1 Outline of the work done to test smart homes.
2. Table 15.2 Outline of the work done to test smart health.
3. Table 15.3 Outline of the work done to test smart transport.
4. Table 15.4 Summary of limitations and research directions for smart home.
5. Table 15.5 Summary of limitations and research directions for smart health.
6. Table 15.6 Summary of limitations and research directions for smart transport.

List of Illustrations

Chapter 1
1. Figure 1.1 IoT applications and environments with supporting computing p...
2. Figure 1.2 FEC nodes supports five basic mechanisms—storage, compute, ac...
3. Figure 1.3 Hierarchy of fog and edge computing.
Chapter 2
1. Figure 2.1 Networking and management challenges in federating edge resou...
2. Figure 2.2 Fog computing with SDN as the network orchestrator.
3. Figure 2.3 Resource and modeling challenges in federating edge resources...
Chapter 3
1. Figure 3.1 Integration of IoT devices with fog and cloud computing.
2. Figure 3.2 Systematic review steps. Adapted from [8].
3. Figure 3.3 The most approaches used to model the integration among cloud...
4. Equation 3.1 Scheme proposed in [10].
5. Equation 3.2 Energy consumption model presented in [17].
6. Equation 3.3 Billing model presented in [15].
7. Equation 3.4 Calculate of energy consumption between IoT and c...
8. Equation 3.5 Calculate of energy consumption between IoT and f...
9. Equation 3.6 Reliability equation presented in [16].
10. Figure 3.4 Petri Net model proposed in [26]. © Elsevier. Reproduced with...
11. Petri Net model presented in [29]. © Elsevier. Reproduced with the pe...
12. Equation 3.7 Objective function used in ILP model presented in [28].
13. Figure 3.6 Markov model presented in [21]. Adapted from [21].
14. Figure 3.7Figure 3.7 Fuzz‐ontology model proposed in [13]. © Elsevier. Rep...
Chapter 4
1. Figure 4.1 Generic 5G slicing framework.
2. Figure 4.2 Taxonomy of network‐aware VM/VNF Management in software‐defin...
Chapter 5
1. Figure 5.1 Number of (a) papers and (b) citations in fog computing.
2. Figure 5.2 Number of (a) papers and (b) citations about optimization in ...
3. Figure 5.3 Three‐layer model of fog computing.
4. Figure 5.4 Total execution time of an example computation offloading sce...
Chapter 6
1. Figure 6.1 Fog and edge computing devices.
2. Figure 6.2 Fog and edge computing architecture.
Chapter 7
1. Figure 7.1 Edge cloud architecture.
2. Figure 7.2 Simplified container orchestration plan for the ski resort ca...
3. Figure 7.3 Overall orchestration flow.
4. Figure 7.4 Blockchain‐based IoT orchestration and security management.
5. Figure 7.5 Provenance model. Adapted from W3C. “PROV Model Primer,” Ap...
6. Figure 7.6 Blockchain‐based tracking of an orchestration plan.
7. Figure 7.7 Architecture of the blockchain integration.
Chapter 8
1. Figure 8.1 Structure of data management in fog computing.
2. Figure 8.2 Basic data management diagram in fog computing.
3. Figure 8.3 Data life cycle in fog computing.
4. Figure 8.4 A simple sequence diagram of an e‐health application.
5. Figure 8.5 Proposed architecture.
6. Figure 8.6 Interaction of the main process in proposed architecture.
Chapter 9
1. Figure 9.1 Fog application of the motivating example.¹
2. Figure 9.2 Fog infrastructure of the motivating example.⁴
3. Figure 9.3 Bird's‐eye view of FogTorchΠ.
4. Figure 9.4 Pseudocode of the exhaustive search algorithm.
5. Figure 9.5 Search space to find eligible deployments of A to I .
6. Figure 9.6 Pseudocode for the backtracking search.
7. Figure 9.7 Pseudocode of the Monte Carlo simulation in FogTorchΠ.
8. Figure 9.8 Bernoulli sampling function example.
9. Figure 9.9 Results for Q1(a) and Q1(b).¹⁵
10. Figure 9.10 Results¹⁶ for Q2.
11. Figure 9.11 VR Game application.
12. Figure 9.12 VR Game infrastructure.¹⁸
Chapter 10
1. Figure 10.1 IoT system with threats and protections annotated.
2. Figure 10.2 Privacy vulnerabilities in IoT
3. Figure 10.3 Privacy threats with entities and information flows in IoT. ...
4. Figure 10.4 DDoS attack.
5. Figure 10.5 Ensemble machine learning.
6. Figure 10.6 Fog computing security at multiple layers.
Chapter 11
1. Figure 11.1 Typical data analytics flow.
2. Figure 11.2 Deployment of FE in a typical cloud‐based computing system. ...
3. Figure 11.3 Data analytics using a fog‐engine before offloading to the c...
4. Figure 11.4 (a) General architecture of fog‐engine; (b) A detailed archi...
5. Figure 11.5 Various configurations of fog‐engine: (a) as a broker; (b) a...
6. Figure 11.6 Fog‐engine collects data and communicates with the cloud.
7. Figure 11.7 Data transmission time between FEs and cloud for various pac...
8. Figure 11.8 The deployment of fog‐engines in the system pipeline.
9. Figure 11.9 Architecture of the smart nutrition monitoring system.
10. Figure 11.10 Prototype of the smart nutrition monitoring system.
Chapter 12
1. Figure 12.1 Architecture of remote real‐time health‐monitoring IoT syste...
2. Figure 12.2 Fog services in a smart gateway.
3. Figure 12.3 Acceleration and angular velocity changes during a fall.
4. Figure 12.4 Multilevel threshold based fall detection algorithm.
5. Figure 12.5 SVM of 3‐D acceleration and SVM of 3‐D angular velocity.
6. Figure 12.6 Real‐time ECG monitoring and preprocess ECG data at fog.
7. Figure 12.7 RMS of HRV features in different window lengths and differen...
8. Figure 12.8 ROC curves of No pain and Pain classification.
Chapter 13
1. Figure 13.1 Edge‐fog‐cloud‐based hierarchy smart surveillance architectu...
2. Figure 13.2 Haar‐like features: (a) two rectangular features; (b) three ...
3. Figure 13.3 (a) Histogram of oriented gradients; (b) representation of H...
4. Figure 13.4 An example of multi‐detection for a single object.
5. Figure 13.5 Object tracking methods.
6. Figure 13.6 Different motion constrains.
7. Figure 13.7 KCF tracking process.
8. Figure 13.8 Convolutional filter vs. separable depthwise convolutional f...
9. Figure 13.9 Results of Haar cascaded human detection.
10. Figure 13.10 Performance of HOG+SVM algorithm.
11. Figure 13.11 Example of light version CNN for human object detection.
12. Figure 13.12 An example of multi‐object tracking.
13. Figure 13.13 An example of object tracker phase in and out.
14. Figure 13.14 An example of re‐tracking after target lost.
Chapter 14
1. Figure 14.1 Key components of a data‐driven ITS [12].
2. Figure 14.2 Topology of FOG computing paradigm for smart transportation ...
3. Figure 14.3 Data/Control Flow among FCNs in Layer 2
4. Figure 14.4 An orchestration scenario for intelligent traffic management...
5. Figure 14.5 Functional elements of a typical fog orchestrator showing th...
6. Figure 14.6 The conceptual framework for BD²A and optimization of ITLM b...
Chapter 16
1. Figure 16.1 Data management in fog environments.
Chapter 17
1. Figure 17.1 Interactions among IoT‐enabled systems, fog and cloud compu...
2. Figure 17.2 High‐level view of interactions among iFogSim components.
3. Figure 17.3 Master–worker application model.
4. Figure 17.4 Sequential unidirectional dataflow application model.
5. Figure 17.5 Network topology for the placement policy.
6. Figure 17.6 Application model for the placement policy.
7. Figure 17.7 Flowchart of the application placement policy.
8. Figure 17.8 Fog environment for IoT‐enabled healthcare case study.
9. Figure 17.9 Application model for IoT‐enabled healthcare case study.

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Library of Congress Cataloging‐in‐Publication Data

Names: Buyya, Rajkumar, 1970‐ editor. | Srirama, Satish Narayana, 1978‐

editor.

Title: Fog and edge computing : principles and paradigms / edited by Rajkumar

Buyya, Satish Narayana Srirama.

Description: Hoboken, NJ, USA : John Wiley & Sons, Inc., 2019. | Series:

Wiley series on parallel and distributed computing | Includes

bibliographical references and index. |

Identifiers: LCCN 2018054742 (print) | LCCN 2018057015 (ebook) | ISBN

9781119525011 (Adobe PDF) | ISBN 9781119525066 (ePub) | ISBN 9781119524984

(hardcover)

Subjects: LCSH: Cloud computing. | Electronic data processing–Distributed

processing.

Classification: LCC QA76.585 (ebook) | LCC QA76.585 .F63 2019 (print) | DDC

004.67/82–dc23

LC record available at https://lccn.loc.gov/2018054742

Cover design by Wiley

Cover image: © Shaxiaozi/iStock.com

List of Contributors

Zoltán Ádám Mann

University of Duisburg‐Essen

Germany

e‐mail: zoltan.mann@gmail.com

Edison Albuquerque

Universidade de Pernambuco

Brazil

e‐mail: edison@ecomp.poli.br

Mohammad Saad Alam

Aligarh Muslim University

India

e‐mail: saad.alam@zhcet.ac.in

Ahmet Cihat Baktir

Bogazici University

Turkey

e‐mail: cihatbaktir@gmail.com

Ayan Banerjee

Arizona State University

USA

e‐mail: abanerj3@asu.edu

M. M. Sufyan Beg

Aligarh Muslim University

India

e‐mail: mmsbeg@cs.berkely.edu

Antonio Brogi

University of Pisa

Italy

e‐mail: brogi@di.unipi.it

Rajkumar Buyya

University of Melbourne

Australia

e‐mail: rbuyya@unimelb.edu.au

Vinaya Chakati

Arizona State University

USA

e‐mail: vchakati@asu.edu

Chii Chang

University of Tartu

Estonia

e‐mail: chang@ut.ee

Priyanka Chawla

Lovely Professional University

India

e‐mail: priyankamatrix@gmail.com

Rohit Chawla

Apeejay College

India

e‐mail: rc.j2ee@gmail.com

Yu Chen

Binghamton University

USA

e‐mail: ychen@binghamton.edu

Qinghua Chi

University of Melbourne

Australia

e‐mail: chiqinghua@huawei.com

Nabil El Ioini

Free University of Bozen‐Bolzano

Italy

e‐mail: nelioini@unibz.it

Patricia Takako Endo

Universidade de Pernambuco

Dublin City University

Ireland

e‐mail: patricia.endo@upe.br

Cem Ersoy

Bogazici University

Turkey

e‐mail: ersoy@boun.edu.tr

Leylane Ferreira

Universidade Federal de Pernambuco

Brazil

e‐mail: leylane.silva@gprt.ufpe.br

Matheus Ferreira

Universidade de Pernambuco

Brazil

e‐mail: matheus0906.mhci@gmail.com

Stefano Forti

University of Pisa

Italy

e‐mail: stefano.forti@di.unipi.it

Tuan Nguyen Gia

University of Turku

Finland

e‐mail: tuan.nguyengia@utu.fi

Sandeep Kumar S. Gupta

Arizona State University

USA

e‐mail: sandeep.gupta@asu.edu

Sven Helmer

Free University of Bozen‐Bolzano

Italy

e‐mail: shelmer@inf.unibz.it

M. Muzakkir Hussain

Aligarh Muslim University

India

e‐mail: md.muzakkirhussain@zhcet.ac.in

Ahmad Ibrahim

University of Pisa

Italy

e‐mail: ahmad@di.unipi.it

Bahman Javadi

Western Sydney University

Australia

e‐mail: b.javadi@westernsydney.edu.au

Mingzhe Jiang

University of Turku

Finland

e‐mail: mizhji@utu.fi

Judith Kelner

Universidade Federal de Pernambuco

Brazil

e‐mail: jk@gprt.ufpe.br

Attila Kertesz

University of Szeged

Hungary

e‐mail: keratt@inf.u‐szeged.hu

Theo Lynn

Dublin City University

Ireland

e‐mail: theo.lynn@dcu.ie

Aniket Mahanti

University of Auckland

New Zealand

e‐mail: a.mahanti@auckland.ac.nz

Redowan Mahmud

University of Melbourne

Australia

e‐mail: mahmudm@student.unimelb.edu.au

Farhad Mehdipour

New Zealand School of Education and STEM Fern Ltd.

Auckland

New Zealand

e‐mail: farhadm@nzseg.com

Lorenzo Miori

Free University of Bozen‐Bolzano

Italy

e‐mail: memorys60@gmail.com

Melody Moh

San Jose State University

USA

e‐mail: melody.moh@sjsu.edu

Seyed Yahya Nikouei

Binghamton University

USA

e‐mail: snikoue1@binghamton.edu

Tina Samizadeh Nikoui

Islamic Azad University

Iran

e‐mail: tina.samizadeh@srbiau.ac.ir

Atay Ozgovde

Galatasaray University

Turkey

e‐mail: atay.ozgovde@gmail.com

Claus Pahl

Free University of Bozen‐Bolzano

Italy

e‐mail: cpahl@unibz.it

Madhurima Pore

Arizona State University

USA

e‐mail: mpore@asu.edu

Amir Masoud Rahmani

Islamic Azad University & University of Human Development

Iran

e‐mail: rahmani@srbiau.ac.ir

Robinson Raju

San Jose State University

USA

e‐mail: robinson.raju@sjsu.edu

Guillermo Ramirez‐Prado

Unitec Institute of Technology

Auckland

New Zealand

e‐mail: gprado@unitec.ac.nz

Djamel Sadok

Universidade Federal de Pernambuco

Brazil

e‐mail: jamel@gprt.ufpe.br

Julian Sanin

Free University of Bozen‐Bolzano

Italy

e‐mail: Julian.Sanin@stud‐inf.unibz.it

Guto Leoni Santos

Universidade Federal de Pernambuco

Brazil

e‐mail: guto.leoni@gprt.ufpe.br

Cagatay Sonmez

Bogazici University

Turkey

e‐mail: cagataysonmez@hotmail.com

Satish Narayana Srirama

University of Tartu

Estonia

e‐mail: srirama@ut.ee

Hooman Tabarsaied

Islamic Azad University

Iran

e‐mail: h.tabarsaied@yahoo.com

Adel Nadjaran Toosi

University of Melbourne

Australia

e‐mail: adel.nadjaran@unimelb.edu.au

Sz. Varadi

University of Szeged

Hungary

e‐mail: varadiszilvia@juris.u‐szeged.hu

Blesson Varghese

Queen's University Belfast

e‐mail: B.Varghese@qub.ac.uk

G. Gultekin Varkonyi

University of Szeged

Hungary

e‐mail: gizemgv@juris.u‐szeged.hu

David von Leon

Free University of Bozen‐Bolzano

Italy

e‐mail: david@davole.com

Ronghua Xu

Binghamton University

USA

e‐mail: rxu22@binghamton.edu

Preface

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:

Foundations
Middlewares
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.

Fog and Edge Computing

Principles and Paradigms

Copyright

List of Contributors

Preface

Organization of the Book

Acknowledgments

Part I
Foundations