Fog and Edge Computing Principles and Paradigms

A comprehensive guide to Fog and Edge applications, architectures, and technologies

Recent years have seen the explosive growth of the Internet of Things (IoT): the internet-connected network of devices that includes everything from personal electronics and home appliances to automobiles and industrial machinery. Responding to the ever-increasing bandwidth demands of the IoT, Fog and Edge computing concepts have developed to collect, analyze, and process data more efficiently than traditional cloud architecture.

Fog and Edge Computing: Principles and Paradigms provides a comprehensive overview of the state-of-the-art applications and architectures driving this dynamic field of computing while highlighting potential research directions and emerging technologies. 

Exploring topics such as developing scalable architectures, moving from closed systems to open systems, and ethical issues rising from data sensing, this timely book addresses both the challenges and opportunities that Fog and Edge computing presents. Contributions from leading IoT experts discuss federating Edge resources, middleware design issues, data management and predictive analysis, smart transportation and surveillance applications, and more. A coordinated and integrated presentation of topics helps readers gain thorough knowledge of the foundations, applications, and issues that are central to Fog and Edge computing. This valuable resource:

• Provides insights on transitioning from current Cloud-centric and 4G/5G wireless environments to Fog Computing
• Examines methods to optimize virtualized, pooled, and shared resources
• Identifies potential technical challenges and offers suggestions for possible solutions
• Discusses major components of Fog and Edge computing architectures such as middleware, interaction protocols, and autonomic management
• Includes access to a website portal for advanced online resources 

Fog and Edge Computing: Principles and Paradigms is an essential source of up-to-date information for systems architects, developers, researchers, and advanced undergraduate and graduate students in fields of computer science and engineering.

Rajkumar Buyya, PhD, is Redmond Barry Distinguished Professor and Director of the Cloud Computing and Distributed Systems Laboratory, University of Melbourne, Australia and founding CEO of Manjrasoft. Dr. Buyya is author of several works including Mastering Cloud Computing and Editor-in-Chief of Wiley Software: Practice and Experience Journal.

Satish Narayana Srirama, PhD, is a Research Professor and head of the Mobile & Cloud Lab, Institute of Computer Science, University of Tartu, Estonia. He is editor of Wiley Software: Practice and Experience Journal and has co-authored over 120 scientific publications.

List of Contributors xix

Preface xxiii

Acknowledgments xxvii

Part I Foundations 1

1 Internet of Things (IoT) and New Computing Paradigms 3
Chii Chang, Satish Narayana Srirama, and Rajkumar Buyya

1.1 Introduction 3

1.2 Relevant Technologies 6

1.3 Fog and Edge Computing Completing the Cloud 8

1.3.1 Advantages of FEC: SCALE 8

1.3.2 How FEC AchievesThese Advantages: SCANC 9

1.4 Hierarchy of Fog and Edge Computing 13

1.5 Business Models 16

1.6 Opportunities and Challenges 17

1.7 Conclusions 20

References 21

2 Addressing the Challenges in Federating Edge Resources 25
Ahmet Cihat Baktir, Cagatay Sonmez, CemErsoy, Atay Ozgovde, and Blesson Varghese

2.1 Introduction 25

2.2 The Networking Challenge 27

2.3 The Management Challenge 34

2.4 Miscellaneous Challenges 40

2.5 Conclusions 45

References 45

3 Integrating IoT + Fog + Cloud Infrastructures: System Modeling and Research Challenges 51
Guto Leoni Santos,Matheus Ferreira, Leylane Ferreira, Judith Kelner, Djamel Sadok, Edison Albuquerque, Theo Lynn, and Patricia Takako Endo

3.1 Introduction 51

3.2 Methodology 52

3.3 Integrated C2F2T Literature by Modeling Technique 55

3.4 Integrated C2F2T Literature by Use-Case Scenarios 65

3.5 Integrated C2F2T Literature by Metrics 68

3.6 Future Research Directions 72

3.7 Conclusions 73

Acknowledgments 74

References 75

4 Management and Orchestration of Network Slices in 5G, Fog, Edge, and Clouds 79
Adel Nadjaran Toosi, RedowanMahmud, Qinghua Chi, and Rajkumar Buyya

4.1 Introduction 79

4.2 Background 80

4.3 Network Slicing in 5G 83

4.4 Network Slicing in Software-Defined Clouds 87

4.5 Network Slicing Management in Edge and Fog 91

4.6 Future Research Directions 93

4.7 Conclusions 96

Acknowledgments 96

References 96

5 Optimization Problems in Fog and Edge Computing 103
Zoltán Ádám Mann

5.1 Introduction 103

5.2 Background / RelatedWork 104

5.3 Preliminaries 105

5.4 The Case for Optimization in Fog Computing 107

5.5 Formal Modeling Framework for Fog Computing 108

5.6 Metrics 109

5.6.5 Further Quality Attributes 112

5.7 Optimization Opportunities along the Fog Architecture 113

5.8 Optimization Opportunities along the Service Life Cycle 114

5.9 Toward a Taxonomy of Optimization Problems in Fog Computing 115

5.10 Optimization Techniques 117

5.11 Future Research Directions 118

5.12 Conclusions 119

Acknowledgments 119

References 119

Part II Middlewares 123

6 Middleware for Fog and Edge Computing: Design Issues 125
Madhurima Pore, Vinaya Chakati, Ayan Banerjee, and Sandeep K. S. Gupta

6.1 Introduction 125

6.2 Need for Fog and Edge Computing Middleware 126

6.3 Design Goals 126

6.4 State-of-the-Art Middleware Infrastructures 128

6.5 System Model 129

6.6 Proposed Architecture 131

6.7 Case Study Example 136

6.8 Future Research Directions 137

6.9 Conclusions 139

References 139

7 A Lightweight Container Middleware for Edge Cloud Architectures 145
David von Leon, LorenzoMiori, Julian Sanin, Nabil El Ioini, Sven Helmer, and Claus Pahl

7.1 Introduction 145

7.2 Background/RelatedWork 146

7.3 Clusters for Lightweight Edge Clouds 149

7.4 Architecture Management – Storage and Orchestration 152

7.5 IoT Integration 159

7.6 Security Management for Edge Cloud Architectures 159

7.7 Future Research Directions 165

7.8 Conclusions 166

References 167

8 Data Management in Fog Computing 171
Tina Samizadeh Nikoui, Amir Masoud Rahmani, and Hooman Tabarsaied

8.1 Introduction 171

8.2 Background 172

8.3 Fog Data Management 174

8.4 Future Research and Direction 186

8.5 Conclusions 186

References 188

9 Predictive Analysis to Support Fog Application Deployment 191
Antonio Brogi, Stefano Forti, and Ahmad Ibrahim

9.1 Introduction 191

9.2 Motivating Example: Smart Building 193

9.3 Predictive Analysis with FogTorch 197

9.4 Motivating Example (continued) 206

9.5 Related Work 207

9.6 Future Research Directions 214

9.7 Conclusions 216

References 217

10 Using Machine Learning for Protecting the Security and Privacy of Internet of Things (IoT) Systems 223
Melody Moh and Robinson Raju

10.1 Introduction 223

10.2 Background 234

10.3 Survey of ML Techniques for Defending IoT Devices 242

10.4 Machine Learning in Fog Computing 248

10.4.1 Introduction 248

10.5 Future Research Directions 252

10.6 Conclusions 252

References 253

Part III Applications and Issues 259

11 Fog Computing Realization for Big Data Analytics 261
Farhad Mehdipour, Bahman Javadi, AniketMahanti, and Guillermo Ramirez-Prado

11.1 Introduction 261

11.2 Big Data Analytics 262

11.3 Data Analytics in the Fog 267

11.4 Prototypes and Evaluation 272

11.4.1 Architecture 272

11.4.2 Configurations 274

11.5 Case Studies 277

11.6 Related Work 282

11.7 Future Research Directions 287

11.8 Conclusions 287

References 288

12 Exploiting Fog Computing in Health Monitoring 291
Tuan Nguyen Gia and Mingzhe Jiang

12.1 Introduction 291

12.2 An Architecture of a Health Monitoring IoT-Based System with Fog Computing 293

12.3 Fog Computing Services in Smart E-Health Gateways 297

12.4 System Implementation 304

12.5 Case Studies, Experimental Results, and Evaluation 308

12.6 Discussion of Connected Components 313

12.7 Related Applications in Fog Computing 313

12.8 Future Research Directions 314

12.9 Conclusions 314

References 315

13 Smart Surveillance Video Stream Processing at the Edge for Real-Time Human Objects Tracking 319
Seyed Yahya Nikouei, Ronghua Xu, and Yu Chen

13.1 Introduction 319

13.2 Human Object Detection 320

13.3 Object Tracking 327

13.4 Lightweight Human Detection 335

13.5 Case Study 337

13.6 Future Research Directions 342

13.7 Conclusions 343

References 343

14 Fog Computing Model for Evolving Smart Transportation Applications 347
M. Muzakkir Hussain,Mohammad Saad Alam, and M.M. Sufyan Beg

14.1 Introduction 347

14.2 Data-Driven Intelligent Transportation Systems 348

14.3 Mission-Critical Computing Requirements of Smart Transportation Applications 351

14.4 Fog Computing for Smart Transportation Applications 354

14.5 Case Study: Intelligent Traffic Lights Management (ITLM) System 359

14.6 Fog Orchestration Challenges and Future Directions 362

14.7 Future Research Directions 364

14.8 Conclusions 369

References 370

15 Testing Perspectives of Fog-Based IoT Applications 373
Priyanka Chawla and Rohit Chawla

15.1 Introduction 373

15.2 Background 374

15.3 Testing Perspectives 376

15.4 Future Research Directions 393

15.5 Conclusions 405

References 406

16 Legal Aspects of Operating IoT Applications in the Fog 411
G. Gultekin Varkonyi, Sz. Varadi, and Attila Kertesz

16.1 Introduction 411

16.2 RelatedWork 412

16.3 Classification of Fog/Edge/IoT Applications 413

16.4 Restrictions of the GDPR Affecting Cloud, Fog, and IoT Applications 414

16.5 Data Protection by Design Principles 425

16.6 Future Research Directions 430

16.7 Conclusions 430

Acknowledgment 431

References 431

17 Modeling and Simulation of Fog and Edge Computing Environments Using iFogSim Toolkit 433
Redowan Mahmud and Rajkumar Buyya

17.1 Introduction 433

17.2 iFogSim Simulator and Its Components 435

17.3 Installation of iFogSim 436

17.4 Building Simulation with iFogSim 437

17.5 Example Scenarios 438

17.6 Simulation of a Placement Policy 450

17.7 A Case Study in Smart Healthcare 461

17.8 Conclusions 463

References 464

Index 467

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