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Fog Computing. Theory and Practice. Edition No. 1. Wiley Series on Parallel and Distributed Computing

  • ID: 5227041
  • Book
  • May 2020
  • 608 Pages
  • John Wiley and Sons Ltd
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Summarizes the current state and upcoming trends within the area of fog computing

Written by some of the leading experts in the field, Fog Computing: Theory and Practice focuses on the technological aspects of employing fog computing in various application domains, such as smart healthcare, industrial process control and improvement, smart cities, and virtual learning environments. In addition, the Machine-to-Machine (M2M) communication methods for fog computing environments are covered in depth.

Presented in two parts - Fog Computing Systems and Architectures, and Fog Computing Techniques and Application - this book covers such important topics as energy efficiency and Quality of Service (QoS) issues, reliability and fault tolerance, load balancing, and scheduling in fog computing systems. It also devotes special attention to emerging trends and the industry needs associated with utilizing the mobile edge computing, Internet of Things (IoT), resource and pricing estimation, and virtualization in the fog environments.

  • Includes chapters on deep learning, mobile edge computing, smart grid, and intelligent transportation systems beyond the theoretical and foundational concepts
  • Explores real-time traffic surveillance from video streams and interoperability of fog computing architectures
  • Presents the latest research on data quality in the IoT, privacy, security, and trust issues in fog computing

Fog Computing: Theory and Practice provides a platform for researchers, practitioners, and graduate students from computer science, computer engineering, and various other disciplines to gain a deep understanding of fog computing.

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List of Contributors xxiii

Acronyms xxix

Part I Fog Computing Systems and Architectures 1

1 Mobile Fog Computing 3
Chii Chang, Amnir Hadachi, Jakob Mass, and Satish Narayana Srirama

1.1 Introduction 3

1.2 Mobile Fog Computing and Related Models 5

1.3 The Needs of Mobile Fog Computing 6

1.3.1 Infrastructural Mobile Fog Computing 7

1.3.2 Land Vehicular Fog 9

1.3.3 Marine Fog 11

1.3.4 Unmanned Aerial Vehicular Fog 12

1.3.5 User Equipment-Based Fog 13

1.4 Communication Technologies 15

1.4.1 IEEE 802.11 15

1.4.2 4G, 5G Standards 16

1.4.3 WPAN, Short-Range Technologies 17

1.4.4 LPWAN, Other Medium- and Long-Range Technologies 18

1.5 Nonfunctional Requirements 18

1.5.1 Heterogeneity 20

1.5.2 Context-Awareness 23

1.5.3 Tenant 25

1.5.4 Provider 27

1.5.5 Security 29

1.6 Open Challenges 31

1.6.1 Challenges in Land Vehicular Fog Computing 31

1.6.2 Challenges in Marine Fog Computing 32

1.6.3 Challenges in Unmanned Aerial Vehicular Fog Computing 32

1.6.4 Challenges in User Equipment-based Fog Computing 33

1.6.5 General Challenges 33

1.7 Conclusion 35

Acknowledgment 36

References 36

2 Edge and Fog: A Survey, Use Cases, and Future Challenges 43
Cosmin Avasalcai, Ilir Murturi, and Schahram Dustdar

2.1 Introduction 43

2.2 Edge Computing 44

2.2.1 Edge Computing Architecture 46

2.3 Fog Computing 47

2.3.1 Fog Computing Architecture 49

2.4 Fog and Edge Illustrative Use Cases 50

2.4.1 Edge Computing Use Cases 50

2.4.2 Fog Computing Use Cases 54

2.5 Future Challenges 57

2.5.1 Resource Management 57

2.5.2 Security and Privacy 58

2.5.3 Network Management 61

2.6 Conclusion 61

Acknowledgment 62

References 62

3 Deep Learning in the Era of Edge Computing: Challenges and Opportunities 67
Mi Zhang, Faen Zhang, Nicholas D. Lane, Yuanchao Shu, Xiao Zeng, Biyi Fang, Shen Yan, and Hui Xu

3.1 Introduction 67

3.2 Challenges and Opportunities 68

3.2.1 Memory and Computational Expensiveness of DNN Models 68

3.2.2 Data Discrepancy in Real-world Settings 70

3.2.3 Constrained Battery Life of Edge Devices 71

3.2.4 Heterogeneity in Sensor Data 72

3.2.5 Heterogeneity in Computing Units 73

3.2.6 Multitenancy of Deep Learning Tasks 73

3.2.7 Offloading to Nearby Edges 75

3.2.8 On-device Training 76

3.3 Concluding Remarks 76

References 77

4 Caching, Security, and Mobility in Content-centric Networking 79
Osman Khalid, Imran Ali Khan, Rao Naveed Bin Rais, and Assad Abbas

4.1 Introduction 79

4.2 Caching and Fog Computing 81

4.3 Mobility Management in CCN 82

4.3.1 Classification of CCN Contents and their Mobility 83

4.3.2 User Mobility 83

4.3.3 Server-side Mobility 84

4.3.4 Direct Exchange for Location Update 84

4.3.5 Query to the Rendezvous for Location Update 84

4.3.6 Mobility with Indirection Point 84

4.3.7 Interest Forwarding 85

4.3.8 Proxy-based Mobility Management 85

4.3.9 Tunnel-based Redirection (TBR) 86

4.4 Security in Content-centric Networks 88

4.4.1 Risks Due to Caching 90

4.4.2 DOS Attack Risk 90

4.4.3 Security Model 91

4.5 Caching 91

4.5.1 Cache Allocation Approaches 91

4.5.2 Data Allocation Approaches 93

4.6 Conclusions 101

References 101

5 Security and Privacy Issues in Fog Computing 105
Ahmad Ali, Mansoor Ahmed, Muhammad Imran, and Hasan Ali Khattak

5.1 Introduction 105

5.2 Trust in IoT 107

5.3 Authentication 109

5.3.1 Related Work 109

5.4 Authorization 113

5.4.1 Related Work 114

5.5 Privacy 117

5.5.1 Requirements of Privacy in IoT 118

5.6 Web Semantics and Trust Management for Fog Computing 120

5.6.1 Trust Through Web Semantics 120

5.7 Discussion 123

5.7.1 Authentication 124

5.7.2 Authorization 125

5.8 Conclusion 130

References 130

6 How Fog Computing Can Suppor Latency/Reliability-sensitive IoT Applications: An Overview and a Taxonomy of State-of-the-art Solutions 139
Paolo Bellavista, Javier Berrocal, Antonio Corradi, Sajal K. Das, Luca Foschini, Isam Mashhour Al Jawarneh, and Alessandro Zanni

6.1 Introduction 139

6.2 Fog Computing for IoT: Definition and Requirements 142

6.2.1 Definitions 142

6.2.2 Motivations 144

6.2.3 Fog Computing Requirements When Applied to Challenging IoTs Application Domains 148

6.2.4 IoT Case Studies 152

6.3 Fog Computing: Architectural Model 154

6.3.1 Communication 154

6.3.2 Security and Privacy 156

6.3.3 Internet of Things 156

6.3.4 Data Quality 156

6.3.5 Cloudification 157

6.3.6 Analytics and Decision-Making 157

6.4 Fog Computing for IoT: A Taxonomy 158

6.4.1 Communication 159

6.4.2 Security and Privacy Layer 165

6.4.3 Internet of Things 170

6.4.4 Data Quality 173

6.4.5 Cloudification 179

6.4.6 Analytics and Decision-Making Layer 183

6.5 Comparisons of Surveyed Solutions 189

6.5.1 Communication 189

6.5.2 Security and Privacy 191

6.5.3 Internet of Things 193

6.5.4 Data Quality 194

6.5.5 Cloudification 195

6.5.6 Analytics and Decision-Making Layer 197

6.6 Challenges and Recommended Research Directions 198

6.7 Concluding Remarks 201

References 202

7 Harnessing the Computing Continuum for Programming Our World 215
Pete Beckman, Jack Dongarra, Nicola Ferrier, Geoffrey Fox, Terry Moore, Dan Reed, and Micah Beck

7.1 Introduction and Overview 215

7.2 Research Philosophy 217

7.3 A Goal-oriented Approach to Programming the Computing Continuum 219

7.3.1 A Motivating Continuum Example 219

7.3.2 Goal-oriented Annotations for Intensional Specification 221

7.3.3 A Mapping and Run-time System for the Computing Continuum 222

7.3.4 Building Blocks and Enabling Technologies 224

7.4 Summary 228

References 228

8 Fog Computing for Energy Harvesting-enabled Internet of Things 231
S. A. Tegos, P. D. Diamantoulakis, D. S. Michalopoulos, and G. K. Karagiannidis

8.1 Introduction 231

8.2 System Model 232

8.2.1 Computation Model 233

8.2.2 Energy Harvesting Model 235

8.3 Tradeoffs in EH Fog Systems 238

8.3.1 Energy Consumption vs. Latency 238

8.3.2 Execution Delay vs. Task Dropping Cost 239

8.4 Future Research Challenges 240

Acknowledgment 241

References 241

9 Optimizing Energy Efficiency of Wearable Sensors Using Fog-assisted Control 245
Delaram Amiri, Arman Anzanpour, Iman Azimi, Amir M. Rahmani, Pasi Liljeberg, Nikil Dutt, and Marco Levorato

9.1 Introduction 245

9.2 Background 247

9.3 Related Topics 249

9.4 Design Challenges 250

9.5 IoT System Architecture 251

9.5.1 Fog Computing and its Benefits 252

9.6 Fog-assisted Runtime Energy Management in Wearable Sensors 253

9.6.1 Computational Self-Awareness 255

9.6.2 Energy Optimization Algorithms 255

9.6.3 Myopic Strategy 258

9.6.4 MDP Strategy 259

9.7 Conclusions 263

Acknowledgment 264

References 264

10 Latency Minimization Through Optimal Data Placement in Fog Networks 269
Ning Wang and Jie Wu

10.1 Introduction 269

10.2 RelatedWork 272

10.2.1 Long-Term and Short-Term Placement 272

10.2.2 Data Replication 272

10.3 Problem Statement 273

10.3.1 Network Model 273

10.3.2 Multiple Data Placement with Budget Problem 274

10.3.3 Challenges 274

10.4 Delay Minimization Without Replication 275

10.4.1 Problem Formulation 275

10.4.2 Min-Cost Flow Formulation 276

10.4.3 Complexity Reduction 277

10.5 Delay Minimization with Replication 279

10.5.1 Hardness Proof 279

10.5.2 Single Request in Line Topology 279

10.5.3 Greedy Solution in Multiple Requests 280

10.5.4 Rounding Approach in Multiple Requests 282

10.6 Performance Evaluation 285

10.6.1 Trace Information 285

10.6.2 Experimental Setting 285

10.6.3 Algorithm Comparison 286

10.6.4 Experimental Results 287

10.7 Conclusion 289

Acknowledgement 289

References 290

11 Modeling and Simulation of Distributed Fog Environment Using FogNetSim++ 293
Tariq Qayyum, Asad Waqar Malik, Muazzam A. Khan, and Samee U. Khan

11.1 Introduction 293

11.2 Modeling and Simulation 294

11.3 FogNetSim++: Architecture 296

11.4 FogNetSim++: Installation and Environment Setup 298

11.4.1 OMNeT++ Installation 298

11.4.2 FogNetSim++ Installation 300

11.4.3 Sample Fog Simulation 300

11.5 Conclusion 305

References 305

Part II Fog Computing Techniques and Applications 309

12 Distributed Machine Learning for IoT Applications in the Fog 311
Aluizio F. Rocha Neto, Flavia C. Delicato, Thais V. Batista, and Paulo F. Pires

12.1 Introduction 311

12.2 Challenges in Data Processing for IoT 314

12.2.1 Big Data in IoT 315

12.2.2 Big Data Stream 318

12.2.3 Data Stream Processing 319

12.3 Computational Intelligence and Fog Computing 322

12.3.1 Machine Learning 322

12.3.2 Deep Learning 326

12.4 Challenges for Running Machine Learning on Fog Devices 328

12.4.1 Solutions Available on the Market to Deploy ML on Fog Devices 331

12.5 Approaches to Distribute Intelligence on Fog Devices 334

12.6 Final Remarks 340

Acknowledgments 341

References 341

13 Fog Computing-Based Communication Systems for Modern Smart Grids 347
Miodrag Forcan and Mirjana Maksimović

13.1 Introduction 347

13.2 An Overview of Communication Technologies in Smart Grid 349

13.3 Distribution Management System (DMS) Based on Fog/Cloud Computing 356

13.4 Real-time Simulation of the Proposed Feeder-based Communication Scheme Using MATLAB and ThingSpeak 359

13.5 Conclusion 366

References 367

14 An Estimation of Distribution Algorithm to Optimize the Utility of Task Scheduling Under Fog Computing Systems 371
Chu-ge Wu and Ling Wang

14.1 Introduction 371

14.2 Estimation of Distribution Algorithm 372

14.3 Related Work 373

14.4 Problem Statement 374

14.5 Details of Proposed Algorithm 376

14.5.1 Encoding and Decoding Method 376

14.5.2 uEDA Scheme 377

14.5.3 Local Search Method 378

14.6 Simulation 378

14.6.1 Comparison Algorithm 378

14.6.2 Simulation Environment and Experiment Settings 379

14.6.3 Compared with the Heuristic Method 381

14.7 Conclusion 383

References 383

15 Reliable and Power-Efficient Machine Learning in Wearable Sensors 385
Parastoo Alinia and Hassan Ghasemzadeh

15.1 Introduction 385

15.2 Preliminaries and Related Work 386

15.2.1 Gold Standard MET Computation 386

15.2.2 Sensor-based MET Estimation 387

15.2.3 Unreliability Mitigation 388

15.2.4 Transfer Learning 388

15.3 System Architecture and Methods 389

15.3.1 Reliable MET Calculation 390

15.3.2 The Reconfigurable MET Estimation System 392

15.4 Data Collection and Experimental Procedures 394

15.4.1 Exergaming Experiment 394

15.4.2 Treadmill Experiment 395

15.5 Results 396

15.5.1 Reliable MET Calculation 396

15.5.2 Reconfigurable Design 402

15.6 Discussion and Future Work 404

15.7 Summary 405

References 406

16 Insights into Software-Defined Networking and Applications in Fog Computing 411
Osman Khalid, Imran Ali Khan, and Assad Abbas

16.1 Introduction 411

16.2 OpenFlow Protocol 414

16.2.1 OpenFlow Switch 414

16.3 SDN-Based Research Works 416

16.4 SDN in Fog Computing 419

16.5 SDN in Wireless Mesh Networks 421

16.5.1 Challenges in Wireless Mesh Networks 421

16.5.2 SDN Technique in WMNs 421

16.5.3 Benefits of SDN in WMNs 423

16.5.4 Fault Tolerance in SDN-based WMNs 424

16.6 SDN in Wireless Sensor Networks 424

16.6.1 Challenges in Wireless Sensor Networks 424

16.6.2 SDN in Wireless Sensor Networks 425

16.6.3 Sensor Open Flow 426

16.6.4 Home Networks Using SDWN 426

16.6.5 Securing Software Defined Wireless Networks (SDWN) 426

16.7 Conclusion 427

References 427

17 Time-Critical Fog Computing for Vehicular Networks 431
Ahmed Chebaane, Abdelmajid Khelil, and Neeraj Suri

17.1 Introduction 431

17.2 Applications and Timeliness Guarantees and Perturbations 434

17.2.1 Application Scenarios 434

17.2.2 Application Model 436

17.2.3 Timeliness Guarantees 436

17.2.4 Benchmarking Vehicular Applications Concerning Timeliness Guarantees 437

17.2.5 Building Blocks to Reach Timeliness Guarantees 440

17.2.6 Timeliness Perturbations 441

17.3 Coping with Perturbation to Meet Timeliness Guarantees 443

17.3.1 Coping with Constraints 443

17.3.2 Coping with Failures 448

17.3.3 Coping with Threats 448

17.4 Research Gaps and Future Research Directions 449

17.4.1 Mobile Fog Computing 449

17.4.2 Fog Service Level Agreement (SLA) 450

17.5 Conclusion 451

References 451

18 A Reliable and Efficient Fog-Based Architecture for Autonomous Vehicular Networks 459
Shuja Mughal, Kamran Sattar Awaisi, Assad Abbas, Inayat ur Rehman, Muhammad Usman Shahid Khan, and Mazhar Ali

18.1 Introduction 459

18.2 Proposed Methodology 461

18.3 Hypothesis Formulation 463

18.4 Simulation Design 464

18.4.1 Results and Discussions 464

18.4.2 Hypothesis Testing 467

18.5 Conclusions 469

References 470

19 Fog Computing to Enable Geospatial Video Analytics for Disaster-incident Situational Awareness 473
Dmitrii Chemodanov, Prasad Calyam, and Kannappan Palaniappan

19.1 Introduction 473

19.1.1 How Can Geospatial Video Analytics Help with Disaster-Incident Situational Awareness? 473

19.1.2 Fog Computing for Geospatial Video Analytics 474

19.1.3 Function-Centric Cloud/Fog Computing Paradigm 475

19.1.4 Function-Centric Fog/Cloud Computing Challenges 476

19.1.5 Chapter Organization 477

19.2 Computer Vision Application Case Studies and FCC Motivation 478

19.2.1 Patient Tracking with Face Recognition Case Study 478

19.2.2 3-D Scene Reconstruction from LIDAR Scans 480

19.2.3 Tracking Objects of Interest in WAMI 482

19.3 Geospatial Video Analytics Data Collection Using Edge Routing 484

19.3.1 Network Edge Geographic Routing Challenges 484

19.3.2 Artificial Intelligence Relevance in Geographic Routing 486

19.3.3 AI-Augmented Geographic Routing Implementation 487

19.4 Fog/Cloud Data Processing for Geospatial Video Analytics Consumption 490

19.4.1 Geo-Distributed Latency-Sensitive SFC Challenges 491

19.4.2 Metapath-Based Composite Variable Approach 492

19.4.3 Metapath-Based SFC Orchestration Implementation 495

19.5 Concluding Remarks 496

19.5.1 What Have We Learned? 496

19.5.2 The Road Ahead and Open Problems 497

References 498

20 An Insight into 5G Networks with Fog Computing 505
Osman Khalid, Imran Ali Khan, Rao Naveed Bin Rais, and Asad Waqar Malik

20.1 Introduction 505

20.2 Vision of 5G 507

20.3 Fog Computing with 5G Networks 508

20.3.1 Fog Computing 508

20.3.2 The Need of Fog Computing in 5G Networks 508

20.4 Architecture of 5G 508

20.4.1 Cellular Architecture 508

20.4.2 Energy Efficiency 510

20.4.3 Two-Tier Architecture 512

20.4.4 Cognitive Radio 512

20.4.5 Cloud-Based Architecture 513

20.5 Technology and Methodology for 5G 514

20.5.1 HetNet 515

20.5.2 Beam Division Multiple Access (BDMA) 516

20.5.3 Mixed Bandwidth Data Path 516

20.5.4 Wireless Virtualization 516

20.5.5 Flexible Duplex 518

20.5.6 Multiple-Input Multiple-Output (MIMO) 518

20.5.7 M2M 519

20.5.8 Multibeam-Based Communication System 520

20.5.9 Software-Defined Networking (SDN) 520

20.6 Applications 521

20.6.1 Health Care 521

20.6.2 Smart Grid 521

20.6.3 Logistic and Tracking 521

20.6.4 Personal Usage 521

20.6.5 Virtualized Home 522

20.7 Challenges 522

20.8 Conclusion 524

References 524

21 Fog Computing for Bioinformatics Applications 529
Hafeez Ur Rehman, Asad Khan, and Usman Habib

21.1 Introduction 529

21.2 Cloud Computing 531

21.2.1 Service Models 532

21.2.2 Delivery Models 532

21.3 Cloud Computing Applications in Bioinformatics 533

21.3.1 Bioinformatics Tools Deployed as SaaS 533

21.3.2 Bioinformatics Platforms Deployed as PaaS 535

21.3.3 Bioinformatics Tools Deployed as IaaS 535

21.4 Fog Computing 537

21.5 Fog Computing for Bioinformatics Applications 539

21.5.1 Real-Time Microorganism Detection System 541

21.6 Conclusion 543

References 543

Index 547

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Assad Abbas
Samee U. Khan
Albert Y. Zomaya University of Western Australia.
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