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 MaksimoviÄ
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
