Fog Computing: Theory and Practice Theory and Practice
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, industri...
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| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
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Hoboken, NJ
Wiley
2020
John Wiley & Sons, Incorporated Wiley-Blackwell John Wiley & Sons (US) |
| Vydání: | 1 |
| Edice: | Wiley Series on Parallel and Distributed Computing |
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| ISBN: | 1119551692, 9781119551690 |
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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 3
- 6.4.3.2 Actuators -- 6.4.4 Data Quality -- 6.4.4.1 Data Normalization -- 6.4.4.2 Data Filtering -- 6.4.4.3 Data Aggregation -- 6.4.5 Cloudification -- 6.4.5.1 Virtualization -- 6.4.5.2 Storage -- 6.4.6 Analytics and Decision‐Making Layer -- 6.4.6.1 Data Analytics -- 6.4.6.2 Decision‐Making -- 6.5 Comparisons of Surveyed Solutions -- 6.5.1 Communication -- 6.5.1.1 Standardization -- 6.5.1.2 Reliability -- 6.5.1.3 Low‐latency Communication -- 6.5.1.4 Mobility -- 6.5.2 Security and Privacy -- 6.5.2.1 Security -- 6.5.2.2 Privacy -- 6.5.2.3 Safety -- 6.5.3 Internet of Things -- 6.5.3.1 Sensors -- 6.5.3.2 Actuators -- 6.5.4 Data Quality -- 6.5.4.1 Data Normalization -- 6.5.4.2 Data Filtering -- 6.5.4.3 Data Aggregation -- 6.5.5 Cloudification -- 6.5.5.1 Virtualization -- 6.5.5.2 Storage -- 6.5.6 Analytics and Decision‐Making Layer -- 6.5.6.1 Data Analytics -- 6.5.6.2 Decision‐Making -- 6.6 Challenges and Recommended Research Directions -- 6.7 Concluding Remarks -- References -- Chapter 7 Harnessing the Computing Continuum for Programming Our World -- 7.1 Introduction and Overview -- 7.2 Research Philosophy -- 7.3 A Goal‐oriented Approach to Programming the Computing Continuum -- 7.3.1 A Motivating Continuum Example -- 7.3.2 Goal‐oriented Annotations for Intensional Specification -- 7.3.3 A Mapping and Run‐time System for the Computing Continuum -- 7.3.4 Building Blocks and Enabling Technologies -- 7.3.4.1 The Array of Things (AoT) -- 7.3.4.2 Iowa Quantified (IQ) -- 7.3.4.3 Intelligent, Multiversion Libraries -- 7.3.4.4 Data Flow Execution for Big Data -- 7.4 Summary -- References -- Chapter 8 Fog Computing for Energy Harvesting‐enabled Internet of Things -- 8.1 Introduction -- 8.2 System Model -- 8.2.1 Computation Model -- 8.2.1.1 Local Execution Model -- 8.2.1.2 Fog Execution Model -- 8.2.2 Energy Harvesting Model -- 8.2.2.1 Stochastic Process
- Cover -- Title Page -- Copyright -- Contents -- List of Contributors -- Acronyms -- Part I Fog Computing Systems and Architectures -- Chapter 1 Mobile Fog Computing -- 1.1 Introduction -- 1.2 Mobile Fog Computing and Related Models -- 1.3 The Needs of Mobile Fog Computing -- 1.3.1 Infrastructural Mobile Fog Computing -- 1.3.1.1 Road Crash Avoidance -- 1.3.1.2 Marine Data Acquisition -- 1.3.1.3 Forest Fire Detection -- 1.3.1.4 Mobile Ambient Assisted Living -- 1.3.2 Land Vehicular Fog -- 1.3.3 Marine Fog -- 1.3.4 Unmanned Aerial Vehicular Fog -- 1.3.5 User Equipment‐Based Fog -- 1.3.5.1 Healthcare -- 1.3.5.2 Content Delivery -- 1.3.5.3 Crowd Sensing -- 1.4 Communication Technologies -- 1.4.1 IEEE 802.11 -- 1.4.2 4G, 5G Standards -- 1.4.3 WPAN, Short‐Range Technologies -- 1.4.4 LPWAN, Other Medium‐ and Long‐Range Technologies -- 1.5 Nonfunctional Requirements -- 1.5.1 Heterogeneity -- 1.5.1.1 Server Heterogeneity -- 1.5.1.2 End‐Device Heterogeneity -- 1.5.1.3 End‐to‐End Network Heterogeneity -- 1.5.2 Context‐Awareness -- 1.5.2.1 Server Context -- 1.5.2.2 Mobility Context -- 1.5.2.3 End‐to‐end Context -- 1.5.2.4 Application Context -- 1.5.3 Tenant -- 1.5.3.1 Application Management -- 1.5.3.2 Cost of Energy and Tenancy -- 1.5.4 Provider -- 1.5.4.1 Physical Placement -- 1.5.4.2 Server Discoverability and Connectivity -- 1.5.4.3 Operation Management -- 1.5.4.4 Operation Cost -- 1.5.5 Security -- 1.5.5.1 Physical Security -- 1.5.5.2 End‐to‐End Security -- 1.5.5.3 Security Monitoring and Management -- 1.5.5.4 Trust Management and Multitenancy Security -- 1.6 Open Challenges -- 1.6.1 Challenges in Land Vehicular Fog Computing -- 1.6.2 Challenges in Marine Fog Computing -- 1.6.3 Challenges in Unmanned Aerial Vehicular Fog Computing -- 1.6.4 Challenges in User Equipment‐based Fog Computing -- 1.6.5 General Challenges -- 1.6.5.1 Testbed Tool
- 1.6.5.2 Autonomous Runtime Adjustment and Rapid Redeployment -- 1.6.5.3 Scheduling of Fog Applications -- 1.6.5.4 Scalable Resource Management of Fog Providers -- 1.7 Conclusion -- Acknowledgment -- References -- Chapter 2 Edge and Fog: A Survey, Use Cases, and Future Challenges -- 2.1 Introduction -- 2.2 Edge Computing -- 2.2.1 Edge Computing Architecture -- 2.3 Fog Computing -- 2.3.1 Fog Computing Architecture -- 2.4 Fog and Edge Illustrative Use Cases -- 2.4.1 Edge Computing Use Cases -- 2.4.1.1 A Wearable ECG Sensor -- 2.4.1.2 Smart Home -- 2.4.2 Fog Computing Use Cases -- 2.4.2.1 Smart Traffic Light System -- 2.4.2.2 Smart Pipeline Monitoring System -- 2.5 Future Challenges -- 2.5.1 Resource Management -- 2.5.2 Security and Privacy -- 2.5.3 Network Management -- 2.6 Conclusion -- Acknowledgment -- References -- Chapter 3 Deep Learning in the Era of Edge Computing: Challenges and Opportunities -- 3.1 Introduction -- 3.2 Challenges and Opportunities -- 3.2.1 Memory and Computational Expensiveness of DNN Models -- 3.2.2 Data Discrepancy in Real‐world Settings -- 3.2.3 Constrained Battery Life of Edge Devices -- 3.2.4 Heterogeneity in Sensor Data -- 3.2.5 Heterogeneity in Computing Units -- 3.2.6 Multitenancy of Deep Learning Tasks -- 3.2.7 Offloading to Nearby Edges -- 3.2.8 On‐device Training -- 3.3 Concluding Remarks -- References -- Chapter 4 Caching, Security, and Mobility in Content‐centric Networking -- 4.1 Introduction -- 4.2 Caching and Fog Computing -- 4.3 Mobility Management in CCN -- 4.3.1 Classification of CCN Contents and their Mobility -- 4.3.2 User Mobility -- 4.3.3 Server‐side Mobility -- 4.3.4 Direct Exchange for Location Update -- 4.3.5 Query to the Rendezvous for Location Update -- 4.3.6 Mobility with Indirection Point -- 4.3.7 Interest Forwarding -- 4.3.8 Proxy‐based Mobility Management -- 4.3.9 Tunnel‐based Redirection (TBR)
- 8.2.2.2 Wireless Power Transfer -- 8.3 Tradeoffs in EH Fog Systems -- 8.3.1 Energy Consumption vs. Latency -- 8.3.2 Execution Delay vs. Task Dropping Cost -- 8.4 Future Research Challenges -- Acknowledgment -- References -- Chapter 9 Optimizing Energy Efficiency of Wearable Sensors Using Fog‐assisted Control -- 9.1 Introduction -- 9.2 Background -- 9.3 Related Topics -- 9.4 Design Challenges -- 9.5 IoT System Architecture -- 9.5.1 Fog Computing and its Benefits -- 9.6 Fog‐assisted Runtime Energy Management in Wearable Sensors -- 9.6.1 Computational Self‐Awareness -- 9.6.2 Energy Optimization Algorithms -- 9.6.3 Myopic Strategy -- 9.6.4 MDP Strategy -- 9.7 Conclusions -- Acknowledgment -- References -- Chapter 10 Latency Minimization Through Optimal Data Placement in Fog Networks -- 10.1 Introduction -- 10.2 Related Work -- 10.2.1 Long‐Term and Short‐Term Placement -- 10.2.2 Data Replication -- 10.3 Problem Statement -- 10.3.1 Network Model -- 10.3.2 Multiple Data Placement with Budget Problem -- 10.3.3 Challenges -- 10.4 Delay Minimization Without Replication -- 10.4.1 Problem Formulation -- 10.4.2 Min‐Cost Flow Formulation -- 10.4.3 Complexity Reduction -- 10.5 Delay Minimization with Replication -- 10.5.1 Hardness Proof -- 10.5.2 Single Request in Line Topology -- 10.5.3 Greedy Solution in Multiple Requests -- 10.5.4 Rounding Approach in Multiple Requests -- 10.5.4.1 Generating Linear Programming Solution -- 10.5.4.2 Creating Centers -- 10.5.4.3 Converting to Integral Solution -- 10.6 Performance Evaluation -- 10.6.1 Trace Information -- 10.6.2 Experimental Setting -- 10.6.3 Algorithm Comparison -- 10.6.4 Experimental Results -- 10.6.4.1 Trace Analysis -- 10.6.4.2 Results Without Data Replication -- 10.6.4.3 Results with Data Replication -- 10.6.4.4 Summary -- 10.7 Conclusion -- Acknowledgement -- References
- Chapter 11 Modeling and Simulation of Distributed Fog Environment Using FogNetSim
- 4.4 Security in Content‐centric Networks -- 4.4.1 Risks Due to Caching -- 4.4.2 DOS Attack Risk -- 4.4.3 Security Model -- 4.5 Caching -- 4.5.1 Cache Allocation Approaches -- 4.5.2 Data Allocation Approaches -- 4.6 Conclusions -- References -- Chapter 5 Security and Privacy Issues in Fog Computing -- 5.1 Introduction -- 5.2 Trust in IoT -- 5.3 Authentication -- 5.3.1 Related Work -- 5.4 Authorization -- 5.4.1 Related Work -- 5.5 Privacy -- 5.5.1 Requirements of Privacy in IoT -- 5.5.1.1 Device Privacy -- 5.5.1.2 Communication Privacy -- 5.5.1.3 Storage Privacy -- 5.5.1.4 Processing Privacy -- 5.6 Web Semantics and Trust Management for Fog Computing -- 5.6.1 Trust Through Web Semantics -- 5.7 Discussion -- 5.7.1 Authentication -- 5.7.2 Authorization -- 5.8 Conclusion -- References -- Chapter 6 How Fog Computing Can Support Latency/Reliability‐sensitive IoT Applications: An Overview and a Taxonomy of State‐of‐the‐art Solutions -- 6.1 Introduction -- 6.2 Fog Computing for IoT: Definition and Requirements -- 6.2.1 Definitions -- 6.2.2 Motivations -- 6.2.3 Fog Computing Requirements When Applied to Challenging IoTs Application Domains -- 6.2.3.1 Scalability -- 6.2.3.2 Interoperability -- 6.2.3.3 Real‐Time Responsiveness -- 6.2.3.4 Data Quality -- 6.2.3.5 Security and Privacy -- 6.2.3.6 Location‐Awareness -- 6.2.3.7 Mobility -- 6.2.4 IoT Case Studies -- 6.3 Fog Computing: Architectural Model -- 6.3.1 Communication -- 6.3.2 Security and Privacy -- 6.3.3 Internet of Things -- 6.3.4 Data Quality -- 6.3.5 Cloudification -- 6.3.6 Analytics and Decision‐Making -- 6.4 Fog Computing for IoT: A Taxonomy -- 6.4.1 Communication -- 6.4.1.1 Standardization -- 6.4.1.2 Reliability -- 6.4.1.3 Low‐Latency -- 6.4.1.4 Mobility -- 6.4.2 Security and Privacy Layer -- 6.4.2.1 Safety -- 6.4.2.2 Security -- 6.4.2.3 Privacy -- 6.4.3 Internet of Things -- 6.4.3.1 Sensors