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Fog for 5G and IoT / edited by Mung Chiang, Bharath Balasubramanian, Flavio Bonomi.

Contributor(s): Chiang, Mung [editor.] | Balasubramanian, Bharath [editor.] | Bonomi, Flavio [editor.] | IEEE Xplore (Online Service) [distributor.] | Wiley [publisher.].
Material type: materialTypeLabelBookSeries: Information and communication technology series: Publisher: Hoboken, New Jersey, USA : John Wiley & Sons Inc., 2017Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2017]Description: 1 PDF (304 pages).Content type: text Media type: electronic Carrier type: online resourceISBN: 9781119187202.Subject(s): Electronic data processing -- Distributed processing | Distributed shared memory | Storage area networks (Computer networks) | Mobile computing | Internet of things | Cloud computingGenre/Form: Electronic books.Additional physical formats: Print version:: No titleDDC classification: 004.67/82 Online resources: Abstract with links to resource Also available in print.
Contents:
-- CONTRIBUTORS xi / /Introduction 1 /Bharath Balasubramanian, Mung Chiang, and Flavio Bonomi / /I.1 Summary of Chapters 5 / /I.2 Acknowledgments 7 / /References 8 / /I COMMUNICATION AND MANAGEMENT OF FOG 11 / /1 ParaDrop: An Edge Computing Platform in Home Gateways 13 /Suman Banerjee, Peng Liu, Ashish Patro, and Dale Willis / /1.1 Introduction 13 / /1.1.1 Enabling Multitenant Wireless Gateways and Applications through ParaDrop 14 / /1.1.2 ParaDrop Capabilities 15 / /1.2 Implementing Services for the ParaDrop Platform 17 / /1.3 Develop Services for ParaDrop 19 / /1.3.1 A Security Camera Service Using ParaDrop 19 / /1.3.2 An Environmental Sensor Service Using ParaDrop 22 / /References 23 / /2 Mind Your Own Bandwidth 24 /Carlee Joe-Wong, Sangtae Ha, Zhenming Liu, Felix Ming Fai Wong, and Mung Chiang / /2.1 Introduction 24 / /2.1.1 Leveraging the Fog 25 / /2.1.2 A Home Solution to a Home Problem 25 / /2.2 Related Work 28 / /2.3 Credit Distribution and Optimal Spending 28 / /2.3.1 Credit Distribution 29 / /2.3.2 Optimal Credit Spending 31 / /2.4 An Online Bandwidth Allocation Algorithm 32 / /2.4.1 Estimating Other Gateways' Spending 32 / /2.4.2 Online Spending Decisions and App Prioritization 34 / /2.5 Design and Implementation 35 / /2.5.1 Traffic and Device Classification 37 / /2.5.2 Rate Limiting Engine 37 / /2.5.3 Traffic Prioritization Engine 38 / /2.6 Experimental Results 39 / /2.6.1 Rate Limiting 39 / /2.6.2 Traffic Prioritization 41 / /2.7 Gateway Sharing Results 41 / /2.8 Concluding Remarks 45 / /Acknowledgments 46 / /Appendix 2.A 46 / /2.A.1 Proof of Lemma 2.1 46 / /2.A.2 Proof of Lemma 2.2 46 / /2.A.3 Proof of Proposition 2.1 47 / /2.A.4 Proof of Proposition 2.2 48 / /2.A.5 Proof of Proposition 2.3 49 / /2.A.6 Proof of Proposition 2.4 49 / /References 50 / /3 Socially-Aware Cooperative D2D and D4D Communications toward Fog Networking 52 /Xu Chen, Junshan Zhang, and Satyajayant Misra / /3.1 Introduction 52 / /3.1.1 From Social Trust and Social Reciprocity to D2D Cooperation 54 / /3.1.2 Smart Grid: An IoT Case for Socially-Aware Cooperative D2D and D4D Communications 55 / /3.1.3 Summary of Main Results 57 / /3.2 Related Work 58 / /3.3 System Model 59 / /3.3.1 Physical (Communication) Graph Model 60 / /3.3.2 Social Graph Model 61 / /3.4 Socially-Aware Cooperative D2D and D4D Communications toward Fog Networking 62 / /3.4.1 Social Trust-Based Relay Selection 63 / /3.4.2 Social Reciprocity-Based Relay Selection 63 / /3.4.3 Social Trust and Social Reciprocity-Based Relay Selection 68 / /3.5 Network Assisted Relay Selection Mechanism 69 / /3.5.1 Reciprocal Relay Selection Cycle Finding 69 / /3.5.2 NARS Mechanism 70 / /3.5.3 Properties of NARS Mechanism 73 / /3.6 Simulations 75 / /3.6.1 Erdos / Renyi Social Graph 76 / /3.6.2 Real Trace Based Social Graph 78 / /3.7 Conclusion 82 / /Acknowledgments 82 / /References 83 / /4 You Deserve Better Properties (From Your Smart Devices) 86 /Steven Y. Ko / /4.1 Why We Need to Provide Better Properties 86 / /4.2 Where We Need to Provide Better Properties 87 / /4.3 What Properties We Need to Provide and How 88 / /4.3.1 Transparency 88 / /4.3.2 Predictable Performance 93 / /4.3.3 Openness 99 / /4.4 Conclusions 102 / /Acknowledgment 102 / /References 103 / /II STORAGE AND COMPUTATION IN FOG 107 / /5 Distributed Caching for Enhancing Communications Efficiency 109 /A. Salman Avestimehr and Andreas F. Molisch / /5.1 Introduction 109 / /5.2 Femtocaching 111 / /5.2.1 System Model 111 / /5.2.2 Adaptive Streaming from Helper Stations 114 / /5.3 User-Caching 115 / /5.3.1 Cluster-Based Caching and D2D Communications 115 / /5.3.2 IT LinQ-Based Caching and Communications 118 / /5.3.3 Coded Multicast 126 / /5.4 Conclusions and Outlook 130 / /References 131 / /6 Wireless Video Fog: Collaborative Live Streaming with Error Recovery 133 /Bo Zhang, Zhi Liu, and S.-H. Gary Chan / /6.1 Introduction 133 / /6.2 Related Work 136 / /6.3 System Operation and Network Model 138 / /6.4 Problem Formulation and Complexity 140 / /6.4.1 NC Packet Selection Optimization 140 / /6.4.2 Broadcaster Selection Optimization 143 / /6.4.3 Complexity Analysis 144 / /6.5 VBCR: A Distributed Heuristic for Live Video with Cooperative Recovery 144 / /6.5.1 Initial Information Exchange 145 / /6.5.2 Cooperative Recovery 145 / /6.5.3 Updated Information Exchange 147 / /6.5.4 Video Packet Forwarding 147 / /6.6 Illustrative Simulation Results 150 / /6.7 Concluding Remarks 156 / /References 156 / /7 Elastic Mobile Device Clouds: Leveraging Mobile Devices to Provide Cloud Computing Services at the Edge 159 /Karim Habak, Cong Shi, Ellen W. Zegura, Khaled A. Harras, and Mostafa Ammar / /7.1 Introduction 159 / /7.2 Design Space with Examples 161 / /7.2.1 Mont-Blanc 162 / /7.2.2 Computing while Charging 163 / /7.2.3 FemtoCloud 164 / /7.2.4 Serendipity 166 / /7.3 FemtoCloud Performance Evaluation 168 / /7.3.1 Experimental Setup 168 / /7.3.2 FemtoCloud Simulation Results 169 / /7.3.3 FemtoCloud Prototype Evaluation 173 / /7.4 Serendipity Performance Evaluation 175 / /7.4.1 Experimental Setup 175 / /7.4.2 Serendipity's Performance Benefits 176 / /7.4.3 Impact of Network Environment 179 / /7.4.4 The Impact of the Job Properties 182 / /7.5 Challenges 186 / /References 186 / /III APPLICATIONS OF FOG 189 / /8 The Role of Fog Computing in the Future of the Automobile 191 /Flavio Bonomi, Stefan Poledna, and Wilfried Steiner / /8.1 Introduction 191 / /8.2 Current Automobile Electronic Architectures 193 / /8.3 Future Challenges of Automotive E/E Architectures and Solution Strategies 195 / /8.4 Future Automobiles as Fog Nodes on Wheels 200 / /8.5 Deterministic FOG Nodes on Wheels Through Real-Time Computing and Time-Triggered Technologies /203 / /8.5.1 Deterministic Fog Node Addressing the Scalability Challenge through Virtualization 203 / /8.5.2 Deterministic Fog Node Addressing the Connectivity and Security Challenges 204 / /8.5.3 Emerging Use Case of Deterministic Fog Nodes in Automotive Applications - Vehicle-Wide /Virtualization 206 / /8.6 Conclusion 209 / /References 209 / /9 Geographic Addressing for Field Networks 211 /Robert J. Hall / /9.1 Introduction 211 / /9.1.1 Field Networking 211 / /9.1.2 Challenges of Field Networking 212 / /9.2 Geographic Addressing 214 / /9.3 SAGP: Wireless GA in the Field 215 / /9.3.1 SAGP Processing 216 / /9.3.2 SAGP Retransmission Heuristics 217 / /9.3.3 Example of SAGP Packet Propagation 218 / /9.3.4 Followcast: Efficient SAGP Streaming 219 / /9.3.5 Meeting the Challenges 220 / /9.4 Georouting: Extending GA to the Cloud 221 / /9.5 SGAF: A Multi-Tiered Architecture for Large-Scale GA 222 / /9.5.1 Bridging Between Tiers 223 / /9.5.2 Hybrid Security Architecture 225 / /9.6 The AT&T Labs Geocast System 225 / /9.7 Two GA Applications 226 / /9.7.1 PSCommander 226 / /9.7.2 Geocast Games 230 / /9.8 Conclusions 232 / /References 232 / /10 Distributed Online Learning and Stream Processing for a Smarter Planet 234 /Deepak S.
Turaga and Mihaela van der Schaar / /10.1 Introduction: Smarter Planet 234 / /10.2 Illustrative Problem: Transportation 237 / /10.3 Stream Processing Characteristics 238 / /10.4 Distributed Stream Processing Systems 239 / /10.4.1 State of the Art 239 / /10.4.2 Stream Processing Systems 240 / /10.5 Distributed Online Learning Frameworks 244 / /10.5.1 State of the Art 244 / /10.5.2 Systematic Framework for Online Distributed Ensemble Learning 247 / /10.5.3 Online Learning of the Aggregation Weights 250 / /10.5.4 Collision Detection Application 254 / /10.6 What Lies Ahead 257 / /Acknowledgment 258 / /References 258 / /11 Securing the Internet of Things: Need for a New Paradigm and Fog Computing 261 /Tao Zhang, Yi Zheng, Raymond Zheng, and Helder Antunes / /11.1 Introduction 261 / /11.2 New IoT Security Challenges That Necessitate Fundamental Changes to the Existing Security /Paradigm 263 / /11.2.1 Many Things Will Have Long Life Spans but Constrained and Difficult-to-Upgrade Resources 264 / /11.2.2 Putting All IoT Devices Inside Firewalled Castles Will Become Infeasible or Impractical 264 / /11.2.3 Mission-Critical Systems Will Demand Minimal-Impact Incident Responses 265 / /11.2.4 The Need to Know the Security Status of a Vast Number of Devices 266 / /11.3 A New Security Paradigm for the Internet of Things 268 / /11.3.1 Help the Less Capable with Fog Computing 269 / /11.3.2 Scale Security Monitoring to Large Number of Devices with Crowd Attestation 272 / /11.3.3 Dynamic Risk / Benefit-Proportional Protection with Adaptive Immune Security 277 / /11.4 Summary 281 / /Acknowledgment 281 / /References 281 / /INDEX 285.
Summary: Fog is starting to shape the future of the balance of power in information technology The book examines how fog will change the information technology industry in the next decade. Along the cloud-to-things continuum, fog distributes the services of computation, communication, control, and storage closer to the edge, access, and users. As a computing and networking architecture, fog enables key applications in wireless 5G, the Internet of things (IoT), and big data. The authors cover the fundamental trade-offs to major applications of fog. The book chapters are designed to motivate a transition from the current cloud architectures to the fog (Chapter 1) and the necessary architectural components to support such a transition (Chapters 2 / 6). The rest of the chapters (Chapters 7 / 11) are dedicated to reviewing various 5G and IoT applications that will benefit from fog networking. This volume is edited by pioneers in fog and includes contributions by active researchers in the field. . Covers fog technologies and describes the interaction between fog and cloud. Presents a view of fog and IoT that combines the aspects of both industry and academia. Discusses the various architectural and design challenges in coordinating the interactions between M2M, D2D, and fog technologies "Fog for 5G and IoT" serves as an introduction to the evolving fog architecture, compiling work from different areas that collectively form this paradigm.
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Includes bibliographical references and index.

-- CONTRIBUTORS xi / /Introduction 1 /Bharath Balasubramanian, Mung Chiang, and Flavio Bonomi / /I.1 Summary of Chapters 5 / /I.2 Acknowledgments 7 / /References 8 / /I COMMUNICATION AND MANAGEMENT OF FOG 11 / /1 ParaDrop: An Edge Computing Platform in Home Gateways 13 /Suman Banerjee, Peng Liu, Ashish Patro, and Dale Willis / /1.1 Introduction 13 / /1.1.1 Enabling Multitenant Wireless Gateways and Applications through ParaDrop 14 / /1.1.2 ParaDrop Capabilities 15 / /1.2 Implementing Services for the ParaDrop Platform 17 / /1.3 Develop Services for ParaDrop 19 / /1.3.1 A Security Camera Service Using ParaDrop 19 / /1.3.2 An Environmental Sensor Service Using ParaDrop 22 / /References 23 / /2 Mind Your Own Bandwidth 24 /Carlee Joe-Wong, Sangtae Ha, Zhenming Liu, Felix Ming Fai Wong, and Mung Chiang / /2.1 Introduction 24 / /2.1.1 Leveraging the Fog 25 / /2.1.2 A Home Solution to a Home Problem 25 / /2.2 Related Work 28 / /2.3 Credit Distribution and Optimal Spending 28 / /2.3.1 Credit Distribution 29 / /2.3.2 Optimal Credit Spending 31 / /2.4 An Online Bandwidth Allocation Algorithm 32 / /2.4.1 Estimating Other Gateways' Spending 32 / /2.4.2 Online Spending Decisions and App Prioritization 34 / /2.5 Design and Implementation 35 / /2.5.1 Traffic and Device Classification 37 / /2.5.2 Rate Limiting Engine 37 / /2.5.3 Traffic Prioritization Engine 38 / /2.6 Experimental Results 39 / /2.6.1 Rate Limiting 39 / /2.6.2 Traffic Prioritization 41 / /2.7 Gateway Sharing Results 41 / /2.8 Concluding Remarks 45 / /Acknowledgments 46 / /Appendix 2.A 46 / /2.A.1 Proof of Lemma 2.1 46 / /2.A.2 Proof of Lemma 2.2 46 / /2.A.3 Proof of Proposition 2.1 47 / /2.A.4 Proof of Proposition 2.2 48 / /2.A.5 Proof of Proposition 2.3 49 / /2.A.6 Proof of Proposition 2.4 49 / /References 50 / /3 Socially-Aware Cooperative D2D and D4D Communications toward Fog Networking 52 /Xu Chen, Junshan Zhang, and Satyajayant Misra / /3.1 Introduction 52 / /3.1.1 From Social Trust and Social Reciprocity to D2D Cooperation 54 / /3.1.2 Smart Grid: An IoT Case for Socially-Aware Cooperative D2D and D4D Communications 55 / /3.1.3 Summary of Main Results 57 / /3.2 Related Work 58 / /3.3 System Model 59 / /3.3.1 Physical (Communication) Graph Model 60 / /3.3.2 Social Graph Model 61 / /3.4 Socially-Aware Cooperative D2D and D4D Communications toward Fog Networking 62 / /3.4.1 Social Trust-Based Relay Selection 63 / /3.4.2 Social Reciprocity-Based Relay Selection 63 / /3.4.3 Social Trust and Social Reciprocity-Based Relay Selection 68 / /3.5 Network Assisted Relay Selection Mechanism 69 / /3.5.1 Reciprocal Relay Selection Cycle Finding 69 / /3.5.2 NARS Mechanism 70 / /3.5.3 Properties of NARS Mechanism 73 / /3.6 Simulations 75 / /3.6.1 Erdos / Renyi Social Graph 76 / /3.6.2 Real Trace Based Social Graph 78 / /3.7 Conclusion 82 / /Acknowledgments 82 / /References 83 / /4 You Deserve Better Properties (From Your Smart Devices) 86 /Steven Y. Ko / /4.1 Why We Need to Provide Better Properties 86 / /4.2 Where We Need to Provide Better Properties 87 / /4.3 What Properties We Need to Provide and How 88 / /4.3.1 Transparency 88 / /4.3.2 Predictable Performance 93 / /4.3.3 Openness 99 / /4.4 Conclusions 102 / /Acknowledgment 102 / /References 103 / /II STORAGE AND COMPUTATION IN FOG 107 / /5 Distributed Caching for Enhancing Communications Efficiency 109 /A. Salman Avestimehr and Andreas F. Molisch / /5.1 Introduction 109 / /5.2 Femtocaching 111 / /5.2.1 System Model 111 / /5.2.2 Adaptive Streaming from Helper Stations 114 / /5.3 User-Caching 115 / /5.3.1 Cluster-Based Caching and D2D Communications 115 / /5.3.2 IT LinQ-Based Caching and Communications 118 / /5.3.3 Coded Multicast 126 / /5.4 Conclusions and Outlook 130 / /References 131 / /6 Wireless Video Fog: Collaborative Live Streaming with Error Recovery 133 /Bo Zhang, Zhi Liu, and S.-H. Gary Chan / /6.1 Introduction 133 / /6.2 Related Work 136 / /6.3 System Operation and Network Model 138 / /6.4 Problem Formulation and Complexity 140 / /6.4.1 NC Packet Selection Optimization 140 / /6.4.2 Broadcaster Selection Optimization 143 / /6.4.3 Complexity Analysis 144 / /6.5 VBCR: A Distributed Heuristic for Live Video with Cooperative Recovery 144 / /6.5.1 Initial Information Exchange 145 / /6.5.2 Cooperative Recovery 145 / /6.5.3 Updated Information Exchange 147 / /6.5.4 Video Packet Forwarding 147 / /6.6 Illustrative Simulation Results 150 / /6.7 Concluding Remarks 156 / /References 156 / /7 Elastic Mobile Device Clouds: Leveraging Mobile Devices to Provide Cloud Computing Services at the Edge 159 /Karim Habak, Cong Shi, Ellen W. Zegura, Khaled A. Harras, and Mostafa Ammar / /7.1 Introduction 159 / /7.2 Design Space with Examples 161 / /7.2.1 Mont-Blanc 162 / /7.2.2 Computing while Charging 163 / /7.2.3 FemtoCloud 164 / /7.2.4 Serendipity 166 / /7.3 FemtoCloud Performance Evaluation 168 / /7.3.1 Experimental Setup 168 / /7.3.2 FemtoCloud Simulation Results 169 / /7.3.3 FemtoCloud Prototype Evaluation 173 / /7.4 Serendipity Performance Evaluation 175 / /7.4.1 Experimental Setup 175 / /7.4.2 Serendipity's Performance Benefits 176 / /7.4.3 Impact of Network Environment 179 / /7.4.4 The Impact of the Job Properties 182 / /7.5 Challenges 186 / /References 186 / /III APPLICATIONS OF FOG 189 / /8 The Role of Fog Computing in the Future of the Automobile 191 /Flavio Bonomi, Stefan Poledna, and Wilfried Steiner / /8.1 Introduction 191 / /8.2 Current Automobile Electronic Architectures 193 / /8.3 Future Challenges of Automotive E/E Architectures and Solution Strategies 195 / /8.4 Future Automobiles as Fog Nodes on Wheels 200 / /8.5 Deterministic FOG Nodes on Wheels Through Real-Time Computing and Time-Triggered Technologies /203 / /8.5.1 Deterministic Fog Node Addressing the Scalability Challenge through Virtualization 203 / /8.5.2 Deterministic Fog Node Addressing the Connectivity and Security Challenges 204 / /8.5.3 Emerging Use Case of Deterministic Fog Nodes in Automotive Applications - Vehicle-Wide /Virtualization 206 / /8.6 Conclusion 209 / /References 209 / /9 Geographic Addressing for Field Networks 211 /Robert J. Hall / /9.1 Introduction 211 / /9.1.1 Field Networking 211 / /9.1.2 Challenges of Field Networking 212 / /9.2 Geographic Addressing 214 / /9.3 SAGP: Wireless GA in the Field 215 / /9.3.1 SAGP Processing 216 / /9.3.2 SAGP Retransmission Heuristics 217 / /9.3.3 Example of SAGP Packet Propagation 218 / /9.3.4 Followcast: Efficient SAGP Streaming 219 / /9.3.5 Meeting the Challenges 220 / /9.4 Georouting: Extending GA to the Cloud 221 / /9.5 SGAF: A Multi-Tiered Architecture for Large-Scale GA 222 / /9.5.1 Bridging Between Tiers 223 / /9.5.2 Hybrid Security Architecture 225 / /9.6 The AT&T Labs Geocast System 225 / /9.7 Two GA Applications 226 / /9.7.1 PSCommander 226 / /9.7.2 Geocast Games 230 / /9.8 Conclusions 232 / /References 232 / /10 Distributed Online Learning and Stream Processing for a Smarter Planet 234 /Deepak S.

Turaga and Mihaela van der Schaar / /10.1 Introduction: Smarter Planet 234 / /10.2 Illustrative Problem: Transportation 237 / /10.3 Stream Processing Characteristics 238 / /10.4 Distributed Stream Processing Systems 239 / /10.4.1 State of the Art 239 / /10.4.2 Stream Processing Systems 240 / /10.5 Distributed Online Learning Frameworks 244 / /10.5.1 State of the Art 244 / /10.5.2 Systematic Framework for Online Distributed Ensemble Learning 247 / /10.5.3 Online Learning of the Aggregation Weights 250 / /10.5.4 Collision Detection Application 254 / /10.6 What Lies Ahead 257 / /Acknowledgment 258 / /References 258 / /11 Securing the Internet of Things: Need for a New Paradigm and Fog Computing 261 /Tao Zhang, Yi Zheng, Raymond Zheng, and Helder Antunes / /11.1 Introduction 261 / /11.2 New IoT Security Challenges That Necessitate Fundamental Changes to the Existing Security /Paradigm 263 / /11.2.1 Many Things Will Have Long Life Spans but Constrained and Difficult-to-Upgrade Resources 264 / /11.2.2 Putting All IoT Devices Inside Firewalled Castles Will Become Infeasible or Impractical 264 / /11.2.3 Mission-Critical Systems Will Demand Minimal-Impact Incident Responses 265 / /11.2.4 The Need to Know the Security Status of a Vast Number of Devices 266 / /11.3 A New Security Paradigm for the Internet of Things 268 / /11.3.1 Help the Less Capable with Fog Computing 269 / /11.3.2 Scale Security Monitoring to Large Number of Devices with Crowd Attestation 272 / /11.3.3 Dynamic Risk / Benefit-Proportional Protection with Adaptive Immune Security 277 / /11.4 Summary 281 / /Acknowledgment 281 / /References 281 / /INDEX 285.

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Fog is starting to shape the future of the balance of power in information technology The book examines how fog will change the information technology industry in the next decade. Along the cloud-to-things continuum, fog distributes the services of computation, communication, control, and storage closer to the edge, access, and users. As a computing and networking architecture, fog enables key applications in wireless 5G, the Internet of things (IoT), and big data. The authors cover the fundamental trade-offs to major applications of fog. The book chapters are designed to motivate a transition from the current cloud architectures to the fog (Chapter 1) and the necessary architectural components to support such a transition (Chapters 2 / 6). The rest of the chapters (Chapters 7 / 11) are dedicated to reviewing various 5G and IoT applications that will benefit from fog networking. This volume is edited by pioneers in fog and includes contributions by active researchers in the field. . Covers fog technologies and describes the interaction between fog and cloud. Presents a view of fog and IoT that combines the aspects of both industry and academia. Discusses the various architectural and design challenges in coordinating the interactions between M2M, D2D, and fog technologies "Fog for 5G and IoT" serves as an introduction to the evolving fog architecture, compiling work from different areas that collectively form this paradigm.

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