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Fog for 5G and IoT [Kõva köide]

Edited by , Edited by , Edited by (Professor, Princeton University)
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Püsilink: https://www.kriso.ee/db/9781119187134.html
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The book examines how Fog will change the information technology industry in the next decade. 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, and big data. The authors cover the fundamental tradeoffs 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 book (Chapters 7-xxx) are dedicated to reviewing the 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 (encompassing ubiquitous computing) 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
Contributors xi
Introduction 1(10)
Bharath Balasubramanian
Mung Chiang
Flavio Bonomi
I.1 Summary of
Chapters
5(2)
I.2 Acknowledgments
7(1)
References
8(3)
I Communication And Management Of Fog 11(96)
1 ParaDrop: An Edge Computing Platform in Home Gateways
13(11)
Suman Banerjee
Peng Liu
Ashish Patro
Dale Willis
1.1 Introduction
13(4)
1.1.1 Enabling Multitenant Wireless Gateways and Applications through ParaDrop
14(1)
1.1.2 ParaDrop Capabilities
15(2)
1.2 Implementing Services for the ParaDrop Platform
17(2)
1.3 Develop Services for ParaDrop
19(4)
1.3.1 A Security Camera Service Using ParaDrop
19(3)
1.3.2 An Environmental Sensor Service Using ParaDrop
22(1)
References
23(1)
2 Mind Your Own Bandwidth
24(28)
Carlee Joe-Wong
Sangtae Ha
Zhenming Liu
Felix Ming Fai Wong
Mung Chiang
2.1 Introduction
24(4)
2.1.1 Leveraging the Fog
25(1)
2.1.2 A Home Solution to a Home Problem
25(3)
2.2 Related Work
28(1)
2.3 Credit Distribution and Optimal Spending
28(4)
2.3.1 Credit Distribution
29(2)
2.3.2 Optimal Credit Spending
31(1)
2.4 An Online Bandwidth Allocation Algorithm
32(3)
2.4.1 Estimating Other Gateways' Spending
32(2)
2.4.2 Online Spending Decisions and App Prioritization
34(1)
2.5 Design and Implementation
35(4)
2.5.1 Traffic and Device Classification
37(1)
2.5.2 Rate Limiting Engine
37(1)
2.5.3 Traffic Prioritization Engine
38(1)
2.6 Experimental Results
39(2)
2.6.1 Rate Limiting
39(2)
2.6.2 Traffic Prioritization
41(1)
2.7 Gateway Sharing Results
41(4)
2.8 Concluding Remarks
45(1)
Acknowledgments
46(1)
Appendix 2.A
46(4)
2.A.1 Proof of Lemma 2.1
46(1)
2.A.2 Proof of Lemma 2.2
46(1)
2.A.3 Proof of Proposition 2.1
47(1)
2.A.4 Proof of Proposition 2.2
48(1)
2.A.5 Proof of Proposition 2.3
49(1)
2.A.6 Proof of Proposition 2.4
49(1)
References
50(2)
3 Socially-Aware Cooperative D2D and D4D Communications toward Fog Networking
52(34)
Xu Chen
Junshan Zhang
Satyajayant Misra
3.1 Introduction
52(6)
3.1.1 From Social Trust and Social Reciprocity to D2D Cooperation
54(1)
3.1.2 Smart Grid: An IoT Case for Socially-Aware Cooperative D2D and D4D Communications
55(2)
3.1.3 Summary of Main Results
57(1)
3.2 Related Work
58(1)
3.3 System Model
59(3)
3.3.1 Physical (Communication) Graph Model
60(1)
3.3.2 Social Graph Model
61(1)
3.4 Socially-Aware Cooperative D2D and D4D Communications toward Fog Networking
62(7)
3.4.1 Social Trust-Based Relay Selection
63(1)
3.4.2 Social Reciprocity-Based Relay Selection
63(5)
3.4.3 Social Trust and Social Reciprocity-Based Relay Selection
68(1)
3.5 Network Assisted Relay Selection Mechanism
69(6)
3.5.1 Reciprocal Relay Selection Cycle Finding
69(1)
3.5.2 NARS Mechanism
70(3)
3.5.3 Properties of NARS Mechanism
73(2)
3.6 Simulations
75(7)
3.6.1 Erdos-Renyi Social Graph
76(2)
3.6.2 Real Trace Based Social Graph
78(4)
3.7 Conclusion
82(1)
Acknowledgments
82(1)
References
83(3)
4 You Deserve Better Properties (From Your Smart Devices)
86(21)
Steven Y. Ko
4.1 Why We Need to Provide Better Properties
86(1)
4.2 Where We Need to Provide Better Properties
87(1)
4.3 What Properties We Need to Provide and How
88(14)
4.3.1 Transparency
88(5)
4.3.2 Predictable Performance
93(6)
4.3.3 Openness
99(3)
4.4 Conclusions
102(1)
Acknowledgment
102(1)
References
103(4)
II Storage And Computation In Fog 107(82)
5 Distributed Caching for Enhancing Communications Efficiency
109(24)
A. Salman Avestimehr
Andreas F. Molisch
5.1 Introduction
109(2)
5.2 Femtocaching
111(4)
5.2.1 System Model
111(3)
5.2.2 Adaptive Streaming from Helper Stations
114(1)
5.3 User-Caching
115(15)
5.3.1 Cluster-Based Caching and D2D Communications
115(3)
5.3.2 IT LinQ-Based Caching and Communications
118(8)
5.3.3 Coded Multicast
126(4)
5.4 Conclusions and Outlook
130(1)
References
131(2)
6 Wireless Video Fog: Collaborative Live Streaming with Error Recovery
133(26)
Bo Zhang
Zhi Liu
S.H. Gary Chan
6.1 Introduction
133(3)
6.2 Related Work
136(2)
6.3 System Operation and Network Model
138(2)
6.4 Problem Formulation and Complexity
140(4)
6.4.1 NC Packet Selection Optimization
140(3)
6.4.2 Broadcaster Selection Optimization
143(1)
6.4.3 Complexity Analysis
144(1)
6.5 VBCR: A Distributed Heuristic for Live Video with Cooperative Recovery
144(6)
6.5.1 Initial Information Exchange
145(1)
6.5.2 Cooperative Recovery
145(2)
6.5.3 Updated Information Exchange
147(1)
6.5.4 Video Packet Forwarding
147(3)
6.6 Illustrative Simulation Results
150(6)
6.7 Concluding Remarks
156(1)
References
156(3)
7 Elastic Mobile Device Clouds: Leveraging Mobile Devices to Provide Cloud Computing Services at the Edge
159(30)
Karim Habak
Cong Shi
Ellen W. Zegura
Khaled A. Harras
Mostafa Ammar
7.1 Introduction
159(2)
7.2 Design Space with Examples
161(7)
7.2.1 Mont-Blanc
162(1)
7.2.2 Computing while Charging
163(1)
7.2.3 FemtoCloud
164(2)
7.2.4 Serendipity
166(2)
7.3 FemtoCloud Performance Evaluation
168(7)
7.3.1 Experimental Setup
168(1)
7.3.2 FemtoCloud Simulation Results
169(4)
7.3.3 FemtoCloud Prototype Evaluation
173(2)
7.4 Serendipity Performance Evaluation
175(11)
7.4.1 Experimental Setup
175(1)
7.4.2 Serendipity's Performance Benefits
176(3)
7.4.3 Impact of Network Environment
179(3)
7.4.4 The Impact of the Job Properties
182(4)
7.5 Challenges
186(1)
References
186(3)
III Applications Of Fog 189(96)
8 The Role of Fog Computing in the Future of the Automobile
191(20)
Flavio Bonomi
Stefan Poledna
Wilfried Steiner
8.1 Introduction
191(2)
8.2 Current Automobile Electronic Architectures
193(2)
8.3 Future Challenges of Automotive E/E Architectures and Solution Strategies
195(5)
8.4 Future Automobiles as Fog Nodes on Wheels
200(3)
8.5 Deterministic FOG Nodes on Wheels Through Real-Time Computing and Time-Triggered Technologies
203(6)
8.5.1 Deterministic Fog Node Addressing the Scalability Challenge through Virtualization
203(1)
8.5.2 Deterministic Fog Node Addressing the Connectivity and Security Challenges
204(2)
8.5.3 Emerging Use Case of Deterministic Fog Nodes in Automotive Applications-Vehicle-Wide Virtualization
206(3)
8.6 Conclusion
209(1)
References
209(2)
9 Geographic Addressing for Field Networks
211(23)
Robert J. Hall
9.1 Introduction
211(3)
9.1.1 Field Networking
211(1)
9.1.2 Challenges of Field Networking
212(2)
9.2 Geographic Addressing
214(1)
9.3 SAGP: Wireless GA in the Field
215(6)
9.3.1 SAGP Processing
216(1)
9.3.2 SAGP Retransmission Heuristics
217(1)
9.3.3 Example of SAGP Packet Propagation
218(1)
9.3.4 Followcast: Efficient SAGP Streaming
219(1)
9.3.5 Meeting the Challenges
220(1)
9.4 Georouting: Extending GA to the Cloud
221(1)
9.5 SGAF: A Multi-Tiered Architecture for Large-Scale GA
222(3)
9.5.1 Bridging Between Tiers
223(2)
9.5.2 Hybrid Security Architecture
225(1)
9.6 The AT&T Labs Geocast System
225(1)
9.7 Two GA Applications
226(6)
9.7.1 PSCommander
226(4)
9.7.2 Geocast Games
230(2)
9.8 Conclusions
232(1)
References
232(2)
10 Distributed Online Learning and Stream Processing for a Smarter Planet
234(27)
Deepak S. Turaga
Mihaela van der Schaar
10.1 Introduction: Smarter Planet
234(3)
10.2 Illustrative Problem: Transportation
237(1)
10.3 Stream Processing Characteristics
238(1)
10.4 Distributed Stream Processing Systems
239(5)
10.4.1 State of the Art
239(1)
10.4.2 Stream Processing Systems
240(4)
10.5 Distributed Online Learning Frameworks
244(13)
10.5.1 State of the Art
244(3)
10.5.2 Systematic Framework for Online Distributed Ensemble Learning
247(3)
10.5.3 Online Learning of the Aggregation Weights
250(4)
10.5.4 Collision Detection Application
254(3)
10.6 What Lies Ahead
257(1)
Acknowledgment
258(1)
References
258(3)
11 Securing the Internet of Things: Need for a New Paradigm and Fog Computing
261(24)
Tao Zhang
Yi Zheng
Raymond Zheng
Helder Antunes
11.1 Introduction
261(2)
11.2 New IoT Security Challenges That Necessitate Fundamental Changes to the Existing Security Paradigm
263(5)
11.2.1 Many Things Will Have Long Life Spans but Constrained and Difficult-to-Upgrade Resources
264(1)
11.2.2 Putting All IoT Devices Inside Firewalled Castles Will Become Infeasible or Impractical
264(1)
11.2.3 Mission-Critical Systems Will Demand Minimal-Impact Incident Responses
265(1)
11.2.4 The Need to Know the Security Status of a Vast Number of Devices
266(2)
11.3 A New Security Paradigm for the Internet of Things
268(13)
11.3.1 Help the Less Capable with Fog Computing
269(3)
11.3.2 Scale Security Monitoring to Large Number of Devices with Crowd Attestation
272(5)
11.3.3 Dynamic Risk-Benefit-Proportional Protection with Adaptive Immune Security
277(4)
11.4 Summary
281(1)
Acknowledgment
281(1)
References
281(4)
Index 285
Mung Chiang is the Arthur LeGrand Doty Professor of Electrical Engineering at Princeton University, the Director of the Keller Center for Innovation in Engineering Education, and the Chair of Princeton Entrepreneurship Council, USA. Dr. Chiang founded the Princeton EDGE Lab in 2009 and a co-founder of OpenFog Consortium in 2015. He is the recipient of the 2013 Alan T. Waterman Award by US National Science Foundation.

Bharath Balasubramanian is a distributed systems researcher in the Cloud Software Research Department at ATT Labs Research, USA. Prior to this, he was a postdoc in the Electrical Engineering Department at Princeton University, working with Mung Chiang in the EDGE Lab.

Flavio Bonomi is the CEO of Nebbiolo Technologies, USA. Before that he was a Vice President and Fellow at Cisco, USA.