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E-raamat: Fog Computing: Theory and Practice

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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.

List of Contributors
xxiii
Acronyms xxix
Part I Fog Computing Systems and Architectures
1(308)
1 Mobile Fog Computing
3(40)
Chii Chang
Amnir Hadachi
Jakob Mass
Satish Narayana Srirama
1.1 Introduction
3(2)
1.2 Mobile Fog Computing and Related Models
5(1)
1.3 The Needs of Mobile Fog Computing
6(9)
1.3.1 Infrastructural Mobile Fog Computing
7(1)
1.3.1.1 Road Crash Avoidance
7(1)
1.3.1.2 Marine Data Acquisition
7(1)
1.3.1.3 Forest Fire Detection
8(1)
1.3.1.4 Mobile Ambient Assisted Living
9(1)
1.3.2 Land Vehicular Fog
9(2)
1.3.3 Marine Fog
11(1)
1.3.4 Unmanned Aerial Vehicular Fog
12(1)
1.3.5 User Equipment-Based Fog
13(1)
1.3.5.1 Healthcare
13(1)
1.3.5.2 Content Delivery
14(1)
1.3.5.3 Crowd Sensing
15(1)
1.4 Communication Technologies
15(3)
1.4.1 IEEE 802.11
15(1)
1.4.2 4G, 5G Standards
16(1)
1.4.3 WPAN, Short-Range Technologies
17(1)
1.4.4 LPWAN, Other Medium-and Long-Range Technologies
18(1)
1.5 Nonfunctional Requirements
18(13)
1.5.1 Heterogeneity
20(1)
1.5.1.1 Server Heterogeneity
21(1)
1.5.1.2 End-Device Heterogeneity
21(1)
1.5.1.3 End-to-End Network Heterogeneity
22(1)
1.5.2 Context-Awareness
23(1)
1.5.2.1 Server Context
23(1)
1.5.2.2 Mobility Context
23(1)
1.5.2.3 End-to-end Context
24(1)
1.5.2.4 Application Context
24(1)
1.5.3 Tenant
25(1)
1.5.3.1 Application Management
25(2)
1.5.3.2 Cost of Energy and Tenancy
27(1)
1.5.4 Provider
27(1)
1.5.4.1 Physical Placement
27(1)
1.5.4.2 Server Discoverability and Connectivity
28(1)
1.5.4.3 Operation Management
28(1)
1.5.4.4 Operation Cost
29(1)
1.5.5 Security
29(1)
1.5.5.1 Physical Security
30(1)
1.5.5.2 End-to-End Security
30(1)
1.5.5.3 Security Monitoring and Management
30(1)
1.5.5.4 Trust Management and Multitenancy Security
31(1)
1.6 Open Challenges
31(4)
1.6.1 Challenges in Land Vehicular Fog Computing
31(1)
1.6.2 Challenges in Marine Fog Computing
32(1)
1.6.3 Challenges in Unmanned Aerial Vehicular Fog Computing
32(1)
1.6.4 Challenges in User Equipment-based Fog Computing
33(1)
1.6.5 General Challenges
33(1)
1.6.5.1 Testbed Tool
33(1)
1.6.5.2 Autonomous Runtime Adjustment and Rapid Redeployment
34(1)
1.6.5.3 Scheduling of Fog Applications
34(1)
1.6.5.4 Scalable Resource Management of Fog Providers
35(1)
1.7 Conclusion
35(8)
Acknowledgment
36(1)
References
36(7)
2 Edge and Fog: A Survey, Use Cases, and Future Challenges
43(24)
Cosmin Avasalcai
Mr Murturi
Schahram Dustdar
2.1 Introduction
43(1)
2.2 Edge Computing
44(3)
2.2.1 Edge Computing Architecture
46(1)
2.3 Fog Computing
47(3)
2.3.1 Fog Computing Architecture
49(1)
2.4 Fog and Edge Illustrative Use Cases
50(7)
2.4.1 Edge Computing Use Cases
50(1)
2.4.1.1 A Wearable ECG Sensor
51(1)
2.4.1.2 Smart Home
52(2)
2.4.2 Fog Computing Use Cases
54(1)
2.4.2.1 Smart Traffic Light System
54(1)
2.4.2.2 Smart Pipeline Monitoring System
55(2)
2.5 Future Challenges
57(4)
2.5.1 Resource Management
57(1)
2.5.2 Security and Privacy
58(3)
2.5.3 Network Management
61(1)
2.6 Conclusion
61(6)
Acknowledgment
62(1)
References
62(5)
3 Deep Learning in the Era of Edge Computing: Challenges and Opportunities
67(12)
Mi Zhang
Faen Zhang
Nicholas D. Lane
Yuanchao Shu
Xiao Zeng
Biyi Fang
Shen Yan
Hui Xu
3.1 Introduction
67(1)
3.2 Challenges and Opportunities
68(8)
3.2.1 Memory and Computational Expensiveness of DNN Models
68(2)
3.2.2 Data Discrepancy in Real-world Settings
70(1)
3.2.3 Constrained Battery Life of Edge Devices
71(1)
3.2.4 Heterogeneity in Sensor Data
72(1)
3.2.5 Heterogeneity in Computing Units
73(1)
3.2.6 Multitenancy of Deep Learning Tasks
73(2)
3.2.7 Offloading to Nearby Edges
75(1)
3.2.8 On-device Training
76(1)
3.3 Concluding Remarks
76(3)
References
77(2)
4 Caching, Security, and Mobility in Content-centric Networking
79(26)
Osman Khalid
Imran Ali Khan
Rao Naveed Bin Rais
Assad Abbas
4.1 Introduction
79(2)
4.2 Caching and Fog Computing
81(1)
4.3 Mobility Management in CCN
82(6)
4.3.1 Classification of CCN Contents and their Mobility
83(1)
4.3.2 User Mobility
83(1)
4.3.3 Server-side Mobility
84(1)
4.3.4 Direct Exchange for Location Update
84(1)
4.3.5 Query to the Rendezvous for Location Update
84(1)
4.3.6 Mobility with Indirection Point
84(1)
4.3.7 Interest Forwarding
85(1)
4.3.8 Proxy-based Mobility Management
85(1)
4.3.9 Tunnel-based Redirection (TBR)
86(2)
4.4 Security in Content-centric Networks
88(3)
4.4.1 Risks Due to Caching
90(1)
4.4.2 DOS Attack Risk
90(1)
4.4.3 Security Model
91(1)
4.5 Caching
91(10)
4.5.1 Cache Allocation Approaches
91(2)
4.5.2 Data Allocation Approaches
93(8)
4.6 Conclusions
101(4)
References
101(4)
5 Security and Privacy Issues in Fog Computing
105(34)
Ahmad Ati
Mansoor Ahmed
Muhammad Imran
Hasan Ati Khattak
5.1 Introduction
105(2)
5.2 Trust in IoT
107(2)
5.3 Authentication
109(4)
5.3.1 Related Work
109(4)
5.4 Authorization
113(4)
5.4.1 Related Work
114(3)
5.5 Privacy
117(3)
5.5.1 Requirements of Privacy in IoT
118(1)
5.5.1.1 Device Privacy
118(1)
5.5.1.2 Communication Privacy
118(1)
5.5.1.3 Storage Privacy
118(1)
5.5.1.4 Processing Privacy
118(2)
5.6 Web Semantics and Trust Management for Fog Computing
120(3)
5.6.1 Trust Through Web Semantics
120(3)
5.7 Discussion
123(7)
5.7.1 Authentication
124(1)
5.7.2 Authorization
125(5)
5.8 Conclusion
130(9)
References
130(9)
6 How Fog Computing Can Support Latency/Reliability-sensitive IoT Applications: An Overview and a Taxonomy of State-of-the-art Solutions
139(76)
Paolo Bellavista
Javier Berrocal
Antonio Corradi
Sajal K. Das
Luca Foschini
Isam Mashhour Al Jawarneh
Alessandro Zanni
6.1 Introduction
139(3)
6.2 Fog Computing for IoT: Definition and Requirements
142(12)
6.2.1 Definitions
142(2)
6.2.2 Motivations
144(4)
6.2.3 Fog Computing Requirements When Applied to Challenging IoTs Application Domains
148(1)
6.2.3.1 Scalability
148(1)
6.2.3.2 Interoperability
149(1)
6.2.3.3 Real-Time Responsiveness
149(1)
6.2.3.4 Data Quality
150(1)
6.2.3.5 Security and Privacy
151(1)
6.2.3.6 Location-Awareness
152(1)
6.2.3.7 Mobility
152(1)
6.2.4 IoT Case Studies
152(2)
6.3 Fog Computing: Architectural Model
154(4)
6.3.1 Communication
154(2)
6.3.2 Security and Privacy
156(1)
6.3.3 Internet of Things
156(1)
6.3.4 Data Quality
156(1)
6.3.5 Cloudification
157(1)
6.3.6 Analytics and Decision-Making
157(1)
6.4 Fog Computing for IoT: A Taxonomy
158(31)
6.4.1 Communication
159(1)
6.4.1.1 Standardization
160(2)
6.4.1.2 Reliability
162(1)
6.4.1.3 Low-Latency
163(1)
6.4.1.4 Mobility
164(1)
6.4.2 Security and Privacy Layer
165(1)
6.4.2.1 Safety
166(1)
6.4.2.2 Security
166(3)
6.4.2.3 Privacy
169(1)
6.4.3 Internet of Things
170(1)
6.4.3.1 Sensors
171(1)
6.4.3.2 Actuators
171(2)
6.4.4 Data Quality
173(1)
6.4.4.1 Data Normalization
174(2)
6.4.4.2 Data Filtering
176(1)
6.4.4.3 Data Aggregation
177(2)
6.4.5 Cloudification
179(1)
6.4.5.1 Virtualization
179(3)
6.4.5.2 Storage
182(1)
6.4.6 Analytics and Decision-Making Layer
183(1)
6.4.6.1 Data Analytics
184(2)
6.4.6.2 Decision-Making
186(3)
6.5 Comparisons of Surveyed Solutions
189(9)
6.5.1 Communication
189(1)
6.5.1.1 Standardization
190(1)
6.5.1.2 Reliability
190(1)
6.5.1.3 Low-latency Communication
190(1)
6.5.1.4 Mobility
191(1)
6.5.2 Security and Privacy
191(1)
6.5.2.1 Security
191(1)
6.5.2.2 Privacy
192(1)
6.5.2.3 Safety
192(1)
6.5.3 Internet of Things
193(1)
6.5.3.1 Sensors
193(1)
6.5.3.2 Actuators
194(1)
6.5.4 Data Quality
194(1)
6.5.4.1 Data Normalization
195(1)
6.5.4.2 Data Filtering
195(1)
6.5.4.3 Data Aggregation
195(1)
6.5.5 Cloudification
195(1)
6.5.5.1 Virtualization
195(1)
6.5.5.2 Storage
196(1)
6.5.6 Analytics and Decision-Making Layer
197(1)
6.5.6.1 Data Analytics
197(1)
6.5.6.2 Decision-Making
198(1)
6.6 Challenges and Recommended Research Directions
198(3)
6.7 Concluding Remarks
201(14)
References
202(13)
7 Harnessing the Computing Continuum for Programming Our World
215(16)
Pete Beckman
Jack Dongarra
Nicola Ferrier
Geoffrey Fox
Terry Moore
Dan Reed
Micah Beck
7.1 Introduction and Overview
215(2)
7.2 Research Philosophy
217(2)
7.3 A Goal-oriented Approach to Programming the Computing Continuum
219(9)
7.3.1 A Motivating Continuum Example
219(2)
7.3.2 Goal-oriented Annotations for Intensional Specification
221(1)
7.3.3 A Mapping and Run-time System for the Computing Continuum
222(2)
7.3.4 Building Blocks and Enabling Technologies
224(1)
7.3.4.1 The Array of Things (AoT)
225(1)
7.3.4.2 Iowa Quantified (IQ)
225(1)
7.3.4.3 Intelligent, Multiversion Libraries
225(1)
7.3.4.4 Data Flow Execution for Big Data
226(2)
7.4 Summary
228(3)
References
228(3)
8 Fog Computing for Energy Harvesting-enabled Internet of Things
231(14)
S. A. Tegos
P. D. Diamantoulakis
D. S. Michalopoulos
G. K. Karagiannidis
8.1 Introduction
231(1)
8.2 System Model
232(6)
8.2.1 Computation Model
233(1)
8.2.1.1 Local Execution Model
234(1)
8.2.1.2 Fog Execution Model
234(1)
8.2.2 Energy Harvesting Model
235(1)
8.2.2.1 Stochastic Process
235(1)
8.2.2.2 Wireless Power Transfer
236(2)
8.3 Tradeoffs in EH Fog Systems
238(2)
8.3.1 Energy Consumption vs. Latency
238(1)
8.3.2 Execution Delay vs. Task Dropping Cost
239(1)
8.4 Future Research Challenges
240(5)
Acknowledgment
241(1)
References
241(4)
9 Optimizing Energy Efficiency of Wearable Sensors Using Fog-assisted Control
245(24)
Delaram Amiri
Arman Anzanpour
Iman Azimi
Amir M. Rahmani
Pasi Liljeberg
Nikil Duff
Marco Levorato
9.1 Introduction
245(2)
9.2 Background
247(2)
9.3 Related Topics
249(1)
9.4 Design Challenges
250(1)
9.5 IoT System Architecture
251(2)
9.5.1 Fog Computing and its Benefits
252(1)
9.6 Fog-assisted Runtime Energy Management in Wearable Sensors
253(10)
9.6.1 Computational Self-Awareness
255(1)
9.6.2 Energy Optimization Algorithms
255(3)
9.6.3 Myopic Strategy
258(1)
9.6.4 MDP Strategy
259(4)
9.7 Conclusions
263(6)
Acknowledgment
264(1)
References
264(5)
10 Latency Minimization Through Optimal Data Placement in Fog Networks
269(24)
Ning Wang
Jie Wu
10.1 Introduction
269(3)
10.2 Related Work
272(1)
10.2.1 Long-Term and Short-Term Placement
272(1)
10.2.2 Data Replication
272(1)
10.3 Problem Statement
273(2)
10.3.1 Network Model
273(1)
10.3.2 Multiple Data Placement with Budget Problem
274(1)
10.3.3 Challenges
274(1)
10.4 Delay Minimization Without Replication
275(4)
10.4.1 Problem Formulation
275(1)
10.4.2 Min-Cost Flow Formulation
276(1)
10.4.3 Complexity Reduction
277(2)
10.5 Delay Minimization with Replication
279(6)
10.5.1 Hardness Proof
279(1)
10.5.2 Single Request in Line Topology
279(1)
10.5.3 Greedy Solution in Multiple Requests
280(2)
10.5.4 Rounding Approach in Multiple Requests
282(1)
10.5.4.1 Generating Linear Programming Solution
282(1)
10.5.4.2 Creating Centers
283(1)
10.5.4.3 Converting to Integral Solution
284(1)
10.6 Performance Evaluation
285(4)
10.6.1 Trace Information
285(1)
10.6.2 Experimental Setting
285(1)
10.6.3 Algorithm Comparison
286(1)
10.6.4 Experimental Results
287(1)
10.6.4.1 Trace Analysis
287(1)
10.6.4.2 Results Without Data Replication
288(1)
10.6.4.3 Results with Data Replication
288(1)
10.6.4.4 Summary
289(1)
10.7 Conclusion
289(4)
Acknowledgement
289(1)
References
290(3)
11 Modeling and Simulation of Distributed Fog Environment Using FogNetSim+4-
293(16)
Tariq Qayyum
Asad Waqar Malik
Muazzam A. Khan
Samee U. Khan
11.1 Introduction
293(1)
11.2 Modeling and Simulation
294(2)
11.3 FogNetSim++: Architecture
296(2)
11.4 FogNetSim++: Installation and Environment Setup
298(7)
11.4.1 OMNeT++ Installation
298(2)
11.4.2 FogNetSim++ Installation
300(1)
11.4.3 Sample Fog Simulation
300(5)
11.5 Conclusion
305(4)
References
305(4)
Part II Fog Computing Techniques and Applications
309(238)
12 Distributed Machine Learning for IoT Applications in the Fog
311(36)
Aluizio F. Rocha Neto
Flavia C. Delicato
Thais V. Batista
Paulo F. Pires
12.1 Introduction
311(3)
12.2 Challenges in Data Processing for IoT
314(8)
12.2.1 Big Data in IoT
315(3)
12.2.2 Big Data Stream
318(1)
12.2.3 Data Stream Processing
319(3)
12.3 Computational Intelligence and Fog Computing
322(6)
12.3.1 Machine Learning
322(4)
12.3.2 Deep Learning
326(2)
12.4 Challenges for Running Machine Learning on Fog Devices
328(6)
12.4.1 Solutions Available on the Market to Deploy ML on Fog Devices
331(3)
12.5 Approaches to Distribute Intelligence on Fog Devices
334(6)
12.6 Final Remarks
340(7)
Acknowledgments
341(1)
References
341(6)
13 Fog Computing-Based Communication Systems for Modern Smart Grids
347(24)
Miodrag Forcan
Mirjana Maksimovic
13.1 Introduction
347(2)
13.2 An Overview of Communication Technologies in Smart Grid
349(7)
13.3 Distribution Management System (DMS) Based on Fog/Cloud Computing
356(3)
13.4 Real-time Simulation of the Proposed Feeder-based Communication Scheme Using MATLAB and Thing Speak
359(7)
13.5 Conclusion
366(5)
References
367(4)
14 An Estimation of Distribution Algorithm to Optimize the Utility of Task Scheduling Under Fog Computing Systems
371(14)
Chu-ge Wu
Ling Wang
14.1 Introduction
371(1)
14.2 Estimation of Distribution Algorithm
372(1)
14.3 Related Work
373(1)
14.4 Problem Statement
374(2)
14.5 Details of Proposed Algorithm
376(2)
14.5.1 Encoding and Decoding Method
376(1)
14.5.2 uEDA Scheme
377(1)
14.5.2.1 Probability Model and Initialization
377(1)
14.5.2.2 Updating and Sampling Method
377(1)
14.5.3 Local Search Method
378(1)
14.6 Simulation
378(5)
14.6.1 Comparison Algorithm
378(1)
14.6.2 Simulation Environment and Experiment Settings
379(2)
14.6.3 Compared with the Heuristic Method
381(2)
14.7 Conclusion
383(2)
References
383(2)
15 Reliable and Power-Efficient Machine Learning in Wearable Sensors
385(26)
Parastoo Alinia
Hassan Ghasemzadeh
15.1 Introduction
385(1)
15.2 Preliminaries and Related Work
386(3)
15.2.1 Gold Standard MET Computation
386(1)
15.2.2 Sensor-based MET Estimation
387(1)
15.2.3 Unreliability Mitigation
388(1)
15.2.4 Transfer Learning
388(1)
15.3 System Architecture and Methods
389(5)
15.3.1 Reliable MET Calculation
390(1)
15.3.1.1 Sensor Localization
390(2)
15.3.1.2 MET Value Estimation
392(1)
15.3.2 The Reconfigurable MET Estimation System
392(2)
15.4 Data Collection and Experimental Procedures
394(2)
15.4.1 Exergaming Experiment
394(1)
15.4.2 Treadmill Experiment
395(1)
15.5 Results
396(8)
15.5.1 Reliable MET Calculation
396(2)
15.5.1.1 Sensor Localization
398(1)
15.5.1.2 MET Value Estimation
398(1)
15.5.1.3 The Impact of Sensor Localization
399(3)
15.5.2 Reconfigurable Design
402(1)
15.5.2.1 Treadmill Experiment
402(1)
15.5.2.2 Exergaming Experiment
403(1)
15.6 Discussion and Future Work
404(1)
15.7 Summary
405(6)
References
406(5)
16 Insights into Software-Defined Networking and Applications in Fog Computing
411(20)
Osman Khalid
Imran Ati Khan
Assad Abbas
16.1 Introduction
411(3)
16.2 OpenFlow Protocol
414(2)
16.2.1 OpenFlow Switch
414(2)
16.3 SDN-Based Research Works
416(3)
16.4 SDN in Fog Computing
419(2)
16.5 SDN in Wireless Mesh Networks
421(3)
16.5.1 Challenges in Wireless Mesh Networks
421(1)
16.5.2 SDN Technique in WMNs
421(2)
16.5.3 Benefits of SDN in WMNs
423(1)
16.5.4 Fault Tolerance in SDN-based WMNs
424(1)
16.6 SDN in Wireless Sensor Networks
424(3)
16.6.1 Challenges in Wireless Sensor Networks
424(1)
16.6.2 SDN in Wireless Sensor Networks
425(1)
16.6.3 Sensor Open Flow
426(1)
16.6.4 Home Networks Using SDWN
426(1)
16.6.5 Securing Software Defined Wireless Networks (SDWN)
426(1)
16.7 Conclusion
427(4)
References
427(4)
17 Time-Critical Fog Computing for Vehicular Networks
431(28)
Ahmed Chebaane
Abdelmajid Khelit
Neeraj Suri
17.1 Introduction
431(3)
17.2 Applications and Timeliness Guarantees and Perturbations
434(9)
17.2.1 Application Scenarios
434(2)
17.2.2 Application Model
436(1)
17.2.3 Timeliness Guarantees
436(1)
17.2.4 Benchmarking Vehicular Applications Concerning Timeliness Guarantees
437(3)
17.2.5 Building Blocks to Reach Timeliness Guarantees
440(1)
17.2.6 Timeliness Perturbations
441(1)
17.2.6.1 Constraints
441(1)
17.2.6.2 Failures
442(1)
17.2.6.3 Threats
442(1)
17.3 Coping with Perturbation to Meet Timeliness Guarantees
443(6)
17.3.1 Coping with Constraints
443(1)
17.3.1.1 Network Resource Management
443(1)
17.3.1.2 Computational Resource and Data Management
444(4)
17.3.2 Coping with Failures
448(1)
17.3.3 Coping with Threats
448(1)
17.4 Research Gaps and Future Research Directions
449(2)
17.4.1 Mobile Fog Computing
449(1)
17.4.2 Fog Service Level Agreement (SLA)
450(1)
17.5 Conclusion
451(8)
References
451(8)
18 A Reliable and Efficient Fog-Based Architecture for Autonomous Vehicular Networks
459(14)
Shuja Mughal
Kamran Sattar Awaisi
Assad Abbas
Inayat ur Rehman
Muhammad Usman Shahid Khan
Mazhar Ali
18.1 Introduction
459(2)
18.2 Proposed Methodology
461(2)
18.3 Hypothesis Formulation
463(1)
18.4 Simulation Design
464(5)
18.4.1 Results and Discussions
464(3)
18.4.2 Hypothesis Testing
467(1)
18.4.2.1 First Hypothesis
467(1)
18.4.2.2 Second Hypothesis
468(1)
18.4.2.3 Third Hypothesis
469(1)
18.5 Conclusions
469(4)
References
470(3)
19 Fog Computing to Enable Geospatial Video Analytics for Disaster-incident Situational Awareness
473(32)
Dmitrii Chemodanov
Prasad Calyam
Kannappan Palaniappan
19.1 Introduction
473(5)
19.1.1 How Can Geospatial Video Analytics Help with Disaster-Incident Situational Awareness?
473(1)
19.1.2 Fog Computing for Geospatial Video Analytics
474(1)
19.1.3 Function-Centric Cloud/Fog Computing Paradigm
475(1)
19.1.4 Function-Centric Fog/Cloud Computing Challenges
476(1)
19.1.5
Chapter Organization
477(1)
19.2 Computer Vision Application Case Studies and FCC Motivation
478(6)
19.2.1 Patient Tracking with Face Recognition Case Study
478(1)
19.2.1.1 Application's 3C Pipeline Needs
478(1)
19.2.1.2 Face Recognition Pipeline Details
479(1)
19.2.2 3-D Scene Reconstruction from LIDAR Scans
480(1)
19.2.2.1 Application's 3C Pipeline Needs
480(1)
19.2.2.2 3-D Scene Reconstruction Pipeline Details
481(1)
19.2.3 Tracking Objects of Interest in WAMI
482(1)
19.2.3.1 Application's 3C Pipeline Needs
482(1)
19.2.3.2 Object Tracking Pipeline Details
483(1)
19.3 Geospatial Video Analytics Data Collection Using Edge Routing
484(6)
19.3.1 Network Edge Geographic Routing Challenges
484(2)
19.3.2 Artificial Intelligence Relevance in Geographic Routing
486(1)
19.3.3 AI-Augmented Geographic Routing Implementation
487(3)
19.4 Fog/Cloud Data Processing for Geospatial Video Analytics Consumption
490(6)
19.4.1 Geo-Distributed Latency-Sensitive SFC Challenges
491(1)
19.4.1.1 SFC Optimality
491(1)
19.4.1.2 SFC Reliability
491(1)
19.4.1.3 Our Approach
491(1)
19.4.2 Metapath-Based Composite Variable Approach
492(1)
19.4.2.1 Metalinks and Metapaths
492(1)
19.4.2.2 Constrained Shortest Metapaths
493(1)
19.4.2.3 Multiple-link Chain Composition via Metapath
493(2)
19.4.2.4 Allowable Fitness Functions for Metapath-Based Variables
495(1)
19.4.2.5 Metapath Composite Variable Approach Results
495(1)
19.4.3 Metapath-Based SFC Orchestration Implementation
495(1)
19.4.3.1 Control Applications
495(1)
19.4.3.2 SCL
496(1)
19.4.3.3 SDN and Hypervisor
496(1)
19.5 Concluding Remarks
496(9)
19.5.1 What Have We Learned?
496(1)
19.5.2 The Road Ahead and Open Problems
497(1)
References
498(7)
20 An Insight into 5G Networks with Fog Computing
505(24)
Osman Khalid
Imran Ali Khan
Rao Naveed Bin Rais
Asad Waqar Malik
20.1 Introduction
505(2)
20.2 Vision of 5G
507(1)
20.3 Fog Computing with 5G Networks
508(1)
20.3.1 Fog Computing
508(1)
20.3.2 The Need of Fog Computing in 5G Networks
508(1)
20.4 Architecture of 5G
508(6)
20.4.1 Cellular Architecture
508(2)
20.4.2 Energy Efficiency
510(2)
20.4.3 Two-Tier Architecture
512(1)
20.4.4 Cognitive Radio
512(1)
20.4.5 Cloud-Based Architecture
513(1)
20.5 Technology and Methodology for 5G
514(7)
20.5.1 HetNet
515(1)
20.5.2 Beam Division Multiple Access (BDMA)
516(1)
20.5.3 Mixed Bandwidth Data Path
516(1)
20.5.4 Wireless Virtualization
516(2)
20.5.5 Flexible Duplex
518(1)
20.5.6 Multiple-Input Multiple-Output (MIMO)
518(1)
20.5.7 M2M
519(1)
20.5.8 Multibeam-Based Communication System
520(1)
20.5.9 Software-Defined Networking (SDN)
520(1)
20.6 Applications
521(1)
20.6.1 Health Care
521(1)
20.6.2 Smart Grid
521(1)
20.6.3 Logistic and Tracking
521(1)
20.6.4 Personal Usage
521(1)
20.6.5 Virtualized Home
522(1)
20.7 Challenges
522(2)
20.8 Conclusion
524(5)
References
524(5)
21 Fog Computing for Bioinformatics Applications
529(18)
Hafeez Ur Rehman
Asad Khan
Usman Habib
21.1 Introduction
529(2)
21.2 Cloud Computing
531(2)
21.2.1 Service Models
532(1)
21.2.2 Delivery Models
532(1)
21.3 Cloud Computing Applications in Bioinformatics
533(4)
21.3.1 Bioinformatics Tools Deployed as SaaS
533(2)
21.3.2 Bioinformatics Platforms Deployed as PaaS
535(1)
21.3.3 Bioinformatics Tools Deployed as IaaS
535(2)
21.4 Fog Computing
537(2)
21.5 Fog Computing for Bioinformatics Applications
539(4)
21.5.1 Real-Time Microorganism Detection System
541(2)
21.6 Conclusion
543(4)
References
543(4)
Index 547
Assad Abbas, PhD, is an Assistant Professor in the Department of Computer Science, COMSATS University Islamabad, Pakistan. He is a member of IEEE and IEEE-Eta Kappa Nu (IEEE-HKN).

Samee U. Khan, PhD, is the Walter B. Booth Endowed Professor at the North Dakota State University, Fargo, ND, USA, and is on the editorial boards of several leading journals.

Albert Y. Zomaya, PhD, is the Chair Professor of High Performance Computing & Networking in the School of Computer Science, The University of Sydney. He is also the Director of the Centre for Distributed and High Performance Computing.
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