Cover
List of Contributors
Acronyms
Part I: Fog Computing Systems and Architectures
1. 1 Mobile Fog Computing
  1. 1.1 Introduction
  2. 1.2 Mobile Fog Computing and Related Models
  3. 1.3 The Needs of Mobile Fog Computing
  4. 1.4 Communication Technologies
  5. 1.5 Nonfunctional Requirements
  6. 1.6 Open Challenges
  7. 1.7 Conclusion
  8. Acknowledgment
  9. References
2. 2 Edge and Fog: A Survey, Use Cases, and Future Challenges
  1. 2.1 Introduction
  2. 2.2 Edge Computing
  3. 2.3 Fog Computing
  4. 2.4 Fog and Edge Illustrative Use Cases
  5. 2.5 Future Challenges
  6. 2.6 Conclusion
  7. Acknowledgment
  8. References
3. 3 Deep Learning in the Era of Edge Computing: Challenges and Opportunities
  1. 3.1 Introduction
  2. 3.2 Challenges and Opportunities
  3. 3.3 Concluding Remarks
  4. References
4. 4 Caching, Security, and Mobility in Content-centric Networking
  1. 4.1 Introduction
  2. 4.2 Caching and Fog Computing
  3. 4.3 Mobility Management in CCN
  4. 4.4 Security in Content-centric Networks
  5. 4.5 Caching
  6. 4.6 Conclusions
  7. References
5. 5 Security and Privacy Issues in Fog Computing
  1. 5.1 Introduction
  2. 5.2 Trust in IoT
  3. 5.3 Authentication
  4. 5.4 Authorization
  5. 5.5 Privacy
  6. 5.6 Web Semantics and Trust Management for Fog Computing
  7. 5.7 Discussion
  8. 5.8 Conclusion
  9. References
6. 6 How Fog Computing Can Support Latency/Reliability-sensitive IoT Applications: An Overview and a Taxonomy of State-of-the-art Solutions
  1. 6.1 Introduction
  2. 6.2 Fog Computing for IoT: Definition and Requirements
  3. 6.3 Fog Computing: Architectural Model
  4. 6.4 Fog Computing for IoT: A Taxonomy
  5. 6.5 Comparisons of Surveyed Solutions
  6. 6.6 Challenges and Recommended Research Directions
  7. 6.7 Concluding Remarks
  8. References
7. 7 Harnessing the Computing Continuum for Programming Our World
  1. 7.1 Introduction and Overview
  2. 7.2 Research Philosophy
  3. 7.3 A Goal-oriented Approach to Programming the Computing Continuum
  4. 7.4 Summary
  5. References
8. 8 Fog Computing for Energy Harvesting-enabled Internet of Things
  1. 8.1 Introduction
  2. 8.2 System Model
  3. 8.3 Tradeoffs in EH Fog Systems
  4. 8.4 Future Research Challenges
  5. Acknowledgment
  6. References
9. 9 Optimizing Energy Efficiency of Wearable Sensors Using Fog-assisted Control
  1. 9.1 Introduction
  2. 9.2 Background
  3. 9.3 Related Topics
  4. 9.4 Design Challenges
  5. 9.5 IoT System Architecture
  6. 9.6 Fog-assisted Runtime Energy Management in Wearable Sensors
  7. 9.7 Conclusions
  8. Acknowledgment
  9. References
10. 10 Latency Minimization Through Optimal Data Placement in Fog Networks
  1. 10.1 Introduction
  2. 10.2 Related Work
  3. 10.3 Problem Statement
  4. 10.4 Delay Minimization Without Replication
  5. 10.5 Delay Minimization with Replication
  6. 10.6 Performance Evaluation
  7. 10.7 Conclusion
  8. Acknowledgement
  9. References
11. 11 Modeling and Simulation of Distributed Fog Environment Using FogNetSim++
  1. 11.1 Introduction
  2. 11.2 Modeling and Simulation
  3. 11.3 FogNetSim++: Architecture
  4. 11.4 FogNetSim++: Installation and Environment Setup
  5. 11.5 Conclusion
  6. References
Part II: Fog Computing Techniques and Applications
1. 12 Distributed Machine Learning for IoT Applications in the Fog
  1. 12.1 Introduction
  2. 12.2 Challenges in Data Processing for IoT
  3. 12.3 Computational Intelligence and Fog Computing
  4. 12.4 Challenges for Running Machine Learning on Fog Devices
  5. 12.5 Approaches to Distribute Intelligence on Fog Devices
  6. 12.6 Final Remarks
  7. Acknowledgments
  8. References
2. 13 Fog Computing-Based Communication Systems for Modern Smart Grids
  1. 13.1 Introduction
  2. 13.2 An Overview of Communication Technologies in Smart Grid
  3. 13.3 Distribution Management System (DMS) Based on Fog/Cloud Computing
  4. 13.4 Real-time Simulation of the Proposed Feeder-based Communication Scheme Using MATLAB and ThingSpeak
  5. 13.5 Conclusion
  6. References
3. 14 An Estimation of Distribution Algorithm to Optimize the Utility of Task Scheduling Under Fog Computing Systems
  1. 14.1 Introduction
  2. 14.2 Estimation of Distribution Algorithm
  3. 14.3 Related Work
  4. 14.4 Problem Statement
  5. 14.5 Details of Proposed Algorithm
  6. 14.6 Simulation
  7. 14.7 Conclusion
  8. References
4. 15 Reliable and Power-Efficient Machine Learning in Wearable Sensors
  1. 15.1 Introduction
  2. 15.2 Preliminaries and Related Work
  3. 15.3 System Architecture and Methods
  4. 15.4 Data Collection and Experimental Procedures
  5. 15.5 Results
  6. 15.6 Discussion and Future Work
  7. 15.7 Summary
  8. References
5. 16 Insights into Software-Defined Networking and Applications in Fog Computing
  1. 16.1 Introduction
  2. 16.2 OpenFlow Protocol
  3. 16.3 SDN-Based Research Works
  4. 16.4 SDN in Fog Computing
  5. 16.5 SDN in Wireless Mesh Networks
  6. 16.6 SDN in Wireless Sensor Networks
  7. 16.7 Conclusion
  8. References
6. 17 Time-Critical Fog Computing for Vehicular Networks
  1. 17.1 Introduction
  2. 17.2 Applications and Timeliness Guarantees and Perturbations
  3. 17.3 Coping with Perturbation to Meet Timeliness Guarantees
  4. 17.4 Research Gaps and Future Research Directions
  5. 17.5 Conclusion
  6. References
7. 18 A Reliable and Efficient Fog-Based Architecture for Autonomous Vehicular Networks
  1. 18.1 Introduction
  2. 18.2 Proposed Methodology
  3. 18.3 Hypothesis Formulation
  4. 18.4 Simulation Design
  5. 18.5 Conclusions
  6. References
8. 19 Fog Computing to Enable Geospatial Video Analytics for Disaster-incident Situational Awareness
  1. 19.1 Introduction
  2. 19.2 Computer Vision Application Case Studies and FCC Motivation
  3. 19.3 Geospatial Video Analytics Data Collection Using Edge Routing
  4. 19.4 Fog/Cloud Data Processing for Geospatial Video Analytics Consumption
  5. 19.5 Concluding Remarks
  6. References
9. 20 An Insight into 5G Networks with Fog Computing
  1. 20.1 Introduction
  2. 20.2 Vision of 5G
  3. 20.3 Fog Computing with 5G Networks
  4. 20.4 Architecture of 5G
  5. 20.5 Technology and Methodology for 5G
  6. 20.6 Applications
  7. 20.7 Challenges
  8. 20.8 Conclusion
  9. References
10. 21 Fog Computing for Bioinformatics Applications
  1. 21.1 Introduction
  2. 21.2 Cloud Computing
  3. 21.3 Cloud Computing Applications in Bioinformatics
  4. 21.4 Fog Computing
  5. 21.5 Fog Computing for Bioinformatics Applications
  6. 21.6 Conclusion
  7. References
Index
End User License Agreement

List of Tables

Chapter 2
1. Table 2.1 Threat model for fog and edge computing [21].
Chapter 3
1. Table 3.1 Memory and computational expensiveness of some of the most commonly...
Chapter 4
1. Table 4.1 Caching schemes comparison.
2. Table 4.2 Objectives-based comparison.
Chapter 5
1. Table 5.1 State of the art research work timeline.
2. Table 5.2 Authentication grid.
3. Table 5.3 Authorization requirements in the Internet of Things.
4. Table 5.4 Privacy requirements in the light of Internet of Things.
Chapter 6
1. Table 6.1 Comparison between surveyed communication approaches.
2. Table 6.2 Comparison between surveyed security and privacy approaches.
3. Table 6.3 Comparison of the surveyed solution related to the Internet of Thin...
4. Table 6.4 Comparison of the surveyed solution related to the data quality lay...
5. Table 6.5 Comparison of the surveyed solution related to the cloudification l...
6. Table 6.6 Comparison of the surveyed solution related to the analytics and de...
Chapter 7
1. Table 7.1 Exemplar continuum computing science applications.
2. Table 7.2 Twister2 components and status.
Chapter 10
1. Table 10.1 Summary of symbols.
Chapter 12
1. Table 12.1 Comparison of machine learningtechnologies [14].
2. Table 12.2 Execution time (ms) for running DL models in three hardware platfo...
3. Table 12.3 Classification of smart IoT devices according to their capacities ...
Chapter 13
1. Table 13.1 Comparison of communication technologies for the SG [10–13].
Chapter 14
1. Table 14.1 Simulation environmental parameters.
2. Table 14.2 Comparison results of utility between heuristic and uEDA.
Chapter 15
1. Table 15.1 Extracted features from sensor signals.
2. Table 15.2 Energy consumption of various configurations, where computation is...
3. Table 15.3 Accuracy of sensor localization.
4. Table 15.4 The comparison between the accuracy of the regression on different...
5. Table 15.5 Leave-one-subject-out cross validation test.
6. Table 15.6 R² values from linear regression on MET vs Ankle and Hip Accelerometer...
7. Table 15.7 Comparing R² values and error of linear regression on MET vs Ankle ...
Chapter 16
1. Table 16.1 Comparison between centralized and distributed controller.
Chapter 17
1. Table 17.1 Benchmarking of application classes.
Chapter 18
1. Table 18.1 Results of the latency in the both architectures.
2. Table 18.2 Log-transformed data for latency.
3. Table 18.3 Results of the network usage in the both architectures.
4. Table 18.4 Log-transformed data for network usage.
Chapter 20
1. Table 20.1 Comparison of wireless technologies.
2. Table 20.2 5G protocol stack.
3. Table 20.3 Challenges of 5G.
4. Table 20.4 Research projects on 5G.
Chapter 21
1. Table 21.1 A summary of existing cloud computing based major bioinformatics a...
2. Table 21.2 Comparison between the cloud computing and fog computing paradigms...

List of Illustrations

Chapter 1
1. Figure 1.1 Land-vehicular fog computing examples. (See color plate section f...
2. Figure 1.2 Maritime fog computing examples.
3. Figure 1.3 UAV fog computing examples.
4. Figure 1.4 UE fog computing examples. (See color plate section for the color...
5. Figure 1.5 A taxonomy of non-functional requirements of mobile fog computing...
6. Figure 1.6 Fog infrastructure service provider, fog service tenant, and tena...
7. Figure 1.7 The three types of end-to-end networking.
Chapter 2
1. Figure 2.1 Edge computing solution using an IoT and edge devices [12].
2. Figure 2.2 An overview of edge computing architecture [16]. (See color plate...
3. Figure 2.3 Fog computing a bridge between cloud and edge [20].
4. Figure 2.4 Fog computing architecture [10]. (See color plate section for the...
5. Figure 2.5 A wearable ECG sensor.
6. Figure 2.6 Structure of edgeOS in the smart home environment [3].
7. Figure 2.7 Smart Traffic light system.
8. Figure 2.8 Smart pipeline monitoring system architecture.
Chapter 3
1. Figure 3.1 Illustration of differences between training and test images of t...
2. Figure 3.2 Illustration of data sharing mechanism.
3. Figure 3.3 Illustration of intermediate results of a DNN model. The size of ...
Chapter 4
1. Figure 4.1 CCN router components.
2. Figure 4.2 Tunnel-based redirection (TBR) scheme.
Chapter 5
1. Figure 5.1 Fog computing enabled smart cities. (See color plate section for ...
2. Figure 5.2 A generic fog enabled IoT environment. (See color plate section f...
3. Figure 5.3 Internet of Things security phenomenon.
4. Figure 5.4 Layered depiction of components of trust.
5. Figure 5.5 Detailed taxonomy of threats.
6. Figure 5.6 Semantic web technology stack by Tim Berners-Lee 2000 (http://w3c...
7. Figure 5.7 With the appropriate sensors and wireless technology, several wir...
8. Figure 5.8 Authentication methods taxonomy.
9. Figure 5.9 Authorization frameworks and models.
10. Figure 5.10 Taxonomy of the frameworks and models used for privacy.
Chapter 6
1. Figure 6.1 Cloud-fog-IoT architecture. (See color plate section for the colo...
2. Figure 6.2 Our proposed architecture for cloud-fog-IoT integration.
3. Figure 6.3 Taxonomy for the classification of the communication layer.
4. Figure 6.4 Taxonomy for the classification of the security and privacy layer...
5. Figure 6.5 Taxonomy for the classification of the Internet of Things layer....
6. Figure 6.6 Taxonomy for the classification of the data quality layer.
7. Figure 6.7 Taxonomy for the classification of the cloudification layer.
8. Figure 6.8 Taxonomy for the classification of the analytics and decision-mak...
Chapter 7
1. Figure 7.1 The Computing Continuum: Cyberinfrastructure that spans every sca...
2. Figure 7.2 Continuum Computing Research Areas: A pictorial depiction of the ...
3. Figure 7.3 Continuum mapping and execution: Research is needed to explore te...
Chapter 8
1. Figure 8.1 Fog system.
2. Figure 8.2 Energy receiver.
3. Figure 8.3 Time slot.
Chapter 9
1. Figure 9.1 PPG sensors consisting of two light sources and one light sensor....
2. Figure 9.2 Power spectral density (PSD) of one-minute PPG signal.
3. Figure 9.3 PPG waveforms and the four features extracted for SpO₂ calculatio...
4. Figure 9.4 IoT system architecture.
5. Figure 9.5 The high level system architecture.
6. Figure 9.6 Modeling accuracy of measurements (e.g. ɛ(X, U)) in PP...
7. Figure 9.7 Optimization algorithm implementation.
8. Figure 9.8 Markov chain of battery states during charging and discharging.
9. Figure 9.9 Markov chain of activities of an individual during one period.
10. Figure 9.10 Markov chain of joint battery and activity states during period ...
11. Figure 9.11 24-hour health monitoring of a healthy person. (a) User's activi...
12. Figure 9.12 Average probability of error as a function of energy consumption...
Chapter 10
1. Figure 10.1 An illustration of a typical fog network.
2. Figure 10.2 An illustration of the problem's challenges.
3. Figure 10.3 An illustration of the min-cost transformation.
4. Figure 10.4 An illustration of latency updating procedure: (a) demand collec...
5. Figure 10.5 The dynamic programming in the line topology.
6. Figure 10.6 An illustration of the greedy algorithm: (a) greedy, (b) optimal...
7. Figure 10.7 Server distribution.
8. Figure 10.8 Latency-distance mapping: (a) greedy, (b) optimal.
9. Figure 10.9 Performance comparison without data replication.
10. Figure 10.10 Performance comparison with data replication.
Chapter 11
1. Figure 11.1 A comparison of different simulators for fog computing environme...
2. Figure 11.2 FogNetSim++ high-level architecture.
3. Figure 11.3 FogNetSim++: Graphical user interface, showing static, mobile, a...
4. Figure 11.4 FogNetSim++: showing the handover features managed through singl...
5. Figure 11.5 FogNetSim++: Internal structure of broker node.
6. Figure 11.6 GUI – Sample fog simulation.
Chapter 12
1. Figure 12.1 Three-tier architecture for IoT (a) and two-tier cloud assisted ...
2. Figure 12.2 IoT Data Challenges in three dimensions: generation, transmissio...
3. Figure 12.3 A multilayer feed-forward neural network.
4. Figure 12.4 Traditional machine learning (a) and deep learning (b) approache...
5. Figure 12.5 Typical CNN architecture [25].
6. Figure 12.6 OpenMV, a machine vision kit for IoT developers.
7. Figure 12.7 NCS based on Intel Movidius Myriad 2 Vision Processing Unit (VPU...
8. Figure 12.8 Fog topology with devices grouped by levels.
9. Figure 12.9 Multilevel data fusion for video processing.
Chapter 13
1. Figure 13.1 Comparison between the Smart Grid and the traditional grid.
2. Figure 13.2 Time triggering of the major functions used in DMS.
3. Figure 13.3 Feeder-based communication scheme for DMS using fog/cloud comput...
4. Figure 13.4 Simplified communication scheme connecting MATLAB/Simulink and T...
5. Figure 13.5 Distribution feeder topology with ThingSpeak channel assignments...
6. Figure 13.6 Simulation test 1 – Three-phase tripping of network branch 571–6...
7. Figure 13.7 Three-phase voltage profile of distribution feeder downstream fr...
8. Figure 13.8 Simulation test 2 – “Heavy” loading of the distribution feeder, ...
9. Figure 13.9 Three-phase voltage profile of distribution feeder downstream fr...
Chapter 14
1. Figure 14.1 Our proposed three-tier IoT system architecture.
2. Figure 14.2 Line chat with average value markers of different CCR values.
3. Figure 14.3 100% stack column chat of different CCR values.
Chapter 15
1. Figure 15.1 The process of developing location-independent MET estimation mo...
2. Figure 15.2 The result of the cross-correlation function on magnitude (a) an...
3. Figure 15.3 Three smart phones are placed on three different locations of ea...
4. Figure 15.4 Performance comparison of the proposed transfer learning approac...
5. Figure 15.5 Performance comparison of the proposed transfer learning approac...
Chapter 16
1. Figure 16.1 Architecture of software-defined network (SDN). (See color plate...
2. Figure 16.2 An OpenFlow switch communicate with controller over a secure con...
3. Figure 16.3 Architecture of SDN-based wireless mesh network.
4. Figure 16.4 Architecture of SDN-enabled wireless sensor network.
Chapter 17
1. Figure 17.1 Fog computing for vehicular applications.
2. Figure 17.2 Obstacle detection as an example of delay-critical application s...
3. Figure 17.3 Timeliness perturbations.
4. Figure 17.4 Coping with perturbation in DCVF.
Chapter 18
1. Figure 18.1 Cloud based architecture for the communication between autonomou...
2. Figure 18.2 Proposed fog-based architecture for the communication among auto...
3. Figure 18.3 Realistic and practical view of the proposed fog-based architect...
4. Figure 18.4 Box plot for the latency for both fog-basedarchitecture and clou...
5. Figure 18.5 Box plot for network usage for both fog-based architecture and c...
Chapter 19
1. Figure 19.1 Illustrative example of a visual data computing at network edges...
2. Figure 19.2 Illustrative example of the function-centric fog/cloud computing...
3. Figure 19.3 Illustrative example of the Panacea's Cloud setup: IoT device da...
4. Figure 19.4 Overview of visual data processing stages in a facial recognitio...
5. Figure 19.5 Illustrative example of a 3-D scene reconstruction with use of L...
6. Figure 19.6 Overview of 3-D scene reconstruction stages with 2-D videos and ...
7. Figure 19.7 Illustrative example of WAMI imagery ecosystem: tiled (TIFF) aer...
8. Figure 19.8 Overview of object tracking stages in a typical WAMI analysis pi...
9. Figure 19.9 Various physical obstacles including both man-made e.g. building...
10. Figure 19.10 Joplin, MO satellite maps of Joplin Hospital (a, b) and Joplin ...
11. Figure 19.11 To cope with deep learning functions complexity of the obstacle...
12. Figure 19.12 Illustrative example of the augmented with metalinks physical n...
13. Figure 19.13 System architecture of our Incident-Supporting Service Chain Or...
Chapter 20
1. Figure 20.1 Evolution of wireless technologies.
2. Figure 20.2 5G cellular architecture.
3. Figure 20.3 5G IP-based architecture.
4. Figure 20.4 Cloud-based architecture.
5. Figure 20.5 Beam division multiple access (BDMA).
6. Figure 20.6 Mixed bandwidth data path.
7. Figure 20.7 Cellular network with the deployment of massive MIMO.
8. Figure 20.8 Heterogeneous network.
Chapter 21
1. Figure 21.1 Fog computing architecture tailored for bioinformatics sequencin...
2. Figure 21.2 Fog computing architecture for bioinformatics applications.
3. Figure 21.3 Fog computing for real time microorganism detection.

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List of Contributors

Assad Abbas
COMSATS University Islamabad
Islamabad Campus
Pakistan

Mansoor Ahmed
COMSATS University Islamabad
Islamabad Campus
Pakistan

Isam Mashhour Al Jawarneh
Department of Computer Science and Engineering
University of Bologna
Italy

Imran Ali Khan
COMSATS University Islamabad
Abbottabad Campus
Pakistan

Mazhar Ali
COMSATS University Islamabad
Abbottabad Campus
Pakistan

Ahmad Ali
COMSATS University Islamabad
Islamabad Campus
Pakistan

Parastoo Alinia
Washington State University
Pullman, WA
United States

Delaram Amiri
Department of Electrical Engineering and Computer Science
University of California–Irvine
United States

Arman Anzanpour
Department of Future Technologies
University of Turku
Finland

Cosmin Avasalcai
Vienna University of Technology
Vienna

Kamran Sattar Awaisi
COMSATS University Islamabad
Islamabad Campus
Pakistan

Iman Azimi
Department of Future Technologies
University of Turku
Finland

Thais V. Batista
Federal University of Rio Grande do Norte
Brazil

Micah Beck
University of Tennessee
Knoxville, TN
United States

Pete Beckman
Argonne National Laboratory
Lamont, IL
United States

Paolo Bellavista
Department of Computer Science and Engineering
University of Bologna
Italy

Javier Berrocal
Department of Computer and Telematics Systems Engineering
University of Extremadura, Cáceres
Spain

Prasad Calyam
Department of Electrical Engineering and Computer Science
University of Missouri–Columbia
United States

Chii Chang
School of Computing and Information Systems
University of Melbourne
Australia

Ahmed Chebaane
Landshut University of Applied Sciences
Landshut
Germany

Dmitrii Chemodanov
Department of Electrical Engineering and Computer Science
University of Missouri–Columbia
United States

Antonio Corradi
Department of Computer Science and Engineering
University of Bologna
Italy

Sajal K. Das
Department of Computer Science
Missouri University of Science and Technology
United States

Flavia C. Delicato
Federal University of Rio de Janeiro
Brazil

Panagiotis D. Diamantoulakis
Electrical and Computer Engineering Department
Aristotle University of Thessaloniki
Thessaloniki
Greece

Jack Dongarra
University of Tennessee
Knoxville, TN
United States
and
Oak Ridge National Laboratory
Oakridge, TN
United States

Schahram Dustdar
Vienna University of Technology
Vienna

Nikil Dutt
School of Information and Computer Sciences
University of California–Irvine
United States

Biyi Fang
Michigan State University
East Lansing, MI
United States

Nicola Ferrier
Argonne National Laboratory
Lamont, IL
United States

Miodrag Forcan
Faculty of Electrical Engineering
University of East Sarajevo
East Sarajevo
Bosnia and Herzegovina

Luca Foschini
Department of Computer Science and Engineering
University of Bologna
Italy

Geoffrey Fox
Indiana University
Bloomington, IN
United States

Hassan Ghasemzadeh
Washington State University
Pullman, WA
United States

Usman Habib
National University of Computer and Emerging Sciences
Peshawar
Pakistan

Amnir Hadachi
Institute of Computer Science
University of Tartu
Estonia

Muhammad Imran
COMSATS University Islamabad
Islamabad Campus
Pakistan

George K. Karagiannidis
Electrical and Computer Engineering Department
Aristotle University of Thessaloniki
Thessaloniki
Greece

Osman Khalid
COMSATS University Islamabad
Abbottabad Campus
Pakistan

Muhammad Usman Shahid Khan
COMSATS University Islamabad
Abbottabad Campus
Pakistan

Asad Khan
National University of Computer and Emerging Sciences
Peshawar
Pakistan

Muazzam A. Khan
National University of Science and Technology (NUST)
Pakistan

Samee U. Khan
North Dakota State University
United States

Hasan Ali Khattak
COMSATS University Islamabad
Islamabad Campus
Pakistan

Abdelmajid Khelil
Landshut University of Applied Sciences
Landshut
Germany

Nicholas D. Lane
Oxford University
UK

Marco Levorato
School of Information and Computer Sciences
University of California–Irvine
United States

Pasi Liljeberg
Department of Future Technologies
University of Turku
Finland

Mirjana Maksimović
Faculty of Electrical Engineering
University of East Sarajevo
East Sarajevo
Bosnia and Herzegovina

Asad Waqar Malik
National University of Science and Technology (NUST), Pakistan
Department of Information System
Faculty of Computer Science & Information Technology
University of Malaya
Malaysia

Jakob Mass
Institute of Computer Science
University of Tartu
Estonia

Diomidis S. Michalopoulos
Nokia Bell Labs
Munich
Germany

Terry Moore
University of Tennessee
Knoxville, TN
United States

Shuja Mughal
COMSATS University Islamabad
Islamabad Campus
Pakistan

Ilir Murturi
Vienna University of Technology
Vienna

Kannappan Palaniappan
Department of Electrical Engineering and Computer Science
University of Missouri–Columbia
United States

Paulo F. Pires
Federal University of Rio de Janeiro
Brazil

Tariq Qayyum
National University of Science and Technology (NUST), Pakistan

Amir M. Rahmani
School of Information and Computer Sciences, and School of Nursing
University of California–Irvine
United States

Rao Naveed Bin Rais
College of Engineering and Information Technology
Ajman University
Ajman
UAE

Dan Reed
University of Utah
Salt Lake City, UT
United States

Aluizio F. Rocha Neto
Federal University of Rio Grande do Norte
Brazil

Yuanchao Shu
Microsoft Research
Redmond, WA
United States

Satish Narayana Srirama
Institute of Computer Science
University of Tartu
Estonia

Neeraj Suri
TU Darmstadt
Darmstadt
Germany

Stergios A. Tegos
Electrical and Computer Engineering Department
Aristotle University of Thessaloniki
Thessaloniki
Greece

Inayat ur Rehman
COMSATS University Islamabad
Islamabad Campus
Pakistan

Hafeez Ur Rehman
National University of Computer and Emerging Sciences
Peshawar
Pakistan

Ling Wang
Department of Automation
Tsinghua University
Beijing
China

Ning Wang
Department of Computer Science
Rowan University
Glassboro, NJ
United States

Jie Wu
Center for Networked Computing
Temple University
Philadelphia, PA
United States

Chu-ge Wu
Department of Automation
Tsinghua University
Beijing
China

Hui Xu
AInnovation
Beijing
China

Shen Yan
Michigan State University
East Lansing, MI
United States

Alessandro Zanni
Department of Computer Science and Engineering
University of Bologna
Italy

Xiao Zeng
Michigan State University
East Lansing, MI
United States

Faen Zhang
AInnovation
Beijing
China

Mi Zhang
Michigan State University
East Lansing, MI
United States

ADAS	advanced driving assistance systems
AAL	ambient assisted living
APIs	application programming interfaces
AI	artificial intelligence
AR	augmented reality
BTS	base transceiver station
CSA	Channel Switch Announcement
CloVR	Cloud Virtual Resource
CoAP	Constrained Application Protocol
CaaS	context as a service
CAPWAP	Control and Provisioning of Wireless Access Point
CNN	Convolutional Neural Network
DNNs	Deep Neural Networks
DTN	delay tolerant network
DBF	distance-based forwarding
edgeOS	edge operating system
ETSI	European Telecommunications Standards Institute
WSN	wireless sensor network
E2C	Elastic Compute Cloud
FLOPs	floating-point operations
FCW	forward collision warning
GRA	Grey relational analysis
HMM	Hidden Markov model
HWMP	Hybrid Wireless Mesh Protocol
IaaS	infrastructure as a service
iFog	indie fog
ITU	International Telecommunication Union
IoD	Internet of drones
IoMaT	Internet of marine things
IoMT	Internet of medical things
IoT	Internet of Things
IoVs	Internet of vehicles
ISP	Internet service provider
IFT	Iterative Feature Transformation
LV-Fog	land vehicular fog computing
LoRa	long range
LTE	long-term evolution
LTE advanced	long-term evolution-advanced
LPWANs	low-power wide-area networks
Marine Fog	marine fog computing
MDP	Markov decision process
MQTT	Message Queuing Telemetry Transport
MANET	mobile ad hoc network
MFC	mobile fog computing
MVCs	mobile vehicular cloudlets
MCS	monitoring and control server
MAC	multiple access control
NIST	National Institute of Standards and Techology
NFC	near-field communication
NCS	Neural Compute Stick
NOMA	nonorthogonal multiple access
ONF	Open Network Foundation
QoE	quality of experience
RFID	radio-frequency identification
RSUs	roadside units
RoT	roots of trust
SLA	service level agreement
S3	Simple Storage Service
SNP	single nucleotide polymorphism
SNS	social network services
SaaS	software as a service
SDN	software-defined network/ing
STORMSeq	Scalable Tools for Open-Source Read Mapping
SOC	system-on-chip
TOSCA	Topology and Orchestration Specification for Cloud Applications
UAVs	unmanned aerial vehicles
UAV-Fog	unmanned aerial vehicular fog computing
UE	user equipment
UE-fog	user equipment-based fog computing
VAT	Variant Annotation Tool
VFC	vehicular fog computing
V2D	vehicle-to-device
V2V	vehicle-to-vehicle
VHF	very high frequency
VM	virtual machine
WAVEs	wireless access in vehicular environments
WPAN	wireless personal area network
WSNs	wireless sensor networks
WiMAX	Worldwide Interoperability for Microwave Access

WILEY SERIES ON PARALLEL AND DISTRIBUTED COMPUTING

Fog Computing: Theory and Practice

List of Contributors

Acronyms

Part I
Fog Computing Systems and Architectures