Fog Computing E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Wiley Series on Parallel and Distributed Computing
• Sprache: Englisch

Seitenzahl: 1015

Veröffentlichungsjahr: 2020

Beschreibung

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.

Leseprobe

Table of Contents

Cover

List of Contributors

Acronyms

Part I: Fog Computing Systems and Architectures

1 Mobile Fog Computing

1.1 Introduction

1.2 Mobile Fog Computing and Related Models

1.3 The Needs of Mobile Fog Computing

1.4 Communication Technologies

1.5 Nonfunctional Requirements

1.6 Open Challenges

1.7 Conclusion

Acknowledgment

References

2 Edge and Fog: A Survey, Use Cases, and Future Challenges

2.1 Introduction

2.2 Edge Computing

2.3 Fog Computing

2.4 Fog and Edge Illustrative Use Cases

2.5 Future Challenges

2.6 Conclusion

Acknowledgment

References

3 Deep Learning in the Era of Edge Computing: Challenges and Opportunities

3.1 Introduction

3.2 Challenges and Opportunities

3.3 Concluding Remarks

References

4 Caching, Security, and Mobility in Content-centric Networking

4.1 Introduction

4.2 Caching and Fog Computing

4.3 Mobility Management in CCN

4.4 Security in Content-centric Networks

4.5 Caching

4.6 Conclusions

References

5 Security and Privacy Issues in Fog Computing

5.1 Introduction

5.2 Trust in IoT

5.3 Authentication

5.4 Authorization

5.5 Privacy

5.6 Web Semantics and Trust Management for Fog Computing

5.7 Discussion

5.8 Conclusion

References

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

6.3 Fog Computing: Architectural Model

6.4 Fog Computing for IoT: A Taxonomy

6.5 Comparisons of Surveyed Solutions

6.6 Challenges and Recommended Research Directions

6.7 Concluding Remarks

References

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

7.4 Summary

References

8 Fog Computing for Energy Harvesting-enabled Internet of Things

8.1 Introduction

8.2 System Model

8.3 Tradeoffs in EH Fog Systems

8.4 Future Research Challenges

Acknowledgment

References

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

9.6 Fog-assisted Runtime Energy Management in Wearable Sensors

9.7 Conclusions

Acknowledgment

References

10 Latency Minimization Through Optimal Data Placement in Fog Networks

10.1 Introduction

10.2 Related Work

10.3 Problem Statement

10.4 Delay Minimization Without Replication

10.5 Delay Minimization with Replication

10.6 Performance Evaluation

10.7 Conclusion

Acknowledgement

References

11 Modeling and Simulation of Distributed Fog Environment Using FogNetSim++

11.1 Introduction

11.2 Modeling and Simulation

11.3 FogNetSim++: Architecture

11.4 FogNetSim++: Installation and Environment Setup

11.5 Conclusion

References

Part II: Fog Computing Techniques and Applications

12 Distributed Machine Learning for IoT Applications in the Fog

12.1 Introduction

12.2 Challenges in Data Processing for IoT

12.3 Computational Intelligence and Fog Computing

12.4 Challenges for Running Machine Learning on Fog Devices

12.5 Approaches to Distribute Intelligence on Fog Devices

12.6 Final Remarks

Acknowledgments

References

13 Fog Computing-Based Communication Systems for Modern Smart Grids

13.1 Introduction

13.2 An Overview of Communication Technologies in Smart Grid

13.3 Distribution Management System (DMS) Based on Fog/Cloud Computing

13.4 Real-time Simulation of the Proposed Feeder-based Communication Scheme Using MATLAB and ThingSpeak

13.5 Conclusion

References

14 An Estimation of Distribution Algorithm to Optimize the Utility of Task Scheduling Under Fog Computing Systems

14.1 Introduction

14.2 Estimation of Distribution Algorithm

14.3 Related Work

14.4 Problem Statement

14.5 Details of Proposed Algorithm

14.6 Simulation

14.7 Conclusion

References

15 Reliable and Power-Efficient Machine Learning in Wearable Sensors

15.1 Introduction

15.2 Preliminaries and Related Work

15.3 System Architecture and Methods

15.4 Data Collection and Experimental Procedures

15.5 Results

15.6 Discussion and Future Work

15.7 Summary

References

16 Insights into Software-Defined Networking and Applications in Fog Computing

16.1 Introduction

16.2 OpenFlow Protocol

16.3 SDN-Based Research Works

16.4 SDN in Fog Computing

16.5 SDN in Wireless Mesh Networks

16.6 SDN in Wireless Sensor Networks

16.7 Conclusion

References

17 Time-Critical Fog Computing for Vehicular Networks

17.1 Introduction

17.2 Applications and Timeliness Guarantees and Perturbations

17.3 Coping with Perturbation to Meet Timeliness Guarantees

17.4 Research Gaps and Future Research Directions

17.5 Conclusion

References

18 A Reliable and Efficient Fog-Based Architecture for Autonomous Vehicular Networks

18.1 Introduction

18.2 Proposed Methodology

18.3 Hypothesis Formulation

18.4 Simulation Design

18.5 Conclusions

References

19 Fog Computing to Enable Geospatial Video Analytics for Disaster-incident Situational Awareness

19.1 Introduction

19.2 Computer Vision Application Case Studies and FCC Motivation

19.3 Geospatial Video Analytics Data Collection Using Edge Routing

19.4 Fog/Cloud Data Processing for Geospatial Video Analytics Consumption

19.5 Concluding Remarks

References

20 An Insight into 5G Networks with Fog Computing

20.1 Introduction

20.2 Vision of 5G

20.3 Fog Computing with 5G Networks

20.4 Architecture of 5G

20.5 Technology and Methodology for 5G

20.6 Applications

20.7 Challenges

20.8 Conclusion

References

21 Fog Computing for Bioinformatics Applications

21.1 Introduction

21.2 Cloud Computing

21.3 Cloud Computing Applications in Bioinformatics

21.4 Fog Computing

21.5 Fog Computing for Bioinformatics Applications

21.6 Conclusion

References

Index

End User License Agreement

List of Tables

Chapter 2

Table 2.1 Threat model for fog and edge computing [21].

Chapter 3

Table 3.1 Memory and computational expensiveness of some of the most commonly...

Chapter 4

Table 4.1 Caching schemes comparison.

Table 4.2 Objectives-based comparison.

Chapter 5

Table 5.1 State of the art research work timeline.

Table 5.2 Authentication grid.

Table 5.3 Authorization requirements in the Internet of Things.

Table 5.4 Privacy requirements in the light of Internet of Things.

Chapter 6

Table 6.1 Comparison between surveyed communication approaches.

Table 6.2 Comparison between surveyed security and privacy approaches.

Table 6.3 Comparison of the surveyed solution related to the Internet of Thin...

Table 6.4 Comparison of the surveyed solution related to the data quality lay...

Table 6.5 Comparison of the surveyed solution related to the cloudification l...

Table 6.6 Comparison of the surveyed solution related to the analytics and de...

Chapter 7

Table 7.1 Exemplar continuum computing science applications.

Table 7.2 Twister2 components and status.

Chapter 10

Table 10.1 Summary of symbols.

Chapter 12

Table 12.1 Comparison of machine learningtechnologies [14].

Table 12.2 Execution time (ms) for running DL models in three hardware platfo...

Table 12.3 Classification of smart IoT devices according to their capacities ...

Chapter 13

Table 13.1 Comparison of communication technologies for the SG [10–13].

Chapter 14

Table 14.1 Simulation environmental parameters.

Table 14.2 Comparison results of utility between heuristic and uEDA.

Chapter 15

Table 15.1 Extracted features from sensor signals.

Table 15.2 Energy consumption of various configurations, where computation is...

Table 15.3 Accuracy of sensor localization.

Table 15.4 The comparison between the accuracy of the regression on different...

Table 15.5 Leave-one-subject-out cross validation test.

Table 15.6

R

2

values from linear regression on MET vs Ankle and Hip Accelerometer...

Table 15.7 Comparing

R

2

values and error of linear regression on MET vs Ankle ...

Chapter 16

Table 16.1 Comparison between centralized and distributed controller.

Chapter 17

Table 17.1 Benchmarking of application classes.

Chapter 18

Table 18.1 Results of the latency in the both architectures.

Table 18.2 Log-transformed data for latency.

Table 18.3 Results of the network usage in the both architectures.

Table 18.4 Log-transformed data for network usage.

Chapter 20

Table 20.1 Comparison of wireless technologies.

Table 20.2 5G protocol stack.

Table 20.3 Challenges of 5G.

Table 20.4 Research projects on 5G.

Chapter 21

Table 21.1 A summary of existing cloud computing based major bioinformatics a...

Table 21.2 Comparison between the cloud computing and fog computing paradigms...

List of Illustrations

Chapter 1

Figure 1.1 Land-vehicular fog computing examples. (

See color plate section f

...

Figure 1.2 Maritime fog computing examples.

Figure 1.3 UAV fog computing examples.

Figure 1.4 UE fog computing examples. (

See color plate section for the color

...

Figure 1.5 A taxonomy of non-functional requirements of mobile fog computing...

Figure 1.6 Fog infrastructure service provider, fog service tenant, and tena...

Figure 1.7 The three types of end-to-end networking.

Chapter 2

Figure 2.1 Edge computing solution using an IoT and edge devices [12].

Figure 2.2 An overview of edge computing architecture [16]. (

See color plate

...

Figure 2.3 Fog computing a bridge between cloud and edge [20].

Figure 2.4 Fog computing architecture [10]. (

See color plate section for the

...

Figure 2.5 A wearable ECG sensor.

Figure 2.6 Structure of edgeOS in the smart home environment [3].

Figure 2.7 Smart Traffic light system.

Figure 2.8 Smart pipeline monitoring system architecture.

Chapter 3

Figure 3.1 Illustration of differences between training and test images of t...

Figure 3.2 Illustration of data sharing mechanism.

Figure 3.3 Illustration of intermediate results of a DNN model. The size of ...

Chapter 4

Figure 4.1 CCN router components.

Figure 4.2 Tunnel-based redirection (TBR) scheme.

Chapter 5

Figure 5.1 Fog computing enabled smart cities. (

See color plate section for

...

Figure 5.2 A generic fog enabled IoT environment. (

See color plate section f

...

Figure 5.3 Internet of Things security phenomenon.

Figure 5.4 Layered depiction of components of trust.

Figure 5.5 Detailed taxonomy of threats.

Figure 5.6 Semantic web technology stack by Tim Berners-Lee 2000 (http://w3c...

Figure 5.7 With the appropriate sensors and wireless technology, several wir...

Figure 5.8 Authentication methods taxonomy.

Figure 5.9 Authorization frameworks and models.

Figure 5.10 Taxonomy of the frameworks and models used for privacy.

Chapter 6

Figure 6.1 Cloud-fog-IoT architecture. (

See color plate section for the colo

...

Figure 6.2 Our proposed architecture for cloud-fog-IoT integration.

Figure 6.3 Taxonomy for the classification of the communication layer.

Figure 6.4 Taxonomy for the classification of the security and privacy layer...

Figure 6.5 Taxonomy for the classification of the Internet of Things layer....

Figure 6.6 Taxonomy for the classification of the data quality layer.

Figure 6.7 Taxonomy for the classification of the cloudification layer.

Figure 6.8 Taxonomy for the classification of the analytics and decision-mak...

Chapter 7

Figure 7.1 The Computing Continuum: Cyberinfrastructure that spans every sca...

Figure 7.2 Continuum Computing Research Areas: A pictorial depiction of the ...

Figure 7.3 Continuum mapping and execution: Research is needed to explore te...

Chapter 8

Figure 8.1 Fog system.

Figure 8.2 Energy receiver.

Figure 8.3 Time slot.

Chapter 9

Figure 9.1 PPG sensors consisting of two light sources and one light sensor....

Figure 9.2 Power spectral density (PSD) of one-minute PPG signal.

Figure 9.3 PPG waveforms and the four features extracted for SpO

2

calculatio...

Figure 9.4 IoT system architecture.

Figure 9.5 The high level system architecture.

Figure 9.6 Modeling accuracy of measurements (e.g.

ɛ(

X

, 

U

)

) in PP...

Figure 9.7 Optimization algorithm implementation.

Figure 9.8 Markov chain of battery states during charging and discharging.

Figure 9.9 Markov chain of activities of an individual during one period.

Figure 9.10 Markov chain of joint battery and activity states during period ...

Figure 9.11 24-hour health monitoring of a healthy person. (a) User's activi...

Figure 9.12 Average probability of error as a function of energy consumption...

Chapter 10

Figure 10.1 An illustration of a typical fog network.

Figure 10.2 An illustration of the problem's challenges.

Figure 10.3 An illustration of the min-cost transformation.

Figure 10.4 An illustration of latency updating procedure: (a) demand collec...

Figure 10.5 The dynamic programming in the line topology.

Figure 10.6 An illustration of the greedy algorithm: (a) greedy, (b) optimal...

Figure 10.7 Server distribution.

Figure 10.8 Latency-distance mapping: (a) greedy, (b) optimal.

Figure 10.9 Performance comparison without data replication.

Figure 10.10 Performance comparison with data replication.

Chapter 11

Figure 11.1 A comparison of different simulators for fog computing environme...

Figure 11.2 FogNetSim++ high-level architecture.

Figure 11.3 FogNetSim++: Graphical user interface, showing static, mobile, a...

Figure 11.4 FogNetSim++: showing the handover features managed through singl...

Figure 11.5 FogNetSim++: Internal structure of broker node.

Figure 11.6 GUI – Sample fog simulation.

Chapter 12

Figure 12.1 Three-tier architecture for IoT (a) and two-tier cloud assisted ...

Figure 12.2 IoT Data Challenges in three dimensions: generation, transmissio...

Figure 12.3 A multilayer feed-forward neural network.

Figure 12.4 Traditional machine learning (a) and deep learning (b) approache...

Figure 12.5 Typical CNN architecture [25].

Figure 12.6 OpenMV, a machine vision kit for IoT developers.

Figure 12.7 NCS based on Intel Movidius Myriad 2 Vision Processing Unit (VPU...

Figure 12.8 Fog topology with devices grouped by levels.

Figure 12.9 Multilevel data fusion for video processing.

Chapter 13

Figure 13.1 Comparison between the Smart Grid and the traditional grid.

Figure 13.2 Time triggering of the major functions used in DMS.

Figure 13.3 Feeder-based communication scheme for DMS using fog/cloud comput...

Figure 13.4 Simplified communication scheme connecting MATLAB/Simulink and T...

Figure 13.5 Distribution feeder topology with ThingSpeak channel assignments...

Figure 13.6 Simulation test 1 – Three-phase tripping of network branch 571–6...

Figure 13.7 Three-phase voltage profile of distribution feeder downstream fr...

Figure 13.8 Simulation test 2 – “Heavy” loading of the distribution feeder, ...

Figure 13.9 Three-phase voltage profile of distribution feeder downstream fr...

Chapter 14

Figure 14.1 Our proposed three-tier IoT system architecture.

Figure 14.2 Line chat with average value markers of different

CCR

values.

Figure 14.3 100% stack column chat of different

CCR

values.

Chapter 15

Figure 15.1 The process of developing location-independent MET estimation mo...

Figure 15.2 The result of the cross-correlation function on magnitude (a) an...

Figure 15.3 Three smart phones are placed on three different locations of ea...

Figure 15.4 Performance comparison of the proposed transfer learning approac...

Figure 15.5 Performance comparison of the proposed transfer learning approac...

Chapter 16

Figure 16.1 Architecture of software-defined network (SDN). (

See color plate

...

Figure 16.2 An OpenFlow switch communicate with controller over a secure con...

Figure 16.3 Architecture of SDN-based wireless mesh network.

Figure 16.4 Architecture of SDN-enabled wireless sensor network.

Chapter 17

Figure 17.1 Fog computing for vehicular applications.

Figure 17.2 Obstacle detection as an example of delay-critical application s...

Figure 17.3 Timeliness perturbations.

Figure 17.4 Coping with perturbation in DCVF.

Chapter 18

Figure 18.1 Cloud based architecture for the communication between autonomou...

Figure 18.2 Proposed fog-based architecture for the communication among auto...

Figure 18.3 Realistic and practical view of the proposed fog-based architect...

Figure 18.4 Box plot for the latency for both fog-basedarchitecture and clou...

Figure 18.5 Box plot for network usage for both fog-based architecture and c...

Chapter 19

Figure 19.1 Illustrative example of a visual data computing at network edges...

Figure 19.2 Illustrative example of the function-centric fog/cloud computing...

Figure 19.3 Illustrative example of the Panacea's Cloud setup: IoT device da...

Figure 19.4 Overview of visual data processing stages in a facial recognitio...

Figure 19.5 Illustrative example of a 3-D scene reconstruction with use of L...

Figure 19.6 Overview of 3-D scene reconstruction stages with 2-D videos and ...

Figure 19.7 Illustrative example of WAMI imagery ecosystem: tiled (TIFF) aer...

Figure 19.8 Overview of object tracking stages in a typical WAMI analysis pi...

Figure 19.9 Various physical obstacles including both man-made e.g. building...

Figure 19.10 Joplin, MO satellite maps of Joplin Hospital (a, b) and Joplin ...

Figure 19.11 To cope with deep learning functions complexity of the obstacle...

Figure 19.12 Illustrative example of the augmented with metalinks physical n...

Figure 19.13 System architecture of our Incident-Supporting Service Chain Or...

Chapter 20

Figure 20.1 Evolution of wireless technologies.

Figure 20.2 5G cellular architecture.

Figure 20.3 5G IP-based architecture.

Figure 20.4 Cloud-based architecture.

Figure 20.5 Beam division multiple access (BDMA).

Figure 20.6 Mixed bandwidth data path.

Figure 20.7 Cellular network with the deployment of massive MIMO.

Figure 20.8 Heterogeneous network.

Chapter 21

Figure 21.1 Fog computing architecture tailored for bioinformatics sequencin...

Figure 21.2 Fog computing architecture for bioinformatics applications.

Figure 21.3 Fog computing for real time microorganism detection.

Guide

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WILEY SERIES ON PARALLEL AND DISTRIBUTED COMPUTING

Series Editor: Albert Y. Zomaya

Parallel and Distributed Simulation Systems / Richard Fujimoto

Mobile Processing in Distributed and Open Environments / Peter Sapaty

Introduction to Parallel Algorithms / C. Xavier and S. S. Iyengar

Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences / Albert Y. Zomaya, Fikret Ercal, and Stephan Olariu (Editors)

Parallel and Distributed Computing: A Survey of Models, Paradigms, and Approaches / Claudia Leopold

Fundamentals of Distributed Object Systems: A CORBA Perspective / Zahir Tari and Omran Bukhres

Pipelined Processor Farms: Structured Design for Embedded Parallel Systems / Martin Fleury and Andrew Downton

Handbook of Wireless Networks and Mobile Computing / Ivan Stojmenović (Editor)

Internet-Based Workflow Management: Toward a Semantic Web / Dan C. Marinescu

Parallel Computing on Heterogeneous Networks / Alexey L. Lastovetsky

Performance Evaluation and Characterization of Parallel and Distributed Computing Tools / Salim Hariri and Manish Parashar

Distributed Computing: Fundamentals, Simulations and Advanced Topics, 2nd Edition / Hagit Attiya and Jennifer Welch

Smart Environments: Technology, Protocols, and Applications / Diane Cook and Sajal Das

Fundamentals of Computer Organization and Architecture / Mostafa Abd-El-Barr and Hesham El-Rewini

Advanced Computer Architecture and Parallel Processing / Hesham El-Rewini and Mostafa Abd-El-Barr

UPC: Distributed Shared Memory Programming / Tarek El-Ghazawi, William Carlson, Thomas Sterling, and Katherine Yelick

Handbook of Sensor Networks: Algorithms and Architectures / Ivan Stojmenović (Editor)

Parallel Metaheuristics: A New Class of Algorithms / Enrique Alba (Editor)

Design and Analysis of Distributed Algorithms / Nicola Santoro

Task Scheduling for Parallel Systems / Oliver Sinnen

Computing for Numerical Methods Using Visual C++ / Shaharuddin Salleh, Albert Y. Zomaya, and Sakhinah A. Bakar

Architecture-Independent Programming for Wireless Sensor Networks / Amol B. Bakshi and Viktor K. Prasanna

High-Performance Parallel Database Processing and Grid Databases / David Taniar, Clement Leung, Wenny Rahayu, and Sushant Goel

Algorithms and Protocols for Wireless and Mobile Ad Hoc Networks / Azzedine Boukerche (Editor)

Algorithms and Protocols for Wireless Sensor Networks / Azzedine Boukerche (Editor)

Optimization Techniques for Solving Complex Problems / Enrique Alba, Christian Blum, Pedro Isasi, Coromoto León, and Juan Antonio Gómez (Editors)

Emerging Wireless LANs, Wireless PANs, and Wireless MANs: IEEE 802.11, IEEE 802.15, IEEE 802.16 Wireless Standard Family / Yang Xiao and Yi Pan (Editors)

High-Performance Heterogeneous Computing / Alexey L. Lastovetsky and Jack Dongarra

Mobile Intelligence / Laurence T. Yang, Augustinus Borgy Waluyo, Jianhua Ma, Ling Tan, and Bala Srinivasan (Editors)

Advanced Computational Infrastructures for Parallel and Distributed Adaptive Applications / Manish Parashar and Xiaolin Li (Editors)

Market-Oriented Grid and Utility Computing / Rajkumar Buyya and Kris Bubendorfer (Editors)

Cloud Computing Principles and Paradigms / Rajkumar Buyya, James Broberg, and Andrzej Goscinski

Energy-Efficient Distributed Computing Systems / Albert Y. Zomaya and Young Choon Lee (Editors)

Fog Computing: Theory and Practice

Edited by

Assad Abbas

COMSATS University Islamabad, Pakistan

Samee U. Khan

North Dakota State University, USA

Albert Y. Zomaya

University of Sydney, Australia

This edition first published 2020.

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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

Acronyms

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

Part IFog Computing Systems and Architectures

1Mobile Fog Computing

Chii Chang, Amnir Hadachi, Jakob Mass, and Satish Narayana Srirama

Institute of Computer Science, University of Tartu, Estonia

1.1 Introduction

The Internet of Things (IoT) paradigm motivates various next-generation applications in the domains of smart home, smart city, smart agriculture, smart manufacturing, smart mobility, and so forth [1], where the online systems are capable of managing physical objects, such as home appliances, public facilities, farming equipment or production line machines via the Internet. Moreover, mobile objects, such as land vehicles (e.g. cars, trucks, buses, etc.), maritime transports (e.g. ships, boats, vessels, etc.), unmanned aerial vehicles (UAVs; e.g. drones), and user equipment (UE; e.g. smartphones, tablets, mobile Internet terminals, etc.), have become the indispensable elements in IoT to assist a broad range of mobile IoT applications.

Mobile IoT applications emphasize the connectivity and the interoperability among the IoT infrastructure and the mobile objects. For example, in an Internet of Vehicles (IoVs) application [2], the IoT-based smart traffic infrastructure provides the connected roadside units (RSUs) that assist the smart cars to exchange the current traffic situation of the city center toward reducing the chance of traffic accidents and issues. As another example, classic disaster recovery activities of a city require numerous manned operations to monitor the disaster conditions, which involve high risk for human workers. Conversely, by integrating an Internet of Drones (IoD) [3], the smart city government can dispatch a number of drones to monitor and to execute the tasks without sending human workers to the frontline. Unexceptionally, mobile IoT also has benefited maritime activities in terms of improving the information exchange among the vessels and the central maritime management system, hastening the overall process speed of fishery or marine scientific activities [4].

Besides the public applications, mobile IoT plays an important role in personal applications, such as Internet of Medical Things (IoMT) applications [5], which utilize both inbuilt sensors of the UE (e.g. smartphone) and the UE-connected body sensors attached on the patient to collect health-related data and forward the data to the central system of the hospital via the mobile Internet connection of the UE.

Explicitly, the mobile IoT applications described above are time-critical applications that require rapid responses. However, the classic IoT system architecture, which relies on the distant central management system to perform the decision making, has faced its limitation to achieve the timely response due to latency issues deriving from the dynamic network condition between the front-end IoT devices and the back-end central server. Furthermore, the large number of connected mobile IoT devices have raised the challenges of mobile Big Data [6] that increase the burden of the central server and hence, lead to bottleneck issues. In order to improve the agility and to achieve the goal of ultra-low latency, researchers have introduced fog computing architecture [1].

Fog computing architecture (the fog) distributes the tasks from the distant central management system in the cloud to the intermediate nodes (e.g. routers, switches, hubs, etc.), which contain computational resources, to reduce the latency caused by transmitting messages between the front-end IoT devices and the back-end cloud. Specifically, the fog provides five basic mechanisms: storage, compute, acceleration, networking, and control toward enhancing IoT systems in five subjects: security, cognition, agility, low latency, and efficiency [1]. For example, in IoV application, the central server can migrate the best route determination function from the cloud to the roadside fog nodes to assist the travel of the connected vehicles. As another example, in an outdoor-based IoMT application, the hospital system can distribute the health measurement function and the alarm function to the UE in order to perform timely determination of the patient's health condition and to perform an alarm to catch the proximal passengers' attention when the patient is having an incident.

Here, we use the term mobile fog computing (MFC) to describe the fog-assisted mobile IoT applications.

MFC brings numerous advantages to mobile IoT in terms of rapidness, ultra-low latency, substitutability and sustainability, efficiency, and self-awareness. However, the dynamic nature of MFC environment raises many challenges in terms of resource and network heterogeneity, the mobility of the participative entities, the cost of operation, and so forth. In general, the static fog computing frameworks designed for applications, such as the smart home or smart factory would not fully address the MFC-specific challenges because they have different perspectives from the involved entities and the topology. For example, a classic fog computing framework, which may involve a thin mobile client-side application for smartphone users, would not consider how to provide a reliable fog service to the high-speed moving vehicles. Moreover, the classic fog computing framework also would not consider how to provide a reliable fog service to vessels at sea where the telecommunication base stations are not available, and the satellite Internet is too expensive.

The goal of this chapter is to provide an introduction and guidance to MFC developments. Specifically, different from the existing works [7, 8] related to MFC, this chapter discusses MFC in four major application domains: land vehicular fog computing (LV-Fog), marine fog computing (Marine Fog), unmanned aerial vehicular fog computing (UAV-Fog), and user equipment-based fog computing (UE-fog).

The rest of the chapter is organized as follows: Section 1.2 clarifies the term MFC. Section 1.3 breaks MFC down into four application domains and describes their characteristics. Section 1.4 enlists the wireless communication technologies used in the mentioned application domains, while Section 1.5 proposes a taxonomy of nonfunctional requirements for MFC. Open research challenges, both domain-oriented and generic, are identified in Section 1.6, and finally, Section 1.7 concludes this chapter.

1.2 Mobile Fog Computing and Related Models

In this chapter, MFC has its specific definition and it is not an exchangeable terminology with the other similar terms, such as mobile cloud computing (MCC) or multi-access (mobile) edge computing (MEC). In order to clarify the meaning of MFC, one needs to understand the aspects of the parties who introduced or adapted the terminologies. Commonly, MCC refers to a system that assists mobile devices (e.g. smartphones) to offload their resource-intensive computational tasks to either distant cloud [7] or to the proximity-based cloudlet [8].

Fundamentally, MEC is an European Telecommunications Standards Institute (ETSI) standard aimed to introduce an open standard for telecommunication service providers to integrate and to provide infrastructure as a service (IaaS), platform as a service (PaaS), or software as a service (SaaS) cloud services from the industrial integrated routers or switches of their cellular base stations. Explicitly, MEC is an implementation approach rather than a software architecture model. Further, as stated by ETSI, ETSI and OpenFog are collaborating to enable the MEC standard and the OpenFog Reference Architecture standard (IEEE 1934) to complement each other [9].

Today, researchers of industry and academia have been broadly using edge computing as the exchangeable term with fog computing. However, National Institute of Standards and Technology (NIST) and the document of IEEE 1934 standard for fog computing reference architecture, which derives from OpenFog Consortium, have specifically explained the differences between fog computing and edge computing. Accordingly, “the Edge computing is the network layer encompassing the end-devices and their users, to provide, for example, local computing capability on a sensor, metering or some other devices that are network-accessible” [10]. Further, based on the literature in edge computing domain, which include cloudlet-based computing models [11], one can explicitly identify that edge computing is loosely a bottom-up model. Specifically, an edge computing-integrated system emphasizes task offloading from the end-devices to the nearby cloudlet resources, which are capable of providing Virtual Machine (VM) or containers engine (e.g. Docker1)-based service to the other nodes within the same subnet.

On the other hand, fog computing is a hierarchical top-down model in which the system specifically tackles the problem about how to utilize the intermediate networking nodes between the central cloud and the end-devices to improve the overall performance and efficiency. Commonly, such intermediate nodes are Internet gateways such as routers, switches, hubs (e.g. an adaptor that interconnects Bluetooth-based device to IP network). Moreover, a fog node is capable of providing five basic services – storage, compute, acceleration, networking, and control [1]. Correspondingly, when a cloudlet or an IoT device is providing gateway mechanism to the other nodes and they are capable of providing some or all of the basic fog services, we also consider them as fog nodes.

By extending the notion above, MFC is the subset of fog computing that addresses mobility-awareness. Specifically, MFC involves two types – infrastructural fog (iFog)-assisted mobile application and mobile fog node (mFog)-assisted application. In summary, iFog-assisted mobile application enhances the performance of a cloud-centric mobile application by migrating the processes from the central cloud to the stationary fog nodes (e.g. the cellular base station) that are currently connecting with the mobile IoT devices. On the other hand, mFog-assisted applications host fog services on mobile gateways (e.g. in-vehicle gateway devices or smartphones) that interconnect other devices (e.g. onboard sensors, body sensors, etc.) or other things (e.g. proximal cars, people, sensors, etc.) to the cloud.

1.3 The Needs of Mobile Fog Computing

MFC encompasses four application domains: land vehicular applications, marine applications, unmanned aerial vehicular applications, and UE-based applications. Specifically, each domain involves both iFog- and mFog-based architecture. Ideally, the approaches of iFog aim to provide generic solutions that are applicable to all the MFC domains where the infrastructure is applicable. On the other hand, mFog-based approaches aim to overcome the challenges in which the iFog is inapplicable or is unable to resolve effectively. To clarify the terminologies used in the rest of the chapter, iFog denotes infrastructural fog node and mFog represents the generic term of mobile fog nodes. Moreover, we further classify mFog to four types: LV-Fog, Marine Fog, UAV-Fog, and user equipment-based fog (UE-fog) corresponding to the mobile fog node hosted on a land vehicle, a vessel, a UAV, and a UE (e.g. smartphone, tablet, etc.).

1.3.1 Infrastructural Mobile Fog Computing

1.3.1.1 Road Crash Avoidance

The number of vehicles on the road is increasing every year as well as the number of road accidents [12]. In order to reduce or avoid the collision accident, academic and industrial researchers have been working on improving the safety aspect of the vehicles. Specifically, the advancement in communication technologies has allowed the development of advanced driver-assistance systems (ADAS), which has emerged as an active manner of preventing car crashes. ADAS has made many achievements through the development of systems that include rear-end collision avoidance and forward collision warning (FCW). The vehicle-to-vehicle (V2V) communication plays a big role in ADAS systems and it is manifested in ensuring that the controllers on board the vehicles (i.e. onboard unit, OBU) are capable of communicating with other vehicles for the purpose of negotiating maneuvers in the intersections and applying automatic control when it is necessary to avoid collisions [13]. The success of these systems relies a lot on the reliability of the communication. Therefore, many models of V2V communication have been investigated. Some of them focus on probabilities and analytic approaches in modeling the communication message reception while others adapt Markovian methods to assess the performance and reliability of the safety-critical data broadcasting in IEEE 802.11p vehicular network [14]. Vehicular ad-hoc network (VANET) has also contributed to integrate and improve the car-following model or platooning, which reduces the risks of collisions and makes the driving experience safer [15]. Explicitly, today's smart vehicular network systems have applied the fog computing mechanisms that utilize the cloud-connected OBUs of the vehicle to process the data from the onboard sensors toward exchanging context information in the vehicular network and participating in the intelligent transport systems.

1.3.1.2 Marine Data Acquisition

Today, marine data acquisition and cartography systems can achieve low-cost data acquisition and processing by composing IoT, mobile ad hoc network (MANET), and delay-tolerant networking (DTN) technologies. Specifically, sea vessels, which equip multiple sensors, can utilize International Telecommunication Union (ITU) standards-based very high frequency (VHF) data exchange system to route the sensory data to the gateway node (i.e. cellular base station) at the shore via the ship-based MANET. Afterwards, the gateway can relay the data to the central cloud. In general, such an architecture may produce many duplicated sensory transmission readings due to the redundant data transmitted from different ships. In order to remove such duplication and to improve the efficiency, the system can deploy fog computing service at the gateway nodes to preprocess the sensory data toward preventing the gateways sending duplicated data to the central server [4].

1.3.1.3 Forest Fire Detection

Emerged smart UAVs, which are relatively inexpensive and can be flexibly dispatched to a large area under different weather conditions, both during day and night, without human involvement are the ideal devices to handle forest fire detection and firefighting missions. Specifically, with onboard image detection mechanism and mobile Internet connectivity, UAVs can provide real-time event reporting to the distant central management system. Further, in order to extend the sustainability of the image-based sensing mission, the system can distribute the computational image detection program to the proximal iFog hosted on cellular base stations and made accessible via standard communication technologies, such as Long-Term Evolution (LTE), SigFox, NB-IoT, etc. Hence, the UAVs can use their battery power only for flying and sensing tasks [16] (Figure 1.1).

Figure 1.1 Land-vehicular fog computing examples. (See color plate section for the color representation of this figure)

1.3.1.4 Mobile Ambient Assisted Living

Today's UE devices, such as smartphones, have numerous inbuilt sensor components. For example, the modern mobile operating systems (e.g. Android OS) have provided numerous software components that are capable of integrating both internal and external sensors to support mobile Ambient Assisted Living (AAL) applications such as real-time health monitoring and observing the surrounding environments of the user to avoid dangers. Fundamentally, classic mobile AAL applications rely on the distant cloud to process the sensory data in order to identify situations. However, such an approach is often unable to provide rapid response due to communication latency issues. Therefore, utilizing proximal fog service derived either from the MEC-supported cellular base station or individual or small business-provided Indie Fog [17] has become an ideal solution to enhance the agility of mobile AAL applications [18].

1.3.2 Land Vehicular Fog

The development of vehicular networking has improved safety and control on the roads. Especially, LV-Fog nodes have emerged as a solution to introduce computational power and reliable connectivity to transportation systems at the level of Vehicle-to-Infrastructure (V2I), V2V, and Vehicle-to-Device (V2D) communications [19]. These networks are shaped around moving vehicles, pedestrians equipped with mobile devices, and road network infrastructure units. Further, these aspects have facilitated the introduction of real-time situational/context awareness by allowing the vehicle to collect or process data about their surroundings and share these insights with the central traffic control management units or other vehicles and devices in a cooperative manner.

To perform such activities, there is a need for adequate computing resources at the edge for performing time-critical and data-intensive jobs [20] and face all the challenges related to data collection and dissemination, data storage, mobility-influenced changing network structure, resource management, energy, and data analysis [21, 22].

Most of the techniques proposed to solve these challenges are focusing on merging the computation power between vehicular cloud and vehicular networks [19]. This combination allows usage of both vehicles' OBUs and RSUs as communication entities. Another side that has been investigated is the issues related to latency and quality optimization of the tasks in the Vehicular Fog Computing (VFC) [20], and it was formulated by presenting the task as a bi-objective minimization problem, where the trade-off is preserved between the latency and quality loss. Furthermore, handling the mobility complexity that massively affects the network structure is addressed by using mobility patterns of the moving vehicles and devices to perform a periodic load balancing in the fog servers [23] or distance-based forwarding (DBF) protocol [19]. The energy management and computational power for data analysis are controlled by distributing the load among the network entities to make use of all the available resources based on CR-based access protocol [22].

Moreover, the design of the Media Access Control (MAC) layer protocol in the vehicular networks is essential for improving the network performance, especially in V2V communication. V2V enables cooperative tasks among the vehicles and introduces cooperative communication, such as:

Dynamic fog service for next generation mobile applications

. The emergence of new mobile applications, such as

augmented reality

(

AR

) and virtual reality, have brought a new level of experience that is greedy for more computational power. However, the traditional approach of a distant cloud-driver is incapable of achieving with good performance due to latency. Therefore, introducing

Metropolitan vehicle-based cloudlet

, which is a form of mobile fog node model, solves the latency issue by dynamically placing the fog at the areas with high demand. Furthermore, by adopting a collaborative task offloading mechanism, the vehicle-based mobile fog nodes are capable of effectively distributing the processes across all the participative nodes, based on their encounter conditions [

24

].

Federated intelligent transportation

. Traffic jams start to have a considerable negative impact by wasting time, fuel, capital, and polluting the environment due to the nonstop increase in the number of vehicles on the roads [

25

]. Fortunately, cloud-driven smart vehicles have emerged as a facilitator to overcome the problem. The solution resides in considering the serviceability level of

mobile vehicular cloudlets

(MVCs), which are a form of the mobile fog node model, based on the real-world large-scale traces of mobility of urban vehicles collected by onboard computers. Based on the peer-to-peer communication network, vehicles can further improve the traffic experience by exchanging real-time information and providing assistance to the manned or unmanned vehicles [

26

].

Vehicular opportunistic computation offloading

. Public transportation service vehicles, such as buses and trams, which commonly have fixed routes and time schedules, can be the mobile fog nodes for the other mobile application devices inside the proximal encountered vehicles that need to execute time-sensitive and computation-intensive tasks, such as augmented reality (AR) processes used for the advanced driver assistance systems and applications [

27

].

1.3.3 Marine Fog

Integrating IoT to existing legacy marine systems can provide rapid information exchange. Initially, classic marine communication systems utilize VHF radio to obtain ship identification, ship location, position, destination, moving speed, and so forth. Unfortunately, VHF can provide only 9.6 kbps, which is insufficient to provide marine sensory data streaming [28]. Alternatively, ships can utilize the new satellite Internet to deliver their data, which is capable of achieving 432 kbps. However, satellite communication is not affordable for small and medium-size businesses since a simple voice service can cost USD $13.75 per minute [28]. In order to overcome the issue, researchers have introduced fog computing and networking-integrated marine communication systems for the Internet of marine t hings (IoMaT) [4]. Here, we term such a fog computing model Marine Fog.

By integrating a virtualization or containerization technology-based fog server with onboard equipment, vessels are capable of realizing a software-defined network (SDN) that allows the vessels to (re)configure a message routing path dynamically. Afterwards, by utilizing Marine Fog–based SDN mechanism, vessels can easily establish an ad hoc–based DTN, which caches data at the onboard Marine Fog node until the vessel encounters the next Marine Fog node, for delivering sensory data from the data source to the base stations toward relaying the data to the cloud. Moreover, an advanced Marine Fog node within the network may also perform data preprocessing in order to further reduce the transmission latency [29] (Figure 1.2).

Existing wireless sensor network (WSN) architecture in marine monitoring uses sea buoys as sink nodes, capable of communicating with nearby sensor nodes (other buoys, vessels) directly (e.g. using ZigBee), as well as via the cellular Internet network [30]. By introducing the previously mentioned virtualization, the WSN architecture could be extended to be used in Marine Fog. However, this approach amplifies the need for energy-harvesting technology at the buoys.

Figure 1.2 Maritime fog computing examples.

1.3.4 Unmanned Aerial Vehicular Fog

UAVs, which are also referred to as drones, can be employed in a