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E-raamat: Autonomous and Connected Heavy Vehicle Technology

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Autonomous and Connected Heavy Vehicle Technology presents the fundamentals, definitions, technologies, standards and future developments of autonomous and connected heavy vehicles. This book provides insights into various issues pertaining to heavy vehicle technology and helps users develop solutions towards autonomous, connected, cognitive solutions through the convergence of Big Data, IoT, cloud computing and cognition analysis. Various physical, cyber-physical and computational key points related to connected vehicles are covered, along with concepts such as edge computing, dynamic resource optimization, engineering process, methodology and future directions.

The book also contains a wide range of case studies that help to identify research problems and an analysis of the issues and synthesis solutions. This essential resource for graduate-level students from different engineering disciplines such as automotive and mechanical engineering, computer science, data science and business analytics combines both basic concepts and advanced level content from technical experts.

  • Covers state-of-the-art developments and research in vehicle sensor technology, vehicle communication technology, convergence with emerging technologies, and vehicle software and hardware integration
  • Addresses challenges such as optimization, real-time control systems for distance and steering mechanism, and cognitive and predictive analysis
  • Provides complete product development, commercial deployment, technological and performing costs and scaling needs
Contributors xix
Preface xxiii
SECTION 1 Review articles
Chapter 1 Lightweight and heavyweight technologies for autonomous vehicles: A survey
3(34)
Kriti Sharma
Usman Naeem
Rajalakshmi Krishnamurthi
Adarsh Kumar
1 Lightweight sensor technology for automated and connected heavy vehicles
3(19)
1.1 Lightweight and heavyweight sensors for vehicular technology
3(15)
1.2 Lightweight and heavyweight sensor quality and data handling issues
18(2)
1.3 Economic issues for automated technologies
20(1)
1.4 Regulation for lightweight and heavyweight sensors-based automated technologies
21(1)
1.5 Future scope and research challenges in lightweight and heavyweight technologies
21(1)
2 Lightweight and heavyweight road safety issues for automated vehicles
22(1)
3 Impact of heavy vehicle technologies with industry 4.0 standards
23(11)
3.1 Industry 4.0 technologies
23(1)
3.2 Heavy vehicle technology with artificial intelligence
24(1)
3.3 Heavy vehicle technology with cloud, fog, and edge computing
25(9)
4 Conclusion and future scope
34(3)
References
34(3)
Chapter 2 Cybercrimes and defense approaches in vehicular networks
37(28)
Anuraj Singh
Priyanka Chawla
Rajalakshmi Krishnamurthi
Adarsh Kumar
1 Introduction
37(5)
1.1 Defense working trends
39(1)
1.2 Wireless networks in defense landscape
39(1)
1.3 Cyberattacks in defense landscape
40(1)
1.4 Automated vehicle network
40(1)
1.5 Wireless networks
41(1)
1.6 Future challenges
42(1)
2 Literature review of cybersecurity and cyberattacks in defense networks
42(5)
2.1 Data breach attacks during the Covid-19 pandemic
42(1)
2.2 Types of cyberattacks
42(1)
2.3 Common data breach cyberattacks
43(2)
2.4 Cyberattack worldwide report in 2020
45(2)
3 Methodology for securing data from cyberattacks
47(4)
3.1 Application security issues and methodologies
49(1)
3.2 Information security issues and methodologies
50(1)
3.3 Network security issues and methodologies
50(1)
4 Data security measures
51(2)
4.1 Manage social media profile security
51(1)
4.2 Check privacy and security settings
51(1)
4.3 Avoid opening and delete suspicious email or attachments
52(1)
4.4 Keep software updated
52(1)
5 Cybersecurity in defense networks
53(7)
5.1 National defense networks
53(1)
5.2 Cybersecurity in military networks
54(1)
5.3 Cybersecurity in air networks
54(1)
5.4 Cybersecurity in naval networks
54(6)
6 Conclusion and future scope
60(5)
References
60(5)
Chapter 3 Autonomous driving systems and experiences: A comprehensive survey
65(16)
Saurabh Jain
Adarsh Kumar
Keshav Kaushik
Rajalakshmi Krishnamurthi
1 Introduction
65(5)
1.1 Classifications of autonomous vehicles
65(2)
1.2 Benefits of autonomous vehicles
67(1)
1.3 2D and 3D object detection systems in autonomous vehicles
67(1)
1.4 Simultaneous localization and mapping issues in driving
68(1)
1.5 Autonomous driving system and future directions
69(1)
2 Autonomous vehicle's datasets and features
70(3)
2.1 KITTI object detection dataset
70(1)
2.2 Cityscape dataset
70(1)
2.3 Mapillary Vistas Dataset
71(1)
2.4 ApolloScape dataset
71(1)
2.5 NuScenes dataset
71(1)
2.6 Comparative analysis of autonomous vehicle datasets and their features
72(1)
3 Lane detection system in autonomous vehicles
73(1)
3.1 Issues and challenges in vision-based lane detection and analysis systems
73(1)
3.2 Comparative analysis of vision-based end-to-end lane detection systems
73(1)
3.3 Road planning and object detection systems for autonomous vehicles
73(1)
3.4 Decision-making systems for autonomous vehicles
74(1)
4 Autonomous vehicle movement systems
74(3)
4.1 Optimal trajectory generation for dynamic street scenarios
75(1)
4.2 Path planning and challenges in autonomous vehicles
75(1)
4.3 Local and remote path planning challenges for off-road autonomous driving
75(1)
4.4 Motion planning for on-road autonomous vehicles
76(1)
4.5 Real-time autonomous vehicle's movement and control techniques
76(1)
4.6 Driving situations and vehicle path planning strategies
76(1)
5 Conclusion
77(4)
References
77(4)
Chapter 4 Applications of blockchain in automated heavy vehicles: Yesterday, today, and tomorrow
81(14)
Gaurav Sharma
Adarsh Kumar
Sukhpal Singh Gill
1 Introduction
81(3)
1.1 Blockchain for automated vehicles
81(1)
1.2 Record keeper for on-road automated vehicles
82(1)
1.3 Security measures for on-road automated vehicles
83(1)
1.4 Blockchain security enhancements for on-road automated vehicle systems
83(1)
1.5 Verification and validation (V&V) approaches for on-road automated vehicle systems
84(1)
1.6 Automated driving systems and future directions
84(1)
2 IoT devices and automated vehicles
84(2)
2.1 IoT for better safety scenario
84(1)
2.2 Facilities provided in automated vehicles
85(1)
2.3 Predictive maintenance
86(1)
2.4 Improving traffic conditions
86(1)
3 Security verification and analysis process
86(5)
3.1 Issues and challenges in blockchain networks
87(1)
3.2 Protection against active and passive attacks
88(1)
3.3 Intrusion detection and prevention mechanisms
88(1)
3.4 Data tampering resistance measures
89(1)
3.5 Formal security verification processes for automated vehicles
90(1)
3.6 Public, private, and consortium/federated blockchain technologies for automated vehicles
90(1)
4 Use case for blockchain-based automated vehicle management
91(1)
5 Conclusion
91(4)
References
92(3)
Chapter 5 Eco-routing navigation systems in electric vehicles: A comprehensive survey
95(30)
Kritanjali Das
Santanu Sharma
1 Introduction
95(3)
1.1 Electric vehicle and factors affecting its acceptability
96(2)
2 Eco-routing of electric vehicles
98(3)
2.1 Definition
98(1)
2.2 Motivation for eco-routing
99(1)
2.3 Future of eco-routing
100(1)
2.4 Current primary eco-routing methods
100(1)
3 Survey of literature
101(10)
3.1 Electric vehicle routing problem
101(1)
3.2 Electric vehicle energy consumption models
102(9)
4 Range determination in electric vehicles
111(2)
5 Existing eco-routing system prototypes
113(2)
5.1 Depending on the speed profiles
114(1)
5.2 Depending on historical and real-time traffic information
114(1)
5.3 Based on GPS and fuel consumption data
115(1)
5.4 Depending on time of travel and the route energy consumption
115(1)
6 Major challenges
115(1)
7 Proposed eco-routing system
116(1)
8 Future scope
117(1)
9 Conclusion
118(7)
Acknowledgment
118(1)
References
118(7)
SECTION 2 Implementation or Simulation-based study for heavy vehicles technologies
Chapter 6 Automatic vehicle number plate detection and recognition systems: Survey and implementation
125(16)
P. Srikanth
Adarsh Kumar
1 Introduction
125(1)
2 Survey of automated vehicle number detection systems
126(1)
3 Number detection system methodology
127(3)
3.1 Vehicle detection
127(1)
3.2 Number plate detection
128(2)
3.3 Character segmentation
130(1)
3.4 Character recognition
130(1)
4 Distributed computing platform for automated number detection
130(2)
5 Proposed automated vehicle number detection systems
132(5)
5.1 Datasets
134(1)
5.2 Experiment and evaluation
134(3)
6 Conclusion and future scope
137(4)
References
137(4)
Chapter 7 A secured IoT parking system based on smart sensor communication with two-step user verification
141(20)
M. Kannan
N. Jagadeesh
L. William Mary
C. Priya
1 Introduction
141(2)
1.1 Internet of Things in transport management
142(1)
2 Existing system
143(1)
3 EcoSystem: Internet of Things
143(4)
3.1 Applications
144(1)
3.2 Smart sensors
145(1)
3.3 Arduino microcontroller
146(1)
3.4 Merits and demerits of smart parking system (SPS)
146(1)
4 Proposed smart parking system
147(3)
4.1 System architecture
147(1)
4.2 Working procedure
147(2)
4.3 Proposed algorithm: VirtualParking
149(1)
5 Cloud computing
150(1)
5.1 Features of cloud computing
151(1)
6 Privacy-preserving smart parking system
151(3)
6.1 Data privacy and preservation
151(1)
6.2 Data aggregation
152(1)
6.3 Security attacks in IoT era
153(1)
6.4 Key note on radio frequency identification
154(1)
7 Networks and security
154(1)
7.1 A role for WSN in parking management
154(1)
7.2 Cryptography
155(1)
7.3 Summary of crypt-based parking system
155(1)
8 Discussion
155(2)
9 Conclusion
157(4)
References
157(2)
Further Reading
159(2)
Chapter 8 Man-and-wife coupling and need for artificially intelligent heavy vehicle technology in The Long, Long Trailer
161(22)
Nancy Ann Watanabe
1 Argument and comparative methodology
161(1)
2 Ethical and moral imperatives
161(1)
3 Film at the intersection of technology, art, and material culture
162(1)
4 Imaginary characters, real stars
163(1)
5 Film adaptation of literary biography
164(1)
6 Marriage as a connected vehicle
165(1)
7 Rocky Mountain imagery in film art and AI for HVT
166(1)
8 Missing: A catalytic converter
166(1)
9 State of the art in artificial intelligence
167(1)
10 Narratological framework and imagery
167(1)
11 High technology and middle class day dreamers
168(1)
12 Connected HVT, disconnected civilians
169(1)
13 Measuring space and time
170(1)
14 At the intersection: The artificiality of AI
170(1)
15 Climbing to the top in a connected heavy vehicle
171(1)
16 Romantic comedy of descent
172(2)
17 Collision and disaster at the family reunion
174(1)
18 Coupling and connectivity
175(1)
19 Love's chemistry, life's gravity
176(2)
20 Love's Rocky overload: Dangerous deception
178(1)
21 Conclusion
179(4)
References
180(1)
Further Reading
181(2)
Chapter 9 Pulse oximeter-based machine learning models for sleep apnea detection in heavy vehicle drivers
183(16)
Naman Gupta
Aayush Tiwari
Kushagra Pathak
Rajalakshmi Krishnamurthi
Adarsh Kumar
1 Introduction
183(1)
2 Literature survey
184(1)
3 Methodology
185(6)
3.1 Objective (a)
186(1)
3.2 Objective (b)
186(1)
3.3 Objective (c)
187(3)
3.4 Objective (d)
190(1)
4 Experimental setup
191(2)
5 Results and discussion
193(3)
6 Conclusion and future scope
196(3)
References
197(2)
Chapter 10 Using wavelet transformation for acoustic signal processing in heavy vehicle detection and classification
199(12)
Rajalakshmi Krishnamurthi
Dhanalekshmi Gopinathan
Adarsh Kumar
1 Introduction
199(1)
2 Literature survey
200(3)
2.1 Time domain audio features in heavy vehicles
200(1)
2.2 Frequency domain audio features
201(2)
3 Comparison of Morlet, Mexican hat, frequency B-spline wavelets in classification of vehicle sound
203(5)
3.1 Mexican hat wavelet transform
205(1)
3.2 Morlet wavelet transform
206(1)
3.3 Frequency B-spline wavelet transform
206(2)
4 Conclusion
208(3)
References
209(2)
Chapter 11 Congestion control mechanisms in vehicular networks: A perspective on Internet of vehicles (loV)
211(14)
Ashish Patil
P. Muthuchidambaranathan
N. Shekar
V. Shet
1 DCC mechanisms
213(5)
1.1 UBPFCC
214(1)
1.2 D-FPAV
214(1)
1.3 PULSAR
214(1)
1.4 LIMERIC
215(1)
1.5 D-NUM
215(1)
1.6 NDNUM
215(1)
1.7 FABRIC
215(1)
1.8 DCCS
216(1)
1.9 Speed-based distributed congestion control algorithm
216(1)
1.10 DisTraC
216(1)
1.11 Multistate active DCC mechanism
217(1)
1.12 Transmit data rate control-based DCC mechanism
217(1)
1.13 Unequal power issue and age of information
217(1)
2 Centralized congestion control mechanisms
218(5)
2.1 ML-CC
218(1)
2.2 DBDC
219(1)
2.3 BSAM
220(1)
2.4 MFCAR
220(1)
2.5 HRLB
220(1)
2.6 MLR
221(1)
2.7 PRE-VE
221(1)
2.8 DGGR
221(1)
2.9 DBDR
222(1)
3 Conclusion
223(2)
References
223(2)
Chapter 12 Smart traffic light management system for heavy vehicles
225(20)
Usha Mittal
Priyanka Chawla
Adarsh Kumar
1 Introduction
225(1)
2 Different techniques of traffic management systems for heavy vehicles
226(2)
2.1 Manual traffic control system
226(1)
2.2 Fixed time control system
226(1)
2.3 Fuzzy expert system (FES)
227(1)
2.4 Artificial neural networks (ANNs)
227(1)
2.5 Wireless sensor network (WSN)
228(1)
2.6 Image-processing based technique
228(1)
2.7 Hybrid technique
228(1)
3 Literature review
228(7)
4 Scope of study
235(1)
5 Proposed methodology
236(3)
6 Results and discussion
239(3)
7 Conclusion and future scope
242(3)
References
242(3)
Chapter 13 Smart automated system for classification of emergency heavy vehicles and traffic light controlling
245(18)
Suyash Verma
Akash Soni
Vaibhav Mishra
Vatsal Gupta
Rajalakshmi Krishnamurthi
Adarsh Kumar
1 Introduction
245(2)
2 Literature survey
247(2)
3 Methodology
249(2)
3.1 Controlling of traffic light according to the real-time traffic density on the road
249(2)
3.2 Emergency vs nonemergency vehicle classification
251(1)
4 Design and implementation
251(4)
4.1 Objective 1
252(3)
5 Results and findings
255(6)
5.1 Background subtraction method
255(3)
5.2 Convolutional neural network
258(3)
6 Conclusion
261(2)
References
261(2)
Chapter 14 Implementation of a cooperative intelligent transport system utilizing weather and road observation data
263(24)
Muhammad Naeem Tahir
Marcos Katz
Muhammad Saad Saud
1 Introduction
263(2)
2 Related work
265(1)
3 C-ITS communication and protocol
266(4)
3.1 C-ITS components
266(1)
3.2 C-ITS communication
267(2)
3.3 C-ITS protocol
269(1)
4 European framework of C-ITS
270(4)
5 Validation framework and deployment of C-ITS pilot system
274(4)
5.1 Validation framework for pilot system
274(1)
5.2 Deployment of C-ITS pilot system
275(3)
6 Results and discussion
278(3)
7 Summary /conclusion
281(6)
Acknowledgment
282(1)
References
282(5)
SECTION 3 Applications and case studies for heavy vehicles technologies
Chapter 15 Heavy vehicle defense procurement use cases and system design using blockchain technology
287(16)
Deepak Kumar Sharma
Adarsh Kumar
Gourav Bathla
1 Introduction
287(1)
1.1 Role of IT technology in defense
287(1)
1.2 Defense deal and trading issues
288(1)
1.3
Chapter key contributions
288(1)
2 Blockchain technology in defense
288(4)
2.1 Related work
290(1)
2.2 Blockchain and defense system characteristics
291(1)
2.3 Blockchain technology in defense applications
292(1)
3 Use cases of defense blockchain
292(8)
3.1 Supply chain management services in defense procurements
292(2)
3.2 Data communication between defense forces
294(6)
4 Conclusion and future scope
300(3)
Acknowledgments
300(1)
References
300(3)
Chapter 16 Cybercriminal approaches in big data models for automated heavy vehicles
303(32)
Keshav Kaushik
Gourav Bathla
Usman Naeem
Adarsh Kumar
1 Introduction
303(11)
1.1 Automated heavy vehicle working trends
305(7)
1.2 Wireless networks in automated heavy vehicles
312(1)
1.3 Big data models
312(1)
1.4 Cyberattacks in big data models
313(1)
1.5 Organization of chapter
314(1)
2 Cybersecurity and cyberattacks in networks (wired and wireless) for automated heavy vehicle movements
314(7)
2.1 Types of cyberattacks
315(2)
2.2 Popular data breaches and cyberattacks
317(3)
2.3 Cyberattacks in automated heavy vehicle infrastructure
320(1)
3 Data security measures for big data
321(3)
3.1 Manage social media profile security in semi-automatic vehicle big data
321(1)
3.2 Check privacy and security settings in heavy vehicle big data
322(2)
4 Big data analytics for heavy autonomous vehicles
324(5)
4.1 Big data-driven models for automated heavy vehicles
324(1)
4.2 Security in big data-driven dynamic driving cycle development for electric buses
325(1)
4.3 A big data-driven dynamic model for heavy trucks
325(1)
4.4 High-resolution air pollution mapping with Google street view cars
326(1)
4.5 Big data for internet of heavy vehicles
326(1)
4.6 Al-based big data-driven models for automated heavy vehicles
327(1)
4.7 Big data analytics for internet of heavy vehicles
327(2)
5 Conclusion and future scope
329(6)
References
329(6)
Chapter 17 Modeling fuel economy of connected vehicles using driving context
335(18)
Neetika Jain
Sangeeta Mittal
1 Introduction
335(1)
2 Literature review
336(2)
2.1 Comparative analysis of different approaches
336(2)
2.2 Limitation of existing approaches
338(1)
3 Proposed architecture for estimating fuel efficiency
338(7)
3.1 Problem formulation
338(1)
3.2 The architecture of proposed solution
339(1)
3.3 Design of factors affecting fuel economy---Defining predictor variables
340(1)
3.4 Environmental context
340(1)
3.5 Driving behavior identification
340(1)
3.6 Model for prediction of fuel consumption
341(1)
3.7 Application of GLM model for prediction of fuel consumption
342(1)
3.8 Dataset
343(1)
3.9 Framing GLM-based model for fuel consumption prediction
343(2)
4 Results and discussion
345(6)
4.1 Defining metrics for evaluating the accuracy of the model
345(1)
4.2 Error distribution for SGLM
346(1)
4.3 Error distribution of CGLM-L5
346(1)
4.4 Error distribution of CGLM-L10
346(1)
4.5 Error distribution of CGLM-L25
347(1)
4.6 Comparison of SGLM, CGLM, and VolScore models
347(4)
4.7 Comparison of predicted, calibrated, and observed values
351(1)
5 Conclusion
351(2)
References
351(2)
Chapter 18 Conceptual design and computational investigations of fixed wing unmanned aerial vehicle for medium-range applications
353(22)
R. Vijayanandh
S. Senthilkumar
R. Rajkumar
Adarsh Kumar
M. Senthil Kumar
J. Darshan Kumar
K.M. Krishna Kumar
R. Arul Prakash
1 Introduction
353(1)
2 Literature survey
354(3)
3 Symbols
357(1)
4 Conceptual design
357(15)
4.1 Estimating wing surface area, wingspan, chord length, and fuselage length
357(3)
4.2 Empennage design---Horizontal tail
360(2)
4.3 Empennage and landing gear design---Stabiligear
362(1)
4.4 Estimation of propulsive system and its weight
362(1)
4.5 Estimation of co-efficient of lift for propeller
363(1)
4.6 Estimation of co-efficient of lift for wing
363(1)
4.7 Mechanical power estimation
363(1)
4.8 Estimation of propeller's pitch
364(1)
4.9 Estimation of pitch angle and chord of the propeller
364(1)
4.10 Aerofoil selection
365(2)
4.11 Estimation of electrical and electronics system and its weight
367(2)
4.12 Hybrid navigation system for medium range fixed wing UAVs
369(3)
5 Conclusions
372(3)
References
372(3)
Chapter 19 Multi-sensor fusion in autonomous heavy vehicles
375(16)
Sakshi Gupta
Itu Snigdh
1 Introduction
375(2)
1.1 Current status
375(1)
1.2 Pros and cons of automated driving systems
376(1)
2 Autonomous heavy vehicle subsystems
377(1)
3 Communication protocols in autonomous heavy vehicles
378(1)
4 ECU in autonomous heavy vehicles
378(1)
5 The sensors used in autonomous heavy vehicles
379(1)
6 Essential sensors used in ADSs
380(1)
7 Sensor fusion in autonomous heavy vehicles
381(3)
7.1 Levels of sensor fusion
382(1)
7.2 Working of sensor fusion module
383(1)
8 Multi-sensor data fusion approaches
384(1)
9 Advantages and challenges in multi-sensor data fusion in AHVs
385(1)
10 Conclusion
386(1)
11 Future directions
386(5)
References
386(5)
Chapter 20 Smart vehicle accident detection for flash floods
391(26)
Kazi Hassan Shakib
Farhin Faiza Neha
Kazi Hassan Shazib
1 Introduction
391(4)
1.1 Motivation of this research work
392(1)
1.2 Modern technologies used for accident detection
392(3)
1.3 Objectives of this research work
395(1)
1.4
Chapter organization
395(1)
2 Literature review
395(5)
3 Proposed methodology
400(4)
3.1 User registration and installation
401(1)
3.2 Getting sensor data
401(1)
3.3 Thresholding
401(1)
3.4 Notification generation and fault-tolerant infrastructure
401(1)
3.5 Response to the notification
402(1)
3.6 Growing dataset and prediction models
402(1)
3.7 Summary of the total accident detection procedure
402(2)
4 Design and architecture
404(2)
4.1 Server and app connectivity
404(1)
4.2 SOS signal generation
404(1)
4.3 SOS response and VANET infrastructure
405(1)
4.4 Response from server
406(1)
4.5 Impact of disasters on parameter threshold
406(1)
5 System implementation
406(4)
5.1 App installation and registration
407(1)
5.2 App activation and user interface
407(2)
5.3 SOS generation and confirmation
409(1)
6 Result
410(2)
6.1 SOS check and finding nearby hospitals
410(1)
6.2 Fault tolerance of the system
411(1)
6.3 Server compatibility in predicting disasters and accidents
412(1)
7 Discussion
412(2)
8 Conclusion and future directions
414(3)
References
414(2)
Further Reading
416(1)
Index 417
Rajalakshmi Krishnamurthi is a Senior Member of IEEE, Professional Member of ACM, SIAM, IET and CSI. She is serving as Treasurer, Delhi ACM-W chapter. She is currently working as Assistant Professor (Senior Grade), Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India. She has more than 17 year of teaching experience. She has more than 50 research publications in various reputed peer reviewed International Journal, Book Chapters, and International Conferences. Her research interest includes Internet of Things, Cloud Computing, optimization techniques in wireless mobile networks, e-learning applications using mobile platform and advanced fuzzy approaches. Dr. Adarsh Kumar is an Professor in the School of Computer Science with University of Petroleum & Energy Studies, Dehradun, India. He received his Master degree (M. Tech) in Software Engineering from Thapar University, Patiala, Punjab, India and earned his PhD degree from Jaypee Institute of Information Technology University, Noida, India followed by Post-Doc from Software Research Institute, Athlone Institute of Technology, Ireland. From 2005 to 2016, he has been associated with the Department of Computer Science Engineering & Information Technology, Jaypee Institute of Information Technology, Noida, Uttar-Pardesh, India, where he worked as Assistant Professor. His main research interests are cybersecurity, cryptography, network security, and ad-hoc networks. He has many research papers in reputed journals, conferences and workshops. He participated in one European Union H2020 sponsored research project and he is currently executing two research projects sponsored from UPES SEED division and one sponsored from Lancaster University. Dr. Sukhpal Singh Gill is a Lecturer and Assistant Professor in Cloud Computing at the School of Electronic Engineering and Computer Science, Queen Mary University of London, UK. Prior to his present stint, Dr. Gill has held positions as a Research Associate at the School of Computing and Communications, Lancaster University, UK and also as a Postdoctoral Research Fellow at CLOUDS Laboratory, The University of Melbourne, Australia. Dr. Gill is serving as an Associate Editor for Wiley ETT and IET Networks Journal. He has co-authored 100+ peer-reviewed papers and has published in prominent international journals and conferences such as IEEE TCC, IEEE TSC, IEEE TII, IEEE TNSM, IEEE IoT Journal, Elsevier JSS/FGCS, IEEE/ACM UCC, and IEEE CCGRID. He has received several awards, including the Distinguished Reviewer Award from SPE (Wiley), Best Paper Award AusPDC at ACSW 2021, and has also served as the PC member for venues such as PerCom, UCC, CCGRID, CLOUDS, ICFEC, AusPDC. He has edited research books for Springer, Elsevier and CRC Press. His research interests include Cloud Computing, Fog Computing, Software Engineering, Internet of Things and Energy Efficiency. Fatos Xhafa, PhD in Computer Science, is Full Professor at the Technical University of Catalonia (UPC), Barcelona, Spain. He has held various tenured and visiting professorship positions. He was a Visiting Professor at the University of Surrey, UK (2019/2020), Visiting Professor at the Birkbeck College, University of London, UK (2009/2010) and a Research Associate at Drexel University, Philadelphia, USA (2004/2005). He was a Distinguished Guest Professor at Hubei University of Technology, China, for the duration of three years (2016-2019). Prof. Xhafa has widely published in peer reviewed international journals, conferences/workshops, book chapters, edited books and proceedings in the field (H-index 55). He has been awarded teaching and research merits by the Spanish Ministry of Science and Education, by IEEE conferences and best paper awards. Prof. Xhafa has an extensive editorial service. He is founder and Editor-In-Chief of Internet of Things - Journal - Elsevier (Scopus and Clarivate WoS Science Citation Index) and of International Journal of Grid and Utility Computing, (Emerging Sources Citation Index), and AE/EB Member of several indexed Int'l Journals. Prof. Xhafa is a member of IEEE Communications Society, IEEE Systems, Man & Cybernetics Society and Founder Member of Emerging Technical Subcommittee of Internet of Things. His research interests include IoT and Cloud-to-thing continuum computing, massive data processing and collective intelligence, optimization, security and trustworthy computing and machine learning, among others. He can be reached at fatos@cs.upc.edu. Please visit also http://www.cs.upc.edu/~fatos/ and at http://dblp.uni-trier.de/pers/hd/x/Xhafa:Fatos
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