: George Mastorakis, Constandinos X. Mavromoustakis, Jordi Mongay Batalla, Evangelos Pallis
: Convergence of Artificial Intelligence and the Internet of Things
: Springer-Verlag
: 9783030449070
: 1
: CHF 47.80
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: Anwendungs-Software
: English
: 446
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This book gathers recent research work on emerging Artificial Intelligence (AI) methods for processing and storing data generated by cloud-based Internet of Things (IoT) infrastructures. Major topics covered include the analysis and development of AI-powered mechanisms in future IoT applications and architectures. Further, the book addresses new technological developments, current research trends, and industry needs. Presenting case studies, experience and evaluation reports, and best practices in utilizing AI applications in IoT networks, it strikes a good balance between theoretical and practical issues. It also provides technical/scientific information on various aspects of AI technologies, ranging from basic concepts to research grade material, including future directions. 

The book is intended for researchers, practitioners, engineers and scientists involved in the design and development of protocols and AI applications for IoT-related devices. As the book covers a wide range of mobile applications and scenarios where IoT technologies can be applied, it also offers an essential introduction to the field.



George Mastorakis received his B.E. (Honours) in Electronic Engineering from UMIST (University of Manchester Institute of Science& Technology) in 2000, his M.Sc. in Telecommunications from UCL (University College London) in 2001 and his Ph.D. in Telecommunications from the University of the Aegean in 2008. He currently serves as an Associate Professor in the Department of Management Science and Technology at Hellenic Mediterranean University in Greece and as a Director of e-Business Intelligence Laboratory. He has actively participated in a large number of European funded research projects (FP6, FP7 and Horizon2020) and national research ones. He has also acted as a technical manager in many research projects funded by GSRT (General Secretariat for Research& Technology, Ministry of Development, Greece). He has more than 250 publications at various international conference proceedings, workshops, scientific journals and book chapters. His research interests include cognitive radio networks, IoT applications, IoE architectures, radio resource management, artificial intelligence applications, networking traffic analysis, 5G mobile networks, dynamic bandwidth management and energy-efficiency networks

Constandinos X. Mavromoustakis (male, Prof., Ph.D. since 2006) is currently a Professor at the Department of Computer Science at the University of Nicosia, Cyprus. He received a five-year dipl. Eng. (B.Sc., B.E., M.E./KISATS approved/accredited) in Electronic and Computer Engineering from Technical University of Crete, Greece, M.Sc. in Telecommunications from University College of London, UK, and his Ph.D. from the Department of Informatics at Aristotle University of Thessaloniki, Greece. Professor Mavromoustakis is leading the Mobile Systems Lab. (MOSys Lab., www.mosys.unic.ac.cy) at the Department of Computer Science at the University of Nicosia. He is the Chair of the IEEE/ R8 regional Cyprus section since November 2019, and since May 2009, he serves as the Chair of C16 Computer ^230) including several books (IDEA/IGI, Springer and Elsevier). He has served as a consultant to many industrial bodies (including Intel Corporation LLC (www.intel.com)), and he is a management member of IEEE Communications Society (ComSoc) Radio Communications Committee (RCC) and a board member of the IEEE-SA Standards IEEE SCC42 WG2040. He has participated in several FP7/H2020/Eureka and national projects. He is a co-founder of the IEEE Technical Committee on IEEE SIG on Big Data Intelligent Networking (IEEE TC BDIN SIG) and currently serves as a Vice-chair.

Jordi Mongay Batalla (male, Prof. Ph.D.) received his M.Sc. degree from Universitat Politecnica de Valencia (Spain) in 2000 and Ph.D. degree from Warsaw University of Technology (Poland) in 2010. He worked in Centro Nazionale di Astrofisica in Bologna (Italy) and in Telcordia Poland (Ericsson R&D Co.). Currently, he is a Professor at Warsaw University of Technology and he is also with National Institute of Telecommunications, where he is the Deputy Director of research. His research interest focuses mainly on new technologies for mobile networks (network services chain, NFV, SDN, slicing, blockchain) and applications (Internet of Things, smart cities, multimedia) for the future Internet. Jordi Mongay Batalla has coordinated around ten R&D international projects and took part (coordination and/or participation) in more than 10 European ICT research projects, four of them inside the EU ICT Framework Programmes. He is Co-editor of several books on the Internet of Things and 5G, author or co-author of more than 150 papers published in books, international and national peer-reviewed journals (such as IEEE Communications Magazine, IEEE Wireless Communications, ACM Computing Surveys, IEEE Systems and Springer Journal of Real-Time Image Processing) and conference proceedings (e.g. IEEE Globecom, IEEE ICC, IEEE/IFIP IM) and several patent appliances. Jordi Mongay Batalla is Editor of several international journals and magazines and has co-edited special issues in the most important research journals. He is involved in several standardization bodies such as ITU working groups, European Blockchain Services Infrastructure technical group and Polish Normalization Committee.

Evangelos Pallis is a Professor in the Department of Electrical and Computer Engineering at Hellenic Mediterranean University in Greece and Co-director of Research and Development of Telecommunication Systems Laboratory 'PASIPHAE' of the same department. He received his B.Sc. in Electronic Engineering from the Technological Educational Institute of Crete in 1994, his M.Sc. in Telecommunications from University of East London, in 1997, and received his Ph.D. in Telecommunications from the University of East London in 2002. His research interests are in the fields of wireless networks, mobile communication systems, digital broadcasting technologies and interactive television systems, QoS/QoE techniques and network management technologies. He has participated in a number of national and European funded R&D projects, including the AC215 'CRABS', IST-2000-26298 'MAMBO', IST-2000-28521 'SOQUET', IST-2001-34692 'REPOSIT', IST-2002-FP6-507637 'ENTHRONE', 'IMOSAN',  and as Technical/Scientific coordinator for the IST-2002-FP6-507312 'ATHENA' project. He has been involved within the FP7-214751 'ADAMANTIUM', in the FP7-ICT-224287 'VITAL++' and in the FP7-ICT-248652 'ALICANTE' projects and several HORIZON2020 projects. He has more than 200 publications in international scientific journals, conference papers and book chapters in the above scientific areas. He is the general chairman of the International Conference on Telecommunications and Multimedia (TEMU), member of IET/IEE and active contributor to the IETF interconnection of content distribution networks (CDNi).
Introduction6
Research Solutions7
Conclusion13
References13
Contents15
Fog Computing: Data Analytics for Time-Sensitive Applications17
1 Introduction17
2 Fog Computing Applications18
3 Architecture of Fog Computing20
3.1 Smart Layer20
3.2 Fog Layer21
3.3 Cloud Services21
4 Benefits of Fog Computing21
5 Challenges of Fog Computing23
6 Conclusion and Discussions26
References27
Medical Image Watermarking in Four Levels Decomposition of DWT Using Multiple Wavelets in IoT Emergence30
1 Introduction31
2 Digital Image Watermarking Algorithms32
2.1 Biorthogonal Wavelet33
2.2 Reverse Biorthogonal Wavelet33
2.3 Symlet Wavelet34
2.4 Coiflets Wavelet34
2.5 Discrete Meyer Wavelet35
3 The Proposed Medical Image Watermarking Algorithm37
4 Experimental Results and Evaluation40
5 Conclusion44
References45
Optimised Statistical Model Updates in Distributed Intelligence Environments47
1 Introduction47
1.1 Problem Statement48
1.2 Paper Organisation48
2 Related Work and Background48
2.1 Optimised Sequential Decision Making49
2.2 Contribution50
3 Methodology51
3.1 Optimal Postponing Policy51
3.2 Policies Under Comparison54
4 Performance Evaluation58
4.1 Data Sets58
4.2 Experimentation with Linear Regression Models58
4.3 Experimentation with Support Vector Regression Models61
4.4 Evaluation Summary65
5 Conclusions66
References71
Intelligent Vehicular Networking Protocols73
1 Introduction74
2 Routing Protocols in VANET76
2.1 Topology Based Routing Protocols77
2.2 Position-Based Routing Protocols or Geographic Routing Protocols84
2.3 Broadcast Routing91
2.4 Geocast Routing Protocols93
2.5 Cluster-Based Routing Protocols95
3 Internet of Vehicles97
3.1 Unicast Protocol98
3.2 Multicast Protocol98
3.3 Broadcast Protocol99
4 Conclusion99
References99
Towards Ubiquitous Privacy Decision Support: Machine Prediction of Privacy Decisions in IoT101
1 Introduction101
2 Related Work104
2.1 Prediction of Privacy Decision-Making105
2.2 Privacy Segmentation107
3 Dataset of Privacy Decisions108
4 Privacy Decision Prediction109
4.1 Machine Learning Model110
4.2 Features112
4.3 Training Strategy116
4.4 Implications120
5 Discussion and Future Work123
5.1 Representability of Data123
5.2 Reliability of Privacy Segmentation123
5.3 Privacy Paradox124
6 Conclusion124
References127
Energy-Efficient Design of Data Center Spaces in the Era of IoT Exploiting the Concept of Digital Twins130
1 Introduction131
2 Related Work132
2.1 Energy Consumption in Data Centers132
2.2 Degrees of Freedom in Energy Efficiency135
2.3 Power Usage Effectiveness137
2.4 Data Centers and Building Envelopes139
3 Proposed Methodology140
3.1 Modeling Procedure140
3.2 Software Simulation Tools142
3.3 Creating the 3D Geometry in SketchUp143
3.4 Measurements145
3.5 Setting Model Parameters in OpenStudio .OSM File146
3.6 Hardware Laboratory’s Simulation Results149
3.7 Data Center Simulation Results150
4 Models’ Validation151
4.1 Hardware Laboratory’s Validation Results151
4.2 Data Center’s Validation Results152
4.3 PUE Calculations for Various Structural Interventions152
5 Conclusions153
References155
In-Network Machine Learning Predictive Analytics: A Swarm Intelligence Approach157
1 Introduction158
1.1 Motivation158
1.2 Aim159
1.3 Outline159
2 Background160
2.1 Particle Swarm Optimisation Algorithm160
2.2 Particle Representation161
2.3 Parameters162
2.4 The PSO Algorithm163
2.5 Regression164
2.6 Prediction Error Metrics166
2.7 Network Modelling166
2.8 Mica2 Wireless Sensor Platform168
3 Analysis169
3.1 Problem Definition and Analysis170
3.2 Baseline Methodology171
3.3 Limitations172
3.4 Proposed Methodology173
3.5 Network Model Impact175
4 Implementation175
4.1 Programming Language175
4.2 Data176
4.3 Particle Swarm Optimisation178
4.4 Convergence181
4.5 Network Models182
5 Performance and Comparative Assessment184
5.1 Prerequisites184
5.2 Random Network Assessment185
5.3 Small World Network Assessment189
6 Conclusions and Future Work191
6.1 Assessment Results191
6.2 Future Work194
6.3 Summary194