Intelligent Wireless Communications (Telecommunications)

The incredible growth in the development and use of wireless communication technologies has led to research in both academia and industry on artificial intelligence (AI) methods that enable intelligent technologies, smarter services and applications, business processes and social interactions to satisfy future requirements. AI mechanisms are being exploited in smart intelligent network applications to provide insights from collected data by identifying patterns and allowing operational predictions with higher accuracy in smaller time periods. The future of AI-powered wireless networking infrastructures depends on finding effective solutions to a number of technical challenges that such paradigms introduce, including better intelligent sensor capabilities, smarter big data analytics, automated remote data management, as well as open and secure processes. This book presents innovative research in emerging artificial intelligence methods for the processing, storing and analysing of large date sets generated by wireless communication infrastructures. It addresses major new technological developments, reflects on industry needs, current research trends and future directions. The authors focus on the development of AI-powered mechanisms for future wireless networking applications and architectures which will lead to more performant, resilient and valuable ecosystems and automated services. The book is aimed at researchers, engineers and scientists involved in the design and development of protocols and AI applications for wireless communication devices, and wireless networking technologies. Contents About the editors 1. An overview of the intelligent big data analytics and their technological presence in the modern digital age | Reinaldo Padilha Franca, Ana Carolina Borges Monteiro, Rangel Arthur and Yuzo Iano 1.1 Introduction 1.2 Big data 1.3 Artificial intelligence 1.4 Big data analytics 1.5 Discussion 1.6 Trends 1.7 Conclusions References 2. Artificial intelligence in IoT and its applications | Mona Bakri Hassan, Elmustafa Sayed Ali, Nahla Nurelmadina and Rashid A. Saeed 2.1 Introduction 2.2 AI revolution in IoT 2.3 Intelligent sensing 2.4 Feature of AI with IoT 2.5 Intelligent machine and deep learning in IoT 2.6 IoT-based AI data access and distributed processing 2.7 AI contribution aspects to Internet of things 2.8 AI for IoT applications 2.9 Summary References 3. Green energy harvesting protocols for intelligent wireless communication systems | Ganesh Prasad and Deepak Mishra 3.1 Background and motivation 3.2 Architecture of energy harvesting wireless networks 3.3 Energy harvesting models for intelligent wireless communications 3.4 AI-assisted online algorithms for optimal energy harvesting communications 3.5 Optimal resource allocation in EH self-sustainable communication systems 3.6 Conclusion and future directions References 4. Discrete wavelet transform applications in the IoMT | Tamara K. Al-Shayea, Constandinos X. Mavromoustakis, George Mastorakis, Jordi Mongay Batalla, Evangelos Pallis, Evangelos K. Markakis, Imran Khan and Dinh-Thuan Do 4.1 Introduction 4.2 The discrete wavelet transform 4.3 DWT applications 4.4 Conclusions 4.5 Future work References 5. Intelligent agents system for medical information communication | Mariya Evtimova-Gardair and Evangelos Pallis 5.1 Introduction 5.2 Analysis of the agent technologies 5.3 Usage of multiagent system for extraction of information 5.4 Big data impact when searching 5.5 Model of the multiagent searching system 5.6 Conceptual schema of the proposed searching system 5.7 Implementation of the searching system in Internet 5.8 Implementation of the intelligent system in Internet 5.9 Conclusion References 6. Intelligent Internet of things in wireless networks | Mona Bakri Hassan, Elmustafa Sayed Ali and Rashid A. Saeed 6.1 Introduction 6.2 IoT networks 6.3 Reprogrammable and reconfigurable of IoT devices 6.4 Open source platforms in IoT networks 6.5 Analysis IoT network context 6.6 Intelligent IoT network algorithms and strategy 6.7 Heterogeneous IoT-based 5G networks 6.8 IoT network adaptive quality of service 6.9 Summary References 7. Impact of jamming signal on system performance in downlink of IoT network relying on nonorthogonal multiple access | Thi-Anh Hoang, Chi-Bao Le, Dinh-Thuan Do, Imran Khan, Constandinos X. Mavromoustakis, George Mastorakis, Evangelos Pallis and Evangelos K. Markakis 7.1 Introduction 7.2 Consideration on IoT system under the impact of a jamming signal 7.3 Outage probability and throughput analysis 7.4 Numerical results 7.5 Conclusion References 8. QoS of communication networks using MPLS protocol | Azeddien M. Sllame 8.1 Introduction 8.2 Definitions related to chapter context 8.3 Quality of service principles 8.4 MPLS principles 8.5 MPLS and multimedia 8.6 MPLS mechanism and data center design 8.7 MPLS-based network-on-chip simulator 8.8 Conclusion References 9. Damaged critical infrastructure for VANETs | Grace Khayat, Constandinos X. Mavromoustakis, George Mastorakis, Jordi Mongay Batalla, Hoda Maalouf, Evangelos Pallis, Evangelos K. Markakis, Imran Khan and Naercio Magaia 9.1 Introduction 9.2 VANET architectures 9.3 VANET hybrid architecture 9.4 VANET routing 9.5 VANET routing protocols 9.6 Cluster routing protocol 9.7 Clustering objectives 9.8 Clustering protocols 9.9 Cluster head selection protocols 9.10 Cluster head election criteria 9.11 VANET cluster routing protocols review 9.12 Clusters with double cluster head 9.13 Cluster merging 9.14 VANET in crisis 9.15 Weighted double cluster head selection 9.16 Conclusion References 10. Artificial intelligence-enabled optical wireless communication links: a revolutionary approach toward smart communication model | Rajan Miglani, Jagjit Singh Malhotra, Sushank Chaudhary and Gurjot Singh Gaba 10.1 Introduction 10.2 Overview of optical wireless communication systems 10.3 Introduction to artificial intelligence 10.4 AI for optical links: opportunities and challenges 10.5 Summary References 11. Intelligent underwater wireless communications | Elmustafa Sayed Ali and Rashid A. Saeed 11.1 Introduction 11.2 Underwater EM wireless communication 11.3 Underwater communication networks 11.4 Internet of underwater things 11.5 Underwater intelligent data gathering system 11.6 The underwater intelligent recharge docking system 11.7 Intelligent UWC methodologies 11.8 Intelligent UWC modeling 11.9 Summary References 12. Machine learning algorithms for smart data analysis in the Internet of things: an overview | Mohammed H. Alsharif, Anabi Hilary Kelechi, Imran Khan, Mahmoud A. Albreem, Abu Jahid, Constandinos X. Mavromoustakis, George Mastorakis, Evangelos Pallis and Evangelos K. Markakis 12.1 Introduction 12.2 Taxonomies of supervised and unsupervised ML algorithms 12.3 Research trends and open issues 12.4 Conclusions and recommendations References 13. Artificial intelligence and machine learning aided blockchain systems to address security vulnerabilities and threats in the industrial Internet of things | Karanjeet Choudhary, Gurjot Singh Gaba, Rajan Miglani, Lavish Kansal and Pardeep Kumar 13.1 Introduction 13.2 Birth of industrial Internet of things 13.3 Application areas and current examples of its use 13.4 Implementation challenges of IIoT 13.5 Vulnerabilities, threats, and risks 13.6 Security considerations 13.7 Future of IIoT 13.8 Introduction to blockchain 13.9 Introduction to AI 13.10 Introduction to machine learning 13.11 Conclusions References 14. Improved gain vector-based recursive least squares for smart antenna applications | Peter N. Chuku, Thomas O. Olwal and Karim Djouani 14.1 Introduction 14.2 Related work 14.3 System model 14.4 Performance evaluation and results 14.5 Conclusions Acknowledgments References 15. Forecast of electricity consumption: a comparison of ARIMA and neural networks | Theodoros Pseftelis, Constandinos Mavromoustakis, George Mastorakis, Periklis Chatzimisios, Evangelos K. Markakis, Evangelos Pallis and Jordi Mongay Batalla 15.1 Introduction 15.2 Data overview 15.3 ARIMA 15.4 Neural networks 15.5 Compare methods References 16. Smart interoperability public safety wireless network | Adil Akasha, Rashid A. Saeed and Elmustafa Sayed Ali 16.1 Introduction 16.2 Public safety and emergency networks 16.3 The need for public safety and emergency collaboration 16.4 Cooperative wireless communication 16.5 Radio systems in public safety and emergency 16.6 Characteristics of smart radios 16.7 Vision of new communications generation for emergency 16.8 Recent public safety interpretability systems and future directions 16.9 Conclusion References Index Aimed At Researchers, Engineers And Scientists Involved In The Design And Development Of Protocols And Ai Applications For Wireless Communication Devices And Networks, This Edited Book Presents Recent Research And Innovations In Emerging Ai Methods And Ai-powered Mechanisms, And Future Perspectives In This Field.