Mubashir Husain Rehmani – författare

The rapid growth of the data traffic demands new ways to achieve high-speed wireless links. The backbone networks, data centers, mission-critical applications, as well as end-users sitting in office or home, all require ultra-high throughput and ultra-low latency wireless links. Sophisticated technological advancement and huge bandwidth are required to reduce the latency. Terahertz band, in this regard, has a huge potential to provide these high-capacity links where a user can download the file in a few seconds. To realize the high-capacity wireless links for future applications, in this book, different aspects of the Terahertz band wireless communication network are presented. This book highlights the Terahertz channel characteristics and modeling, antenna design and beamforming, device characterization, applications, and protocols. It also provides state-of-the-art knowledge on different communication aspects of Terahertz communication and techniques to realize the true potential of the Terahertz band for wireless communication.

This comprehensive book unveils the working relationship of blockchain and the fog/edge computing. The contents of the book have been designed in such a way that the reader will not only understand blockchain and fog/edge computing but will also understand their co-existence and their collaborative power to solve a range of versatile problems.The first part of the book covers fundamental concepts and the applications of blockchain-enabled fog and edge computing. These include: Internet of Things, Tactile Internet, Smart City; and E-challan in the Internet of Vehicles. The second part of the book covers security and privacy related issues of blockchain-enabled fog and edge computing. These include, hardware primitive based Physical Unclonable Functions; Secure Management Systems; security of Edge and Cloud in the presence of blockchain; secure storage in fog using blockchain; and using differential privacy for edge-based Smart Grid over blockchain.This book is written for students, computer scientists, researchers and developers, who wish to work in the domain of blockchain and fog/edge computing. One of the unique features of this book is highlighting the issues, challenges, and future research directions associated with Blockchain-enabled fog and edge computing paradigm. We hope the readers will consider this book a valuable addition in the domain of Blockchain and fog/edge computing.

This comprehensive book unveils the working relationship of blockchain and the fog/edge computing. The contents of the book have been designed in such a way that the reader will not only understand blockchain and fog/edge computing but will also understand their co-existence and their collaborative power to solve a range of versatile problems.The first part of the book covers fundamental concepts and the applications of blockchain-enabled fog and edge computing. These include: Internet of Things, Tactile Internet, Smart City; and E-challan in the Internet of Vehicles. The second part of the book covers security and privacy related issues of blockchain-enabled fog and edge computing. These include, hardware primitive based Physical Unclonable Functions; Secure Management Systems; security of Edge and Cloud in the presence of blockchain; secure storage in fog using blockchain; and using differential privacy for edge-based Smart Grid over blockchain.This book is written for students, computer scientists, researchers and developers, who wish to work in the domain of blockchain and fog/edge computing. One of the unique features of this book is highlighting the issues, challenges, and future research directions associated with Blockchain-enabled fog and edge computing paradigm. We hope the readers will consider this book a valuable addition in the domain of Blockchain and fog/edge computing.

Emerging Communication Technologies Based on Wireless Sensor Networks: Current Research and Future Applications fills a gap in the existing literature by combining a plethora of WSN-based emerging technologies into a single source so that researchers can form opinions regarding these technologies. It presents different types of emerging communication technologies based on WSNs and describes how wireless sensor networks can be integrated with other communication technologies. It covers many of the new techniques and demonstrates the application of WSNs. The book’s 14 chapters are divided into four parts.The first part covers the basics of wireless sensor networks and their principal working methods. The authors then move on to discuss different types of WSNs, characteristics of different types of emerging technologies based on WSNs, renewable energy sources, battery replenishment strategies, and application-specific energy challenges of WSNs. The second part is dedicated to issues related to wireless body area networks (WBANs). It discusses wearable WSNs and their applications, standards, and research trends. The authors also discuss routing schemes devised for WBANs and thermal-aware routing protocols for WBANs.The third part focuses on different emerging communication technologies based on WSNs, including electromagnetic wireless nanosensor networks, WSNs in the IoT, management of WSNs through satellite networks, WSNs in smart homes, and cognitive radio technology in conjunction with WSNs. The last part of the book covers topics generally related to typical WSNs, including energy-efficient data collection in WSNs, key distribution mechanisms in WSNs, distributed data gathering algorithms for mobile WSNs, and finally, a novel mobility scheme for WSNs that supports IPv6.

The rapid growth of the data traffic demands new ways to achieve high-speed wireless links. The backbone networks, data centers, mission-critical applications, as well as end-users sitting in office or home, all require ultra-high throughput and ultra-low latency wireless links. Sophisticated technological advancement and huge bandwidth are required to reduce the latency. Terahertz band, in this regard, has a huge potential to provide these high-capacity links where a user can download the file in a few seconds. To realize the high-capacity wireless links for future applications, in this book, different aspects of the Terahertz band wireless communication network are presented. This book highlights the Terahertz channel characteristics and modeling, antenna design and beamforming, device characterization, applications, and protocols. It also provides state-of-the-art knowledge on different communication aspects of Terahertz communication and techniques to realize the true potential of the Terahertz band for wireless communication.

Machine learning has shown tremendous benefits in solving complex network problems and providing situation and parameter prediction. However, heavy resources are required to process and analyze the data, which can be done either offline or using edge computing but also requires heavy transmission resources to provide a timely response. The need here is to provide lightweight machine learning protocols that can process and analyze the data at run time and provide a timely and efficient response. These algorithms have grown in terms of computation and memory requirements due to the availability of large data sets. These models/algorithms also require high levels of resources such as computing, memory, communication, and storage. The focus so far was on producing highly accurate models for these communication networks without considering the energy consumption of these machine learning algorithms.For future scalable and sustainable network applications, efforts are required toward designing new machine learning protocols and modifying the existing ones, which consume less energy, i.e., green machine learning protocols. In other words, novel and lightweight green machine learning algorithms/protocols are required to reduce energy consumption which can also reduce the carbon footprint. To realize the green machine learning protocols, this book presents different aspects of green machine learning for future communication networks. This book highlights mainly the green machine learning protocols for cellular communication, federated learning-based models, and protocols for Beyond Fifth Generation networks, approaches for cloud-based communications, and Internet-of-Things. This book also highlights the design considerations and challenges for green machine learning protocols for different future applications.

Machine learning has shown tremendous benefits in solving complex network problems and providing situation and parameter prediction. However, heavy resources are required to process and analyze the data, which can be done either offline or using edge computing but also requires heavy transmission resources to provide a timely response. The need here is to provide lightweight machine learning protocols that can process and analyze the data at run time and provide a timely and efficient response. These algorithms have grown in terms of computation and memory requirements due to the availability of large data sets. These models/algorithms also require high levels of resources such as computing, memory, communication, and storage. The focus so far was on producing highly accurate models for these communication networks without considering the energy consumption of these machine learning algorithms.For future scalable and sustainable network applications, efforts are required toward designing new machine learning protocols and modifying the existing ones, which consume less energy, i.e., green machine learning protocols. In other words, novel and lightweight green machine learning algorithms/protocols are required to reduce energy consumption which can also reduce the carbon footprint. To realize the green machine learning protocols, this book presents different aspects of green machine learning for future communication networks. This book highlights mainly the green machine learning protocols for cellular communication, federated learning-based models, and protocols for Beyond Fifth Generation networks, approaches for cloud-based communications, and Internet-of-Things. This book also highlights the design considerations and challenges for green machine learning protocols for different future applications.

The incorporation of Cognitive Radio (CR) into the Internet of Vehicles (IoV) has emerged as the Intelligent Transportation System (ITS). Section 1 covers the aspects of cognitive radio when it provides support to IoV. The challenges which limit the performance of ITS are highlighted in this chapter. These issues include unreliable delivery, the dynamic topology of IoV, routing overhead, scalability, and energy, to name a few. The issues can be considered as future research directions for a promising intelligent transportation system. Machine learning (ML) is a promising discipline of Artificial Intelligence (AI) to train the CR-based IoV system so that it can make decisions for improved spectrum utilization. The ML-enabled IoV systems can better adapt to the dynamically changing environment. Section 2 covers the applications of ML techniques to the CR-IoV systems and highlights their issues and challenges. Section 3 covers the examination of ML in conjunction with Data Science applications which further widens the scope of the readership. In CR-IoV, ML and Data Science can be collaboratively used to further enhance road safety through inter-vehicle, intra-vehicle, and beyond-vehicle networks.The channel switching and routing overhead is an important issue in CR-based IoVs. To minimize the channel switching and routing overheads, an effective scheme has been presented in Section 4 to discuss the promising solutions and performance analysis. Meanwhile, IoV communication is a highly time-sensitive application that requires that the vehicles should be synchronized. The time synchronization in IoVs has been highlighted in Section 5 to elaborate further on the critical metrics, challenges, and advancements in synchronization of IoVs. As the vehicles exchange data using wireless channels, they are at risk of being exposed to various security threats. The eavesdropping, identity exposure, message tampering, or sinkhole attack to name a few. It needs time to discuss the security issues and their countermeasures to make the CR-IoV attack resilient. The last section of the book highlights the security issues and maintaining the quality of service (QoS) of the CR-based IoVs which concludes the book.Key featuresThe architecture and applications of Intelligent Transportation System (ITS) in CR-IoVs.The overview of ML techniques and their applications in CR-IoVs.The ML applications in conjunction with Data Science in CR-IoVs.A minimized channel switching and routing (MCSR) technique to improve the performance of CR-IoVs.Data Science applications and approaches to improve the inter and intra-vehicle communications in CR-IoVs.The classification of security threats and their countermeasures in CR-IoVs.The QoS parameters and their impact on the performance of the CR-IoV ecosystem.The targeted audience of this book can be undergraduate and graduate-level students, researchers, scientists, academicians, and professionals in the industry. This book will greatly help the readers to understand the application scenarios, the issues and challenges, and the possible solutions. All the chapters highlight the future research directions that can be taken as research topics for future research.

The incorporation of Cognitive Radio (CR) into the Internet of Vehicles (IoV) has emerged as the Intelligent Transportation System (ITS). Section 1 covers the aspects of cognitive radio when it provides support to IoV. The challenges which limit the performance of ITS are highlighted in this chapter. These issues include unreliable delivery, the dynamic topology of IoV, routing overhead, scalability, and energy, to name a few. The issues can be considered as future research directions for a promising intelligent transportation system. Machine learning (ML) is a promising discipline of Artificial Intelligence (AI) to train the CR-based IoV system so that it can make decisions for improved spectrum utilization. The ML-enabled IoV systems can better adapt to the dynamically changing environment. Section 2 covers the applications of ML techniques to the CR-IoV systems and highlights their issues and challenges. Section 3 covers the examination of ML in conjunction with Data Science applications which further widens the scope of the readership. In CR-IoV, ML and Data Science can be collaboratively used to further enhance road safety through inter-vehicle, intra-vehicle, and beyond-vehicle networks.The channel switching and routing overhead is an important issue in CR-based IoVs. To minimize the channel switching and routing overheads, an effective scheme has been presented in Section 4 to discuss the promising solutions and performance analysis. Meanwhile, IoV communication is a highly time-sensitive application that requires that the vehicles should be synchronized. The time synchronization in IoVs has been highlighted in Section 5 to elaborate further on the critical metrics, challenges, and advancements in synchronization of IoVs. As the vehicles exchange data using wireless channels, they are at risk of being exposed to various security threats. The eavesdropping, identity exposure, message tampering, or sinkhole attack to name a few. It needs time to discuss the security issues and their countermeasures to make the CR-IoV attack resilient. The last section of the book highlights the security issues and maintaining the quality of service (QoS) of the CR-based IoVs which concludes the book.Key featuresThe architecture and applications of Intelligent Transportation System (ITS) in CR-IoVs.The overview of ML techniques and their applications in CR-IoVs.The ML applications in conjunction with Data Science in CR-IoVs.A minimized channel switching and routing (MCSR) technique to improve the performance of CR-IoVs.Data Science applications and approaches to improve the inter and intra-vehicle communications in CR-IoVs.The classification of security threats and their countermeasures in CR-IoVs.The QoS parameters and their impact on the performance of the CR-IoV ecosystem.The targeted audience of this book can be undergraduate and graduate-level students, researchers, scientists, academicians, and professionals in the industry. This book will greatly help the readers to understand the application scenarios, the issues and challenges, and the possible solutions. All the chapters highlight the future research directions that can be taken as research topics for future research.

This book offers a timely introduction to the rapidly evolving role of artificial intelligence in modern healthcare. Designed for a broad audience, including students, practitioners, researchers, and librarians, it provides clear explanations and a solid conceptual foundation for understanding current advances and anticipating future breakthroughs in healthcare AI. A central focus of the book is the transformative rise of large language models (LLMs). These models now influence clinical documentation, decision support, knowledge extraction, and patient communication. While showcasing their potential to enhance healthcare delivery, the book also examines the concerns they raise, including ethical risks, transparency, fairness, and the significant resources required to deploy them safely.Bringing together contributions from international experts, the book offers a comprehensive overview of how LLMs are being applied across medical imaging, diagnostics, multimodal data analysis, and clinical workflows. It also highlights the practical challenges of fine-tuning and integrating these systems into real-world clinical environments.Importantly, the book provides thought-provoking insights on trust, accountability, and responsible AI, making it an essential reference for anyone seeking to navigate the opportunities and challenges of LLM-driven healthcare.Key Features:· A timely comprehensive coverage of LLMs in healthcare.· Contributions from a global network of authors from ten different countries.· Insightful discussions on ethics, trust, and hallucinations in LLMs.· Real world case studies and fills the gap between technical AI research and implementation.· Value for a broad audience, serving as an essential resource for researchers, clinicians, policymakers, and advanced students.

With the increasing worldwide trend in population migration into urban centers, we are beginning to see the emergence of the kinds of mega-cities which were once the stuff of science fiction. It is clear to most urban planners and developers that accommodating the needs of the tens of millions of inhabitants of those megalopolises in an orderly and uninterrupted manner will require the seamless integration of and real-time monitoring and response services for public utilities and transportation systems. Part speculative look into the future of the world’s urban centers, part technical blueprint, this visionary book helps lay the groundwork for the communication networks and services on which tomorrow’s “smart cities” will run.Written by a uniquely well-qualified author team, this book provides detailed insights into the technical requirements for the wireless sensor and actuator networks required to make smart cities a reality.

Emerging Communication Technologies Based on Wireless Sensor Networks: Current Research and Future Applications fills a gap in the existing literature by combining a plethora of WSN-based emerging technologies into a single source so that researchers can form opinions regarding these technologies. It presents different types of emerging communication technologies based on WSNs and describes how wireless sensor networks can be integrated with other communication technologies. It covers many of the new techniques and demonstrates the application of WSNs. The book’s 14 chapters are divided into four parts.The first part covers the basics of wireless sensor networks and their principal working methods. The authors then move on to discuss different types of WSNs, characteristics of different types of emerging technologies based on WSNs, renewable energy sources, battery replenishment strategies, and application-specific energy challenges of WSNs. The second part is dedicated to issues related to wireless body area networks (WBANs). It discusses wearable WSNs and their applications, standards, and research trends. The authors also discuss routing schemes devised for WBANs and thermal-aware routing protocols for WBANs.The third part focuses on different emerging communication technologies based on WSNs, including electromagnetic wireless nanosensor networks, WSNs in the IoT, management of WSNs through satellite networks, WSNs in smart homes, and cognitive radio technology in conjunction with WSNs. The last part of the book covers topics generally related to typical WSNs, including energy-efficient data collection in WSNs, key distribution mechanisms in WSNs, distributed data gathering algorithms for mobile WSNs, and finally, a novel mobility scheme for WSNs that supports IPv6.

This book provides an overview of the latest research and development of new technologies for cognitive radio, mobile communications, and wireless networks. The contributors discuss the research and requirement analysis and initial standardization work towards 5G cellular systems and the capacity problems it presents. They show how cognitive radio, with the capability to flexibly adapt its parameters, has been proposed as the enabling technology for unlicensed secondary users to dynamically access the licensed spectrum owned by legacy primary users on a negotiated or an opportunistic basis. They go on to show how cognitive radio is now perceived in a much broader paradigm that will contribute to solve the resource allocation problem that 5G requirements raise. The chapters represent hand-selected expanded papers from EAI sponsored and hosted conferences such as the 12th EAI International Conference on Mobile and Ubiquitous Systems, the 11th EAI International Conference on HeterogeneousNetworking for Quality, Reliability, Security and Robustness, the 10th International Conference on Cognitive Radio Oriented Wireless Networks, the 8th International Conference on Mobile Multimedia Communications, and the EAI International Conference on Software Defined Wireless Networks and Cognitive Technologies for IoT.

This book provides extensive insights on blockchain systems, starting from a historical perspective and moving towards building foundational knowledge, with focus on communication networks.

This book provides extensive insights on blockchain systems, starting from a historical perspective and moving towards building foundational knowledge, with focus on communication networks.

It covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data.

It covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data.