Bøger af Agbotiname Lucky Imoize

The book covers the exploitation of computational models for effectively developing and managing large-scale wireless communication systems. The goal is to create and establish computational models for seamless human interaction and efficient decision-making in beyond 5G wireless systems.Computational Modeling and Simulation of Advanced Wireless Communication Systems looks to create and establish computational models for seamless human interaction and efficient decision-making in the beyond 5G wireless systems. This book presents the design and development of several computational modeling techniques and their applications in wireless communication systems. The book examines shortcomings and limitations of the existing computational models and offers solutions to revamp the traditional architecture toward addressing the vast network issues in wireless systems. The book addresses the need to design efficient computational and simulation models to address several issues, such as interference, pathloss, delay, traffic outage, etc., in wireless communication systems. It discusses how theoretical, mathematical and experimental results are integrated for optimal system performance to enhance the quality of service for mobile subscribers.The book is intended for industry and academic researchers, scientists, and engineers in the fields of ICTs it is structured to present a practical guide to wireless communication engineers, IT practitioners, and students.

Federated Learning for Digital Healthcare Systems critically examines the key factors that contribute to the problem of applying machine learning in healthcare systems and investigates how federated learning can be employed to address the problem. The book discusses, examines, and compares the applications of federated learning solutions in emerging digital healthcare systems, providing a critical look in terms of the required resources, computational complexity, and system performance. In the first section, chapters examine how to address critical security and privacy concerns and how to revamp existing machine learning models. In subsequent chapters, the book's authors review recent advances to tackle emerging efficient and lightweight algorithms and protocols to reduce computational overheads and communication costs in wireless healthcare systems. Consideration is also given to government and economic regulations as well as legal considerations when federated learning is applied to digital healthcare systems.

This edited book discusses the technical considerations, potential opportunities and critical challenges of AI and blockchain in telehealth systems and presents case studies and critical lessons to consider when designing future AI and blockchain-based telehealth systems which have privacy and security in mind.

The book proposes solutions to revamp traditional security architecture by addressing critical security challenges in commercialized 5G and envisioned 6G wireless communication systems. New insights into real-world scenarios for the deployment, applications and management of robust, secure, and efficient security schemes are discussed.

The book discusses how Unmanned Aerial Vehicles (UAVs) can leverage the sub-6 GHz massive MIMO to address cell selection and interference issues in future wireless networks. The book takes a close look at utilizing UAVs to achieving direct and efficient device-to device (D2D) communications in the sky. Also, the key 6G enablers (cell-free architectures, artificial intelligence, reconfigurable intelligent surfaces, THz communications, and non-terrestrial networks) for UAV communication are broached, and the primary technological challenges of each enabler are discussed extensively. Furthermore, the book covers the design of adaptable UAVs to operate in diverse and harsh environmental conditions. Additionally, the existing UAVs' networking protocols and how these can be greatly enhanced to address the issue of intermittent network changes and channel impairments are discussed. The prospects and societal benefits envisioned in future UAVs are also presented.

This edited book gives insights into the deployment, application, management, and benefits of explainable artificial intelligence (XAI) in medical decision support systems (MDSS). The book discusses XAI-based analytics for patient-specific MDSS as well as related security and privacy issues.