This book provides an essential overview of IoT, energy-efficient topology control protocols, motivation, and challenges for topology control for Wireless Sensor Networks, and the scope of the research in the domain of IoT. Further, it discusses the different design issues of topology control and energy models for IoT applications, different types of simulators with their advantages and disadvantages. It also discusses extensive simulation results and comparative analysis for various algorithms. The key point of this book is to present a solution to minimize energy and extend the lifetime of IoT networks using optimization methods to improve the performance. Features: Describes various facets necessary for energy optimization in IoT domain. Covers all aspects to achieve energy optimization using latest technologies and algorithms, in wireless sensor networks. Presents various IoT and Topology Control Methods and protocols, various network models, and model simulation using MATLAB (R). Reviews methods and results of optimization with Simulation Hardware architecture leading to prolonged life of IoT networks. First time introduces bio-inspired algorithms in the IoT domain for performance optimization This book aims at Graduate Students, Researchers in Information Technology, Computer Science and Engineering, Electronics and Communication Engineering.

Currently, informatics within the field of public health is a developing and growing industry. Clinical informatics are used in direct patient care by supplying medical practitioners with information that can be used to develop a care plan. Intelligent applications in clinical informatics facilitates with the technology-based solutions to analyze data or medical images and help clinicians to retrieve that information. Decision models aid with making complex decisions especially in uncertain situations. The Handbook of Research on Applied Intelligence for Health and Clinical Informatics is a comprehensive reference book that focuses on the study of resources and methods for the management of healthcare infrastructure and information. This book provides insights on how applied intelligence with deep learning, experiential learning, and more will impact healthcare and clinical information processing. The content explores the representation, processing, and communication of clinical information in natural and engineered systems. This book covers a range of topics including applied intelligence, medical imaging, telehealth, and decision support systems, and also looks at technologies and tools used in the detection and diagnosis of medical conditions such as cancers, diabetes, heart disease, lung disease, and prenatal syndromes. It is an essential reference source for diagnosticians, medical professionals, imaging specialists, data specialists, IT consultants, medical technologists, academicians, researchers, industrial experts, scientists, and students.