Description
5G IoT and Edge Computing for Smart Healthcare addresses the importance of a 5G IoT and Edge-Cognitive-Computing-based system for the successful implementation and realization of a smart-healthcare system. The book provides insights on 5G technologies, along with intelligent processing algorithms/processors that have been adopted for processing the medical data that would assist in addressing the challenges in computer-aided diagnosis and clinical risk analysis on a real-time basis. Each chapter is self-sufficient, solving real-time problems through novel approaches that help the audience acquire the right knowledge.With the progressive development of medical and communication - computer technologies, the healthcare system has seen a tremendous opportunity to support the demand of today's new requirements.- Focuses on the advancement of 5G in terms of its security and privacy aspects, which is very important in health care systems- Address advancements in signal processing and, more specifically, the cognitive computing algorithm to make the system more real-time- Gives insights into various information-processing models and the architecture of layers to realize a 5G based smart health care system
Table of Contents
1. Fundamentals and architecture of edge computing platform: 5G IoT/IoMT2. Physical layer architecture of 5G enabled IoT/IoMT system3. HetNet/M2M/D2D communication in 5G technologies4. Sensor Networks: Data and Traffic Models in 5G Network5. Convergent Network Architecture of 5G and MEC6. Privacy and Security aspect of MEC enabled 5G- IoT network7. Healthcare data encryption, data processing for the data acquired from smart sensors and smart city healthcare approaches8. Artificial Neural Networks/ Deep Learning approaches for the disease diagnosis and treatment9. Advanced pattern recognition tools/ computer vision algorithm for the disease diagnosis10. Cognitive computing for the data cognition for the information relevant to the user's disease and resource management11. Computational Intelligence in Human-machine interface (HMI) for telemedicine application12. Case Study: challenges and implications of smart healthcare applications and solutions to address these challenges
