# Smart Healthcare Network Management: A Comprehensive Review
## 6 Architecture Implementation Plan
### Supporting the Architecture with Software-Defined Networking and Deep Learning  
Providing reliable, timely, secure, cost-effective, and pervasive smart healthcare is an essential requirement of the healthcare industry nowadays. These requirements are driven by the increasing number of patients with chronic diseases, the increasing average life expectancy, and associated healthcare spending. Considerable research is being conducted to tackle these issues and provide practical solutions based on the current Internet architecture and the deployed technologies. However, common challenges related to network and device heterogeneity, complexity, and the lack of full control of the underlying network infrastructure hinder the achievement of desirable end-to-end healthcare-specific requirements. In addition, the massive amount of data generated from the various sensors will make it extremely difficult to use traditional management techniques and tools for fault management, performance analysis, traffic management, and real-time response to any possible malfunction of the software or hardware components. Therefore, we mention two promising technologies that will add value to the architecture and help address the above challenges, namely, software-defined networking (SDN) and deep learning. SDN is an emerging networking paradigm that helps in facilitating network configuration and management [102]. Unlike traditional network configurations used in the current Internet architecture, where network devices implement a control plane and a data-forwarding plane, SDN has two distinct features: SDN separates the control plane from the data plane while enabling programmatic control for network application development. Through this design, SDN aims to achieve more efficient configuration, enhanced performance, and flexible adaptation to emerging network innovations [103]. Four main principles define this SDN architecture:  
* Flow-based forwarding rather than traditional destination-based forwarding. * Externalized control logic, handled by an SDN controller located outside the forwarding devices. * Programmable network elements, managed by software applications running on the controller. * Decoupled control and data planes, allowing independent yet coordinated operation. SDN plays a crucial role in predictively allocating network resources and preventing performance bottlenecks in healthcare applications. The SDN controller dynamically prioritizes critical health data transmissions, such as real-time patient monitoring and emergency response alerts, ensuring preferential network bandwidth for life-saving applications over non-urgent traffic. This proactive approach improves network reliability, reduces congestion, and enhances the overall efficiency of healthcare services [103]. Deep learning (DL) has emerged recently as a powerful tool for machine learning capable of solving complex optimization and predictive analytics problems. It is contributing to solving significant problems that previous attempts by the artificial intelligence community failed to solve for many years. It enables multi-layered computational models to learn abstract representations of healthcare data, providing enhanced fault detection, anomaly prediction, and network optimization. Artificial intelligence techniques have already been explored for fault management in computer networks, where neural networks have been applied for alarm filtering and correlation [104]. DL is also widely used in healthcare analytics and bioinformatics [48; 49], demonstrating its potential in predictive network management. DL-based predictive models continuously analyze network performance metrics to anticipate failures, allowing for preemptive intervention. We also envisage the use of deep learning by SDN controller applications. For example, deep learning-based applications can be used to control the switching, intelligent routing, and firewalling behavior of the overall healthcare infrastructure.