## 5 Key Enabling Technologies of Healthcare 4.0
### Related Work
Syneos Health, and USAID, to conduct analyses and make estimates about the use of AI-driven biosensors in diagnosis, surveillance, and prevention during the COVID-19 pandemic. COVID-19 prevention, screening, and treatment have all benefitted from AI-powered diagnostic technologies. In [103], the authors built an e-health model with multiple patient agent software instances with three layers: sensing, close processing, and remote processing. They described a possible 5G device configuration for the patient agent. The dedicated patient agent app could control the resources of individual 5G network slices. An investigation of the new e-health app's performance indicated that it would use blockchain technology to handle health data in near-real time. The research in [104] provided a road map for introducing IoT and AI into healthcare. The study investigates the future of healthcare and how AI would be combined with sensor-enabled IoT-based medical equipment. The groundwork, for future healthcare services, has been laid through a review of the literature on AI, ML, utilizing smart systems, and IoT in healthcare. The authors explained how a network of medical professionals might remotely monitor patients using wearables supported by the IoT and AI. The research stated that seamless integration between patients, doctors, and other healthcare professionals allows for timely, convenient healthcare delivery. The authors investigated how future smart healthcare technologies can facilitate cooperation amongst network partners in delivering continuous patient monitoring and care. A comprehensive framework was provided that aids medical professionals in delivering top-notch treatment to their patients. However, the study focused on the medical field. In [105], the authors proposed a fog-computing architecture for processing sensor data that makes efficient use of shared resources. The model was built using publish/subscribe and distributed hash tables (DHT). The authors recommended architecture for efficient sharing and use of resources. The issues with processing data in the cloud have been fixed by introducing fog computing to Healthcare 4.0. Publish/subscribe and peer-to-peer (P2P) overlays are used in the proposed framework to satisfy the requirement for fast node discovery. End-to-end security solutions are necessary to prevent and regulate health data breaches because of the sensitive nature of this data. Several authentication and authorization systems can protect private information gathered by IoT devices in Healthcare 4.0. The transport layer security (TLS) protocol aims to make information transmission more secure. Using this protocol, a user can avoid the issue of communications getting lost or switched around. A tricky flaw in the datagram transport layer security (DTLS) protocol is that an attacker might flood a server with ClientHello messages. With each new ClientHello message, the denial-of-service (DoS) attacker can gain more bandwidth and resources by establishing a new connection to the server. The authors in [106] developed an intelligent gateway-based authentication and authorization model to solve these issues. The proposed model keeps attackers and malicious users from accessing important physiological data. The authors used the Contiki Network Simulator to demonstrate the improved DTLS based on smart gateways. The packet loss ratio was used as a performance measure for the introduced work. The enhanced DTLS's efficacy was evaluated by timing data transfers and handshakes. The meaning of fog computing in the context of Healthcare 4.0 was examined in [107]. Fog computing has proven to be a successful answer to various healthcare concerns. The research used a framework that compared various healthcare technologies against present technology. Issues and challenges in healthcare have been examined, as well as health-related technologies. Fog computing has been regarded as a well-designed data processing solution that combines the benefits of the cloud.