The advancement of wireless connectivity in smart cities will enhance connections between their various key elements. Federated intelligent health monitoring systems inside autonomous vehicles will achieve smart cities’ goal of improving the quality of life. This paper proposes a novel cooperative health emergency response system within Cooperative Intelligent Transportation Environment, namely, C-HealthIER. C-HealthIER is a cooperative health intelligent emergency response system that aims to reduce the time of receiving the first emergency treatment for passengers with abnormal health conditions. C-HealthIER continuously monitors passengers’ health and conducts cooperative behavior in response to health emergencies by vehicle-to-vehicle and vehicle-to-infrastructure information sharing to find the nearest treatment provider. A conducted simulation that integrates three different tools (Veins, SUMO, and OMNET++) to simulate the proposed system showed that C-HealthIER reduces the total time to receive the emergency treatment by at least 92.5% and the time to receive the first emergency treatment by at least 73.2% compared to the time taken by AutoPilot mode in self-driving cars. C-HealthIER also reduces the travel distance to the first emergency treatment place by 40.9% and thus reduces the travel time by 43.8% compared to receiving the treatment at the same hospital in the AutoPilot mode.

Recently, IoT devices have become affordable to support various types of applications which have encouraged their usability in data collection, behavior tracking, and pattern analysis to gain knowledge to achieve certain goals. The Internet of Behavior (IoB) allows organizations and individuals to achieve all of this simultaneously. Various technologies and approaches can be used to support IoB systems to operate efficiently, such as 6G networks and decentralized systems structure that support IoT-based systems to distribute operations across devices and influence each device individually. Therefore, this paper proposes a decentralized IoB framework for achieving energy sustainability by tracking, analyzing, and influencing IoT devices’ behavior. The collected results from an extensive decentralized IoB electrical power consumption experiment show that the decentralized system achieved higher accuracy compared to the centralized system, thus sending 3.5% fewer alerts and saving 3.4% more power for 3 sub-meters over a period of 500 hours.

Due to recent privacy trends and the increase in data breaches in various industries, it has become imperative to adopt new technologies that support data privacy, maintain accuracy, and ensure sustainability at the same time. The healthcare industry is one of the most vulnerable sectors to cyberattacks and data breaches as health data are highly sensitive and distributed in nature. The use of IoT devices with machine learning models to monitor the health status has made the challenge more acute, as it increases the distribution of health data and adds a decentralized structure to healthcare systems. A new privacy-preserving technology, namely, federated learning (FL), is promising for such a challenge as implementing solutions that integrate FL with deep learning, for healthcare applications that rely on IoT, provides several benefits by mainly preserving data privacy, building robust and high accuracy models, and dealing with the decentralized structure, thus achieving sustainability. This article proposes a deep FL (DFL) framework for healthcare data monitoring and analysis using IoT devices. Moreover, it proposes an FL algorithm that addresses the local training data acquisition process. Furthermore, it presents an experiment to detect skin diseases using the proposed framework. The extensive results collected show that the DFL models can preserve data privacy without sharing it, maintain the decentralized structure of the system made by IoT devices, improve thearea under the curve(AUC) of the model to reach 97%, and reduce the operational costs (OC) for service providers.

Recent trends in the healthcare industry, such as the use of wearable IoT for continuous health monitoring, are setting new requirements for healthcare systems that boost data analysis. These systems should support decentralization and maintain the privacy and ownership of users' data due to the sensitivity of healthcare data. Therefore, the use of federated learning techniques is recommended for systems that need such requirements. This paper proposes a Deep Federated Learning framework for decentralized healthcare systems that maintain user privacy in a distributed architecture. It also proposes an algorithm for an automated training data acquiring process. Furthermore, it presents an experiment for using deep federated learning in detecting skin diseases and using Transfer Learning to address the problem of limited availability of healthcare data in building deep learning models. The evaluated results show how the federated learning increased the Area Under the Curve of the centralized learning model up to 0.97, as it also shows good model performance during federated rounds in terms of accuracy, precision, recall, and F1-score. Moreover, although the FL system has affected the quality of service to the user in terms of model conversion time, the Federated Learning system meets the requirements of building models in a decentralized manner with no sharing of users' private data.

Recent technological advances have reshaped many aspects of our lives, especially modern transportation systems. For instance, AI and B5G Networks have raised the level of automation as autonomous vehicles (AV) become decision-independent and self-aware. However, in-vehicle health monitoring is still an open issue. Therefore, a cooperative healthcare emergency response framework has been proposed that employs in-vehicle intelligent health monitoring and local networks for AV to minimize the time to receive emergency treatment for passengers with abnormal health conditions. The extensive simulation results show that the framework minimizes the First Emergency Treatment Time by at least 75%, and eliminates hospital waiting time, the Total Time for Emergency Treatment is minimized by at least 93%. Finally, it reduces Travel Time by nearly 50%. All results compared to the autopilot approach.

Since the emergence of digital and smart healthcare, the world has hastened to apply various technologies in this field to promote better health operation and patients’ well being, increase life expectancy, and reduce healthcare costs. One promising technology and game changer in this domain is digital twin (DT). DT is expected to change the concept of digital healthcare and take this field to another level that has never been seen before. DT is a virtual replica of a physical asset that reflects the current status through real-time transformed data. This article proposes and implements an intelligent context-aware healthcare system using the DT framework. This framework is a beneficial contribution to digital healthcare and to improve healthcare operations. Accordingly, an electrocardiogram (ECG) heart rhythms classifier model was built using machine learning to diagnose heart disease and detect heart problems. The implemented models successfully predicted a particular heart condition with high accuracy in different algorithms. The collected results have shown that integrating DT with the healthcare field would improve healthcare processes by bringing patients and healthcare professionals together in an intelligent, comprehensive, and scalable health ecosystem. Also, implementing an ECG classifier that detects heart conditions gives the inspiration for applying ML and artificial intelligence with different human body metrics for continuous monitoring and abnormalities detection. Finally, neural-network-based algorithms deal better with ECG data than traditional ML algorithms.