Healthcare 4.0 is a recent e-health paradigm associated with the concept of Industry 4.0. It provides approaches to achieving precision medicine that delivers healthcare services based on the patient’s characteristics. Moreover, Healthcare 4.0 enables telemedicine, including telesurgery, early predictions, and diagnosis of diseases. This represents an important paradigm for modern societies, especially with the current situation of pandemics. The release of the fifth-generation cellular system (5G), the current advances in wearable device manufacturing, and the recent technologies, e.g., artificial intelligence (AI), edge computing, and the Internet of Things (IoT), are the main drivers of evolutions of Healthcare 4.0 systems. To this end, this work considers introducing recent advances, trends, and requirements of the Internet of Medical Things (IoMT) and Healthcare 4.0 systems. The ultimate requirements of such networks in the era of 5G and next-generation networks are discussed. Moreover, the design challenges and current research directions of these networks. The key enabling technologies of such systems, including AI and distributed edge computing, are discussed.

Designing Internet of Things (IoT) applications faces many challenges including security, massive traffic, high availability, high reliability and energy constraints. Recent distributed computing paradigms, such as Fog and multi-access edge computing (MEC), software-defined networking (SDN), network virtualization and blockchain can be exploited in IoT networks, either combined or individually, to overcome the aforementioned challenges while maintaining system performance. In this paper, we present a framework for IoT that employs an edge computing layer of Fog nodes controlled and managed by an SDN network to achieve high reliability and availability for latency-sensitive IoT applications. The SDN network is equipped with distributed controllers and distributed resource constrained OpenFlow switches. Blockchain is used to ensure decentralization in a trustful manner. Additionally, a data offloading algorithm is developed to allocate various processing and computing tasks to the OpenFlow switches based on their current workload. Moreover, a traffic model is proposed to model and analyze the traffic indifferent parts of the network. The proposed algorithm is evaluated in simulation and in a testbed. Experimental results show that the proposed framework achieves higher efficiency in terms of latency and resource utilization.

Colon cancer is the second most common cause of cancer death in women and the third most common cause of cancer death in men. Therefore, early detection of this cancer can lead to lower infection and death rates. In this research, we propose a new lightweight deep learning approach based on a Convolutional Neural Network (CNN) for efficient colon cancer detection. In our method, the input histopathological images are normalized before feeding them into our CNN model, and then colon cancer detection is performed. The efficiency of the proposed system is analyzed with publicly available histopathological images database and compared with the state-of-the-art existing methods for colon cancer detection. The result analysis demonstrates that the proposed deep model for colon cancer detection provides a higher accuracy of 99.50%, which is considered the best accuracy compared with the majority of other deep learning approaches. Because of this high result, the proposed approach is computationally efficient.

Sixth-generation (6G) wireless networks have the potential to transform global connectivity by supporting ultra-high data rates, ultra-reliable low latency communication (uRLLC), and intelligent, adaptive networking. To realize this vision, 6G must incorporate groundbreaking technologies that enhance network efficiency, spectral utilization, and dynamic adaptability. Among them, unmanned aerial vehicles (UAVs), terahertz (THz) communication, and intelligent reconfigurable surfaces (IRSs) are three major enablers in redefining the architecture and performance of next-generation wireless systems. This survey provides a comprehensive review of these transformative technologies, exploring their potential, design challenges, and integration into future 6G ecosystems. UAV-based communication provides flexible, on-demand communication in remote, harsh areas and is a vital solution for disasters, self-driving, and industrial automation. THz communication taking place in the 0.1–10 THz band reveals ultra-high bandwidth capable of a data rate of multi-gigabits per second and can avoid spectrum bottlenecks in conventional bands. IRS technology based on programmable metasurface allows real-time wavefront control, maximizing signal propagation and spectral/energy efficiency in complex settings. The work provides architectural evolution, active current research trends, and practical issues in applying these technologies, including their potential contribution to the creation of intelligent, ultra-connected 6G networks. In addition, it presents open research questions, possible answers, and future directions and provides information for academia, industry, and policymakers.

Lung cancer, which claims 1.8 million lives annually, is still one of the leading causes of cancer-related deaths globally. Patients with lung cancer frequently have a bad prognosis because of late-stage detection, which severely limits treatment options and decreases survival rates. Early detection is essential for better outcomes, but traditional CT image analysis is time-consuming, prone to error, and relies on subjective judgments. To overcome these issues, we propose a custom convolutional neural network (CNN) combined with explainable AI (XAI) techniques, particularly gradient-weighted class activation mapping (Grad-CAM). This approach is intended to reliably classify lung cancer into squamous cell carcinoma, large cell carcinoma, or adenocarcinoma. Unlike conventional methods, our approach not only achieves highly accurate classification of lung cancer subtypes but also incorporates clinically validated interpretability features to ensure alignment with medical diagnostics. Our model trained on a comprehensive dataset of CT images achieved an overall accuracy of 93.06%. This performance demonstrates the model’s robustness in detecting even subtle malignancies, with strong precision, recall, and F1-scores across all cancer types. Including interpretable Grad-CAM visualizations ensures reliability and transparency, aiding clinicians in understanding the model’s predictions. This innovative method demonstrates the potential to revolutionize early lung cancer detection and improve patient survival rates by combining state-of-the-art accuracy with explainability tailored for clinical application.