Next-generation wireless network applications that combine the Internet of Things (IoT), intelligent edges, and connectivity technologies will benefit every area, including e-health and medical Internet of Things (M-IoT). Fifth-generation (5G) mobile network technology cannot match the requirements of emerging mobile apps, which require extreme communication speed, network intelligence, ultra-low latency, comprehensive connectivity, and the capacity to manage diversely related usages. The sixth-generation (6G) mobile network technology establishes new performance standards that the fifth-generation (5G) mobile network technology could not satisfy. For incredibly immersive applications such as 3D communications and enormous virtual reality (VR)/ extended reality (XR) applications to be economically viable, 6G capabilities must be delivered at a large scale. Deploying several tiny cells to construct ultra-dense networks (UDN) is one option for tackling the extraordinary rise in capacity and coverage needs. The proposed work estimates that only the future 6G networks can deliver a high-performance connection with many connected devices, particularly in challenging situations such as diverse mobility, energetic environments, and extreme density. Accordingly, this article discusses the most current and forthcoming 6G network-compatible advancements to comprehensively review 6G mobile communication technologies in single survey research. At the outset, we thoroughly overview UDN and the 6G system's goals, motivations, requirements, architecture, and conceptual parts.

So far, the communication standard development requires specific parameters to achieve the requests of the desired application, most frequently, the connection speed rate. On the other hand, the term Beyond the Fifth Generation (B5G) symbolizes certain specifications required to succeed the future-proof of the Fifth Generation (5G), i.e., the predicted high-level parameters, such as Ultra-reliable low-latency communications, massive machine-type communications, and improved mobile broadband, which are essential for the expected high-level future applications; consequently, 5G wireless (cellular) networks must be reconsidered precisely and in-depth to cope with the applications’ required high-level standard parameters in B5G. Therefore, it is crucial to develop novel wireless access configurations and technologies that utilize additional spectrum. However, this alone is not sufficient for now. Incorporating technologies such as software-defined networking, cloud computing, machine learning, 3D networking, and network function virtualization into B5G networks is imperative due to raised concerns regarding decentralization, transparency, interoperability, privacy, and security. This page provides a comprehensive overview of B5G’s design, functionality, and security, as well as its relationship to cloud computing. Furthermore, the proposed study examines the techniques employed for data transmission in B5G applications, such as V2V, D2D, and M2M transmissions. Lastly, the proposed study focuses on essential technology-based software services, such as healthcare, Smart Grid, tourism, and agricultural services. These services use the advantages of B5G communication networks and cloud computing. So, the proposed work collects all the necessary information for researches and developers in one article, supported by the most up-to-date references.

Networked cameras that continuously capture video data have generated a high demand for hybrid edge-to-cloud servers that can process live videos in real time. The environment of art museums is rarely studied, but visual analysis is an important factor in categorizing and distinguishing individuals and crowds through smart surveillance systems. This paper demonstrates how video surveillance data from art museums can be analyzed to identify abnormal behavior using an innovative deep learning framework. To enhance the extracted features, a spatial feature extraction method based on the U-Net architecture is applied, along with the encoder component of the proposed approach, MobileNetV2. Additionally, we propose an improved Long-Short-Term Memory (LSTM) algorithm for extracting temporal features. Optical flow enhances surveillance in art museums by tracking individuals and crowds. Our approach yields an average accuracy of 97.67±1.23% when applied to a collection of video datasets. Using U-Net, MobileNetV2, and optimized LSTM algorithms, the model recognizes patterns in video data, such as crowd motion in museums. Consequently, this methodology generates reliable results as well as being computationally efficient. Compared to the state-of-the-art, the proposed method is more comprehensive and generalizable for analyzing atypical museum visitor behavior.