Smart health-care is undergoing rapid transformation from the conventional specialist and hospital-focused style to a distributed patient-focused manner. Several technological developments have encouraged this rapid revolution of health-care vertical. Currently, 4G and other communication standards are used in health-care for smart health-care services and applications. These technologies are crucial for the evolution of future smart health-care services. With the growth in the health-care industry, several applications are expected to produce a massive amount of data in different format and size. Such immense and diverse data needs special treatment concerning the end-to-end delay, bandwidth, latency and other attributes. It is difficult for current communication technologies to fulfil the requirements of highly dynamic and time-sensitive health care applications of the future. Therefore, the 5G networks are being designed and developed to tackle the diverse communication needs of health-care applications in Internet of Things (IoT). 5G assisted smart health-care networks are an amalgamation of IoT devices that require improved network performance and enhanced cellular coverage. Current connectivity solutions for IoT face challenges, such as the support for a massive number of devices, standardisation, energy-efficiency, device density, and security. In this paper, we present a comprehensive review of 5G assisted smart health-care solutions in IoT. We present a structure for smart health-care in 5G by categorizing and classifying existing literature. We also present key requirements for successful deployment of smart health-care systems for certain scenarios in 5G. Finally, we discuss several open issues and research challenges in 5G smart health-care solutions in IoT.

As the world pushes toward the use of greener technology and minimizes energy waste, energy efficiency in the wireless network has become more critical than ever. The next-generation networks, such as 5G, are being designed to improve energy efficiency and thus constitute a critical aspect of research and network design. The 5G network is expected to deliver a wide range of services that includes enhanced mobile broadband, massive machine-type communication and ultra-reliability, and low latency. To realize such a diverse set of requirement, 5G network has evolved as a multi-layer network that uses various technological advances to offer an extensive range of wireless services. Several technologies, such as software-defined networking, network function virtualization, edge computing, cloud computing, and small cells, are being integrated into the 5G networks to fulfill the need for diverse requirements. Such a complex network design is going to result in increased power consumption; therefore, energy efficiency becomes of utmost importance. To assist in the task of achieving energy efficiency in the network machine learning technique could play a significant role and hence gained significant interest from the research community. In this paper, we review the state-of-art application of machine learning techniques in the 5G network to enable energy efficiency at the access, edge, and core network. Based on the review, we present a taxonomy of machine learning applications in 5G networks for improving energy efficiency. We discuss several issues that can be solved using machine learning regarding energy efficiency in 5G networks. Finally, we discuss various challenges that need to be addressed to realize the full potential of machine learning to improve energy efficiency in the 5G networks. The survey presents a broad range of ideas related to machine learning in 5G that addresses the issue of energy efficiency in virtualization, resource optimization, power allocation, and incorporating enabling technologies of 5G can enhance energy efficiency.

Until now, every evolution of communication standard was driven by the need for providing high-speed connectivity to the end-user. However, 5G marks a radical shift from this focus as 5G and beyond networks are being designed to be future-proof by catering to diverse requirements of several use cases. These requirements include Ultra-Reliable Low Latency Communications, Massive Machine-Type Communications and Enhanced Mobile Broadband. To realize such features in 5G and beyond, there is a need to rethink how current cellular networks are deployed because designing new radio access technologies and utilizing the new spectrum are not enough. Several technologies, such as software-defined networking, network function virtualization, machine learning and cloud computing, are being integrated into the 5G networks to fulfil the need for diverse requirements. These technologies, however, give rise to several challenges associated with decentralization, transparency, interoperability, privacy and security. To address these issues, Blockchain has emerged as a potential solution due to its capabilities such as transparency, data encryption, auditability, immutability and distributed architecture. In this paper, we review the state-of-art application of Blockchain in 5G network and explore how it can facilitate enabling technologies of 5G and beyond to enable various services at the front-haul, edge and the core. Based on the review, we present a taxonomy of Blockchain application in 5G networks and discuss several issues that can be solved using Blockchain integration. We then present various field-trials and Proof of concept that are using Blockchain to address the challenges faced in the current 5G deployment. Finally, we discuss various challenges that need to be addressed to realize the full potential of Blockchain in beyond 5G networks. The survey presents a broad range of ideas related to Blockchain integration in 5G and beyond networks that address issues such as interoperability, security, mobility, resource allocation, resource sharing and management, energy efficiency and other desirable features.

With the ongoing efforts for widespread Internet of Things (IoT) adoption, one of the key factors hindering the wide acceptance of IoT is security. Securing IoT networks such as the electric power grid or water supply systems has emerged as a major national and global priority. To address the security issue of IoT, several studies are being carried out that involve the use of, but are not limited to, blockchain, artificial intelligence, and edge/fog computing. Authentication and authorization are crucial aspects of the CIA triad to protect the network from malicious parties. However, existing authorization and authentication schemes are not sufficient for handling security, due to the scale of the IoT networks and the resource-constrained nature of devices. In order to overcome challenges due to various constraints of IoT networks, there is a significant interest in using machine learning techniques to assist in the authentication and authorization process for IoT. In this paper, recent advances in authentication and authorization techniques for IoT networks are reviewed. Based on the review, we present a taxonomy of authentication and authorization schemes in IoT focusing on machine learning-based schemes. Using the presented taxonomy, a thorough analysis is provided of the authentication and authorization (AA) security threats and challenges for IoT. Furthermore, various criteria to achieve a high degree of AA resiliency in IoT implementations to enhance IoT security are evaluated. Lastly, a detailed discussion on open issues, challenges, and future research directions is presented for enabling secure communication among IoT nodes.

The global surge of connected devices and multimedia services necessitates increased capacity and coverage of communication networks. One approach to address the unprecedented rise in capacity and coverage requirement is deploying several small cells to create ultra-dense networks. This, however, exacerbates problems with energy consumption and network management due to the density and unplanned nature of the deployment. This review discusses various approaches to solving energy efficiency problems in ultra-dense networks, ranging from deployment to optimisation. Based on the review, we propose a taxonomy, summarise key findings, and discuss operational and implementation details of past research contributions. In particular, we focus on popular approaches such as machine learning, game theory, stochastic and heuristic techniques in the ultra-dense network from an energy perspective due to their promise in addressing the issue in future networks. Furthermore, we identify several challenges for improving energy efficiency in an ultra-dense network. Finally, future research directions are outlined for improving energy efficiency in ultra-dense networks in 5G and beyond 5G networks.

Due to the rise of connected devices, a decentralised, patient-centred paradigm is being adopted in healthcare as an alternative to the traditional hospital and specialist-focused approach. As the healthcare sector expands, more applications will be connected to the network, producing data of various shapes and sizes that will allow for customised and remote healthcare services. Future intelligent healthcare will include a combination of 6G and the Internet of Things (IoT) that will address current limitations related to cellular coverage, network performance and security issues. This paper discusses and sheds light on prospects, associated challenges and future directions.