The Internet of Things (IoT) will be ripe for the deployment of novel machine learning algorithm for both network and application management. However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT. To overcome this challenge, federated learning can be a promising solution that enables on-device machine learning without the need to migrate the private end-user data to a central cloud. In federated learning, only learning model updates are transferred between end-devices and the aggregation server. Although federated learning can offer better privacy preservation than centralized machine learning, it has still privacy concerns. In this paper, first, we present the recent advances of federated learning towards enabling federated learning-powered IoT applications. A set of metrics such as sparsification, robustness, quantization, scalability, security, and privacy, is delineated in order to rigorously evaluate the recent advances. Second, we devise a taxonomy for federated learning over IoT networks. Finally, we present several open research challenges with their possible solutions.

Recent years have disclosed a remarkable proliferation of compute-intensive applications in smart cities. Such applications continuously generate enormous amounts of data which demand strict latency-aware computational processing capabilities. Although edge computing is an appealing technology to compensate for stringent latency related issues, its deployment engenders new challenges. In this survey, we highlight the role of edge computing in realizing the vision of smart cities. First, we analyze the evolution of edge computing paradigms. Subsequently, we critically review the state-of-the-art literature focusing on edge computing applications in smart cities. Later, we categorize and classify the literature by devising a comprehensive and meticulous taxonomy. Furthermore, we identify and discuss key requirements, and enumerate recently reported synergies of edge computing enabled smart cities. Finally, several indispensable open challenges along with their causes and guidelines are discussed, serving as future research directions.

Future wireless services will focus on improving the quality of life by enabling various applications, such as extended reality, brain-computer interaction, and healthcare. These applications will have diverse performance requirements (e.g., user-defined quality of experience metrics, latency, and reliability) which will be challenging to be fulfilled by existing wireless systems. To meet the diverse requirements of the emerging applications, the concept of digital twins has been recently proposed. A digital twin uses a virtual representation along with security-related technologies (e.g., blockchain), communication technologies (e.g., 6G), computing technologies (e.g., edge computing), and machine learning, so as to enable the smart applications. In this tutorial, we present a comprehensive overview on digital twins for wireless systems. First, we present the fundamental concepts (i.e., design aspects, high-level architecture, and frameworks) of digital twins for wireless systems. Second, a comprehensive taxonomy is devised for two aspects, namely, twins for wireless and wireless for twins. For the twins for wireless aspect, we consider issues related to design of twin objects, physical devices, and interface, as well as prototyping, deployment trends, incentive mechanism, isolation of twins, and decoupling. For the wireless for twins aspect, we consider issues related to accessing twin objects, security and privacy, and air interface design are considered. Finally, open research challenges and opportunities are discussed.

Internet of everything (IoE)-based smart services are expected to gain immense popularity in the future, which raises the need for next-generation wireless networks. Although fifth-generation (5G) networks can support various IoE services, they might not be able to completely fulfill the requirements of novel applications. Sixth-generation (6G) wireless systems are envisioned to overcome 5G network limitations. In this article, we explore recent advances made toward enabling 6G systems. We devise a taxonomy based on key enabling technologies, use cases, emerging machine learning schemes, communication technologies, networking technologies, and computing technologies. Furthermore, we identify and discuss open research challenges, such as artificial-intelligence-based adaptive transceivers, intelligent wireless energy harvesting, decentralized and secure business models, intelligent cell-less architecture, and distributed security models. We propose practical guidelines including deep Q-learning and federated learning-based transceivers, blockchain-based secure business models, homomorphic encryption, and distributed-ledger-based authentication schemes to cope with these challenges. Finally, we outline and recommend several future directions.

Fifth-generation (5G) and beyond networks are envisioned to provide multi-services with diverse specifications. Network slicing is identified as a key enabling technology to enable 5G networks with multi-services. Network slicing allows a transition from a network-as-an-infrastructure setup to a network-as-a-service to enable numerous 5G smart services with diverse requirements. Although several surveys and tutorials have discussed network slicing in detail, there is no comprehensive study discussing the taxonomy and requirements of network slicing. In this paper, we present and investigate key recent advances of network slicing towards enabling several Internet of Things (IoT) smart applications. A taxonomy is devised for network slicing using different parameters: key design principles, enablers, slicing resources levels, service function chaining schemes, physical infrastructures, and security. Furthermore, we discuss key requirements for network slicing to enable smart services. Finally, we present several open research challenges along with possible guidelines for network slicing.

Recent trends have shown a widespread increase in the landscape of digital healthcare (i.e., Healthcare 4.0) services, such as personalized healthcare, intelligent rehabilitation, telemedicine, and smart diet management, among others. These healthcare services are based on a variety of diverse requirements. Fulfilling these requirements require proactive intelligent analytics and self-sustainability of networks. Self-sustainability enables the operation of a network with minimum possible interaction from the end-users/network operators, whereas proactive intelligent analytics enables efficient management of resources in response to users’ requests. To enable healthcare 4.0 with proactive online analytics and self-sustainability, one can leverage digital twins. In this article, we present an overview and recent advances of digital twins for healthcare 4.0. An architecture of digital twins for healthcare is also proposed. Furthermore, we present several use cases of digital twins. Finally, we present open research challenges with possible solutions.