In recent years, the concept of the Metaverse has attracted considerable attention. This article provides a comprehensive overview of the Metaverse. First, the development status of the Metaverse is presented. We summarize the policies of various countries, companies, and organizations relevant to the Metaverse, as well as statistics on the number of Metaverse-related publications. Characteristics of the Metaverse are identified: 1) multitechnology convergence; 2) sociality; and 3) hyper-spatio-temporality. For the multitechnology convergence of the Metaverse, we divide the technological framework of the Metaverse into five dimensions. For the sociality of the Metaverse, we focus on the Metaverse as a virtual social world. Regarding the characteristic of hyper-spatio-temporality, we introduce the Metaverse as an open, immersive, and interactive 3-D virtual world which can break through the constraints of time and space in the real world. The challenges of the Metaverse are also discussed.

Federated learning (FL) is a promising framework for distributed machine learning that trains models without sharing local data while protecting privacy. FL exploits the concept of collaborative learning and builds privacy-preserving models. Nevertheless, the integral features of FL are fraught with problems, such as the disclosure of private information, the unreliability of uploading model parameters to the server, the communication cost, etc. Blockchain, as a decentralized technology, is able to improve the performance of FL without requiring a centralized server and also solves the above problems. In this paper, a systematic literature review on the integration of Blockchain in federated learning was considered with the analysis of the existing FL problems that can be compensated. Through carefully screening, most relevant studies are included and research questions cover the potential security and privacy attacks in traditional federated learning that can be solved by blockchain as well as the characteristics of Blockchain-based FL. In addition, the latest Blockchain-based approaches to federated learning have been studied in-depth in terms of security and privacy, records and rewards, and verification and accountability. Furthermore, open issues related to the combination of Blockchain and FL are discussed. Finally, future research directions for the robust development of Blockchain-based FL systems are proposed.

Smart Grids (SGs) are governed by advanced computing, control technologies, and networking infrastructure. However, compromised cybersecurity of the smart grid not only affects the security of existing energy systems but also directly impacts national security. The increasing number of cyberattacks against the smart grid urgently necessitates more robust security protection technologies to maintain the security of the grid system and its operations. The purpose of this review paper is to provide a thorough understanding of the incumbent cyberattacks’ influence on the entire smart grid ecosystem. In this paper, we review the various threats in the smart grid, which have two core domains: the intrinsic vulnerability of the system and the external cyberattacks. Similarly, we analyze the vulnerabilities of all components of the smart grid (hardware, software, and data communication), data management, services and applications, running environment, and evolving and complex smart grids. A structured smart grid architecture and global smart grid cyberattacks with their impact from 2010 to July 2022 are presented. Then, we investigated the the thematic taxonomy of cyberattacks on smart grids to highlight the attack strategies, consequences, and related studies analyzed. In addition, potential cybersecurity solutions to smart grids are explained in the context of the implementation of blockchain and Artificial Intelligence (AI) techniques. Finally, technical future directions based on the analysis are provided against cyberattacks on SGs.

Rapid urbanisation has intensified the need for sustainable solutions to address challenges in urban infrastructure, climate change, and resource constraints. This study reveals that Artificial Intelligence (AI)-enabled metaverse offers transformative potential for developing sustainable smart cities. AI techniques, such as machine learning, deep learning, generative AI (GAI), and large language models (LLMs), enhance the metaverse’s capabilities in data analysis, urban decision making, and personalised user experiences. The study further examines how these advanced AI models facilitate key metaverse technologies such as big data analytics, natural language processing (NLP), computer vision, digital twins, Internet of Things (IoT), Edge AI, and 5G/6G networks. Applications across various smart city domains—environment, mobility, energy, health, governance, and economy, and real-world use cases of virtual cities like Singapore, Seoul, and Lisbon are presented, demonstrating AI’s effectiveness in the metaverse for smart cities. However, AI-enabled metaverse in smart cities presents challenges related to data acquisition and management, privacy, security, interoperability, scalability, and ethical considerations. These challenges’ societal and technological implications are discussed, highlighting the need for robust data governance frameworks and AI ethics guidelines. Future directions emphasise advancing AI model architectures and algorithms, enhancing privacy and security measures, promoting ethical AI practices, addressing performance measures, and fostering stakeholder collaboration. By addressing these challenges, the full potential of AI-enabled metaverse can be harnessed to enhance sustainability, adaptability, and livability in smart cities.

Intelligent transportation systems (ITS) have made significant advancements in enhancing transportation safety, reliability, and efficiency. However, challenges persist in security, privacy, data management, and integration. Metaverse, an emerging technology enabling immersive and simulated experiences, presents promising solutions to overcome these challenges. By establishing secure communication channels, facilitating virtual simulations for safe testing and training, and enabling centralized data management with real-time analytics, metaverse offers a transformative approach to address these challenges. While metaverse has found extensive applications across industries, its potential in transportation remains largely untapped. This comprehensive review delves into the integration of the metaverse in ITS, exploring key technologies like virtual reality, digital twin, blockchain, and artificial intelligence, and their specific applications in the context of ITS. Real-world case studies, research projects, and initiatives are compiled to showcase the metaverse’s potential for ITS. It also examines the societal, economic, and technological implications of metaverse integration in ITS and highlights the associated integration challenges. Lastly, future research directions are identified to unlock the metaverse’s full potential in enhancing transportation systems.

Concerns for parking are becoming imminent to best support the urban core. These persistent parking problems could be turned into new opportunities, brought by current trends in meeting the globally connected continuum. This paper reveals a work-in- progress to capitalize on private land properties for parking, in order to relieve stress on public agencies, create new sources of revenue, and enlist new entities in the intermediary market. These intermediaries, labelled as Parking Service Providers (or PSPs) play a broker role through advertising parking lots on a shared cloud platform. To streamline these business collaborations and related processes, physical parking lots are augmented with Internet connectivity allowing cloud-provided applications to congregate these lots into a larger inventory. The Internet of Things (IoT) paradigm expands the scope of cloud-based intelligent car parking services in smart cities, with novel applications that better regulate car-parking related traffic. This paper presents a work-in-progress agenda that contributes to new business solutions and state-of-the-art research impacts. We reveal a multi- layered system of PSP-business model through interdisciplinary research blocks where original results are expected to be made at each layer