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The advances in artificial intelligence (AI) have led to technological advancements in a plethora of domains. Healthcare, education, and smart city services are now enriched with AI capabilities. These technological advancements would not have been realized without the assistance of fast, secure, and fault-tolerant communication media. Traditional processing, communication, and storage technologies cannot maintain high levels of scalability and user experience for immersive services. The metaverse is an immersive three-dimensional (3D) virtual world that integrates fantasy and reality into a virtual environment using advanced virtual reality (VR) and augmented reality (AR) devices. Such an environment is still being developed and requires extensive research in order for it to be realized to its highest attainable levels. In this article, we discuss some of the key issues required in order to attain realization of metaverse services. We propose a framework that integrates digital twin (DT) with other advanced technologies, such as the sixth-generation (6G) communication network, blockchain, and AI, to maintain continuous end-to-end metaverse services. This article also outlines requirements for an integrated, DT-enabled metaverse framework and provides a look ahead into the evolving topic.

The global economy is experiencing significant business growth leading to increasing demands for energy. Global energy consumption increased by 2.1 percent in 2017, and according to the 2019 U.S. Energy Information Administration (EIA) reports, the expected energy growth in Asia will reach 50 percent by 2050. Since it is unlikely that these demands will be met using conventional energy systems, the world is moving toward sustainable renewable energy systems (RESs) that are supported by cyberphysical technologies. Additionally, climate change is putting more pressure on governments to reduce their dependency on conventional energy resources and invest more in sustainable renewable energies. The shift from conventional energy sources to renewables is clear, as revealed in 2018 where 30 percent of the U.K. gross electricity consumption was generated by RES (according to the Green Match report on Renewable Energy in the United Kingdom). This transformation has led to the emergence of prosumers (i.e., producers and consumers) capable of both generating and consuming energy. Many households and buildings are now equipped with solar panels that can contribute to the overall energy needs. Power systems need to adapt to renewable energy to provide more efficient and sustainable operations. As such, the concept of smart grid needs to be promoted and looked at more closely in the research and development fields to support such sustainable power systems and energy solutions.

The global economy is experiencing significant business growth leading to increasing demands for energy. Global energy consumption increased by 2.1 percent in 2017, and according to the 2019 U.S. Energy Information Administration (EIA) reports, the expected energy growth in Asia will reach 50 percent by 2050. Since it is unlikely that these demands will be met using conventional energy systems, the world is moving toward sustainable renewable energy systems (RESs) that are supported by cyberphysical technologies. Additionally, climate change is putting more pressure on governments to reduce their dependency on conventional energy resources and invest more in sustainable renewable energies. The shift from conventional energy sources to renewables is clear, as revealed in 2018 where 30 percent of the U.K. gross electricity consumption was generated by RES (according to the Green Match report on Renewable Energy in the United Kingdom). This transformation has led to the emergence of prosumers (i.e., producers and consumers) capable of both generating and consuming energy. Many households and buildings are now equipped with solar panels that can contribute to the overall energy needs. Power systems need to adapt to renewable energy to provide more efficient and sustainable operations. As such, the concept of smart grid needs to be promoted and looked at more closely in the research and development fields to support such sustainable power systems and energy solutions.

Mobile edge computing (MEC) is an emergent technology that has revolutionized traditional cloud service solutions. Mobile edge computing extends cloud computing by providing processing, storage, and networking capabilities at the edge of the mobile network. Delay‐sensitive and context‐aware applications are able to execute within close proximity of mobile users. Additionally, today's cloud services are not tailored to user specifications, but rather diversified toward a group of users. To guarantee delivery of user‐specific services in 5G networks, service composition techniques should be incorporated. This article envisions a real‐time, context‐aware, service‐composition collaborative framework that lies at the edge of the network, comprising MEC and user devices for fast composite service delivery. The proposed solution decomposes cloud data into a set of files and services, which are then replicated to MEC nodes. Frequently requested files and services are further cached onto user mobile devices for faster access. Both MEC nodes and mobile users advertise their services onto the collaborative edge/user space, where services are delivered either composite or unrendered according to users' requests. Service composition is achieved through a learning‐based workflow‐net approach that relies on previous composition results to build service composition models to be used for new compositions. The presented solution provides guaranteed and fast delivery of the requested cloud composite services to end users while sustaining QoS requirements and load balancing among edge and mobile nodes. This article envisions a real‐time framework that lies at the edge of the network, comprising MEC and user devices for fast composite service delivery in 5G systems. The solution decomposes cloud data into a set of files and services which are then replicated to MEC nodes. Files and services are further cached onto user mobile devices for faster access. Nodes advertise their services onto the collaborative Edge/user space, where services are delivered either composite or un‐rendered according to users' requests.