  By amalgamating recent communication and control technologies, computing and
data analytics techniques, and modular manufacturing, Industry~4.0 promotes
integrating cyber-physical worlds through cyber-physical systems (CPS) and
digital twin (DT) for monitoring, optimization, and prognostics of industrial
processes. A DT is an emerging but conceptually different construct than CPS.
Like CPS, DT relies on communication to create a highly-consistent,
synchronized digital mirror image of the objects or physical processes. DT, in
addition, uses built-in models on this precise image to simulate, analyze,
predict, and optimize their real-time operation using feedback. DT is rapidly
diffusing in the industries with recent advances in the industrial Internet of
things (IIoT), edge and cloud computing, machine learning, artificial
intelligence, and advanced data analytics. However, the existing literature
lacks in identifying and discussing the role and requirements of these
technologies in DT-enabled industries from the communication and computing
perspective. In this article, we first present the functional aspects, appeal,
and innovative use of DT in smart industries. Then, we elaborate on this
perspective by systematically reviewing and reflecting on recent research in
next-generation (NextG) wireless technologies (e.g., 5G and beyond networks),
various tools (e.g., age of information, federated learning, data analytics),
and other promising trends in networked computing (e.g., edge and cloud
computing). Moreover, we discuss the DT deployment strategies at different
industrial communication layers to meet the monitoring and control requirements
of industrial applications. We also outline several key reflections and future
research challenges and directions to facilitate industrial DT's adoption.
