This survey provides a comprehensive analysis of digital twin (DT) technology as a transformative tool for advancing connected and autonomous vehicles (CAVs) and intelligent transportation systems (ITSs), focusing on advancements in vehicle safety, traffic management, and autonomous driving capabilities. The paper begins by discussing the foundational concepts and enabling technologies behind DT systems, setting the stage for their application in transportation networks. We review DT applications in vehicle safety, highlighting their role in real-time monitoring, predictive maintenance, and risk mitigation. Next, we explore the role of DT technology in optimizing traffic flow, enhancing traffic management, and enabling adaptive responses to dynamic conditions. The paper then examines the integration of DTs in intelligent and autonomous vehicles, emphasizing advancements in simulation, testing, and the development of autonomous driving functionalities. Finally, we outline future research opportunities and challenges for DT applications, providing a roadmap for their continued evolution in CAVs and ITS.

Inter-core crosstalk causes serious signal impairment in a multi-core fiber (MCF) optical network. With extensive usage of data centers (DCs), traffic demands become increasingly asymmetric leading to significant overall capacity wastage if the network is designed with bidirectional links. To alleviate these effects in a MCF optical network, we consider assigning the fiber cores asymmetrically, in a counter propagating manner. This not only reduces the inter-core crosstalk between counter-propagating fiber cores significantly but also allows the flexibility of assigning different numbers of fiber cores in the opposite directions of a fiber link. This reduces network capacity wastage arising from bidirectional traffic demand asymmetry. To evaluate the benefits of the proposed strategy, we consider the routing, spectrum, and core assignment (RSCA) problem for the MCF optical network. An integer linear programming (ILP) model and an auxiliary graph (AG) based heuristic algorithm are developed to optimize network spectrum resource utilization. Simulation studies show the effectiveness of the proposed crosstalk-aware core counter-propagation strategy, which can significantly outperform its counterpart, i.e., the co-propagation scheme, in terms of the total number of MCFs used and average inter-core crosstalk. In addition, the proposed RSCA heuristic algorithm is efficient to perform close to the ILP model, which can minimize the number of MCFs used and crosstalk between neighboring cores.

5G and beyond (B5G) applications generate tremendous computing-intensive, latency-sensitive, and privacy-sensitive tasks, which differ from the legacy cloud computing tasks, requiring more sophisticated scheduling strategies. We must satisfy the stringent service requirements, particularly privacy preservation that has not been sufficiently considered in the past. Meanwhile, we need to balance the tasks offloaded to different edge nodes to avoid overwhelming some fog nodes, which may degrade the overall performance. To appropriately schedule the privacy-sensitive tasks while balancing the traffic load, we define IoT tasks according to their security need, processing time, and real-time requirement and segment IoT tasks into smaller pieces based on their privacy levels. The sliced tasks are scheduled to multiple fog nodes with satisfactory security reputations to avoid a compromised fog node handling a whole task. Meanwhile, we consider the constraint of the response time of all available fog nodes before scheduling IoT tasks to avoid chaos task scheduling that may overwhelm some fog nodes. Regarding this, we propose a reinforcement learning (RL) model in which the agent tends to satisfy the required latency and security requirements while avoiding overloading some fog nodes to minimize the average delay. The numerical results demonstrate that the proposed approach performs well in a better-balanced load and less performance violation in latency and security.

Body area networks (BANs) are configured with a great number of ultra-low power consumption wearable devices, which constantly monitor physiological signals of the human body and thus realize intelligent monitoring. However, the collection and transfer of human body signals consume energy, and considering the comfort demand of wearable devices, both the size and the capacity of a wearable device’s battery are limited. Thus, minimizing the energy consumption of wearable devices and optimizing the BAN energy efficiency is still a challenging problem. Therefore, in this paper, we propose an energy harvesting-based BAN for smart health and discuss an optimal resource allocation scheme to improve BAN energy efficiency. Specifically, firstly, considering energy harvesting in a BAN and the time limits of human body signal transfer, we formulate the energy efficiency optimization problem of time division for wireless energy transfer and wireless information transfer. Secondly, we convert the optimization problem into a convex optimization problem under a linear constraint and propose a closed-form solution to the problem. Finally, simulation results proved that when the size of data acquired by the wearable devices is small, the proportion of energy consumed by the circuit and signal acquisition of the wearable devices is big, and when the size of data acquired by the wearable devices is big, the energy consumed by the signal transfer of the wearable device is decisive.