Virtual reality-embedded digital twin (VR-DT) service integrates digital twin with virtual reality to visualize the digital representation of real-world production, boosting the digital transformation of manufacturing industry in the Industrial Internet of Things (IIoT). Balanced against the advantages of the VR-DT service, its data-driven, computing-intensive, and security-sensitive features bring challenges to the current IIoT. Therefore, we propose a blockchain-based distributed resource allocation scheme to improve the average Quality of Service (QoS) of the VR-DT services with regard to service delay and transaction throughput. We formulate the joint optimization of channel assignment, subframe configuration, computing capacity allocation, and block size adjustment as a mixed-integer nonlinear programming problem. A fully decentralized multiagent compound-action actor–critic algorithm is developed to solve the QoS optimization problem. Simulation results demonstrate that our proposed scheme can efficiently improve the average QoS of the VR-DT services in a realizable way as compared to existing schemes.

With the full development of intelligent mobile communications, wireless mixed reality (MR) provides a more visually immersive experience and stronger interaction with environments than virtual reality (VR) and augmented reality (AR). However, the asymmetric characteristic of wireless MR traffic creates a huge challenge to current mobile networks. Dynamic time division duplex (D-TDD) is considered as a promising technology to improve wireless MR users’ quality of experience (QoE) due to its potentials and advantages in delivering asymmetric traffic. Therefore, in this paper, we propose a QoE-driven distributed multidimensional resource allocation (MRA) supplemented by inter-cell interference (ICI) mitigation scheme for wireless MR in multi-cell D-TDD systems. First, to improve QoE of MR users, we formulate the joint optimization of subframe configuration, channel assignment and computation offloading as a mixed-integer nonlinear programming problem. A novel fully-decentralized multi-agent deep Q-network (DQN) algorithm is developed to solve the problem. Then, to mitigate ICI, a water filling based power control algorithm is investigated to minimize the total power of each small base station and its associated MR users. Simulation results demonstrate that our proposed scheme improves QoE of MR users in a realizable way as compared to existing schemes.

A vehicular cloud (VC) can reduce latency and improve resource utilization of the Internet of vehicles by effectively using the underutilized computing resources of nearby vehicles. Although the task offloading of the VC enhances road safety and traffic management on the Internet of vehicles and meets the low-latency requirements for driving safety services on the Internet of vehicles business, there are still some key challenges such as the resource allocation mechanism of differentiated services (DiffServ) and task offloading mechanism of improving user experience. To address these issues, we study the task offloading and resource allocation strategy of the VC system where tasks generated by vehicles can be offloaded and executed cooperatively by vehicles in VC. Specifically, the computing task is further divided into independent subtasks and executed in different vehicles in VC to maximize the offloading utility. Considering the mobility of vehicles, the deadline of tasks, and the limited computing resources, we propose the optimization problem of task offloading in the VC system in the cause of improved user experience. To characterize the difference in service requirements resulting from the diversity of tasks, a DiffServ model focusing on the pricing of a task is utilized. The initial pricing of a task is tailored by the characteristics of the task and the uniqueness of the network status. In this model, tasks are sorted and processed in order according to task pricing, so as to optimize resource allocation. Numerical results show that the proposed scheme can effectively increase the resource utilization and task completion ratio.

Three InSAR products and DEM data are involved to detect the potential landslides over Wudongde Hydropower Station Section, Jinsha River. More than ten known and unknown landslides are successfully detected. Meanwhile, Small Baseline Subsets (SBAS) InSAR technique is applied to calculate the time-series deformation of the Jinpingzi landslide. Then, in-situ georobot measurements are used for point-wise comparison. The precision in slide direction is 1.8 cm by comparing with in-situ georobot measurements. At last, the Erpingcun landslide is taken as an example to calculate the vertical and horizontal deformation components by fusing the ascending and descending line-of-sight results.