In this paper, we study a mobile edge computing (MEC) architecture with the assistance of digital twin (DT) applied for industrial automation where multiple Internet-of-Things (IoT) devices intelligently offload computing tasks to multiple MEC servers to reduce end-to-end latency. To do so, first we propose and formulate a practical end-to-end latency minimisation problem in the DT-assisted MEC model subject to the constraints of quality-of-services and computation resource at the IoT devices and MEC servers in industrial IoT networks. Then, we solve the proposed latency minimisation problem by iteratively optimising the transmit power of IoT devices, user association, intelligent task offloading, and estimated CPU processing rate of the devices. Finally, simulation results are conducted to prove the effectiveness of the proposed method in terms of the latency performance compared with some conventional methods.

In this work, we consider a joint optimisation of real-time deployment and resource allocation scheme for UAVaided relay systems in emergency scenarios such as disaster relief and public safety missions. In particular, to recover the network within a disaster area, we propose a fast K-meansbased user clustering model and jointly optimal power and time transferring allocation which can be applied in the real system by using UAVs as flying base stations for real-time recovering and maintaining network connectivity during and after disasters. Under the stringent QoS constraints, we then provide centralised and distributed models to maximise the energy efficiency of the considered network. Numerical results are provided to illustrate the effectiveness of the proposed computational approaches in terms of network energy efficiency and execution time for solving the resource allocation problem in real-time scenarios. We demonstrate that our proposed algorithm outperforms other benchmark schemes.

In this paper, we propose an aerial reconfigurable intelligent surface (RIS) system to support the stringent constraints of ultra-reliable low latency communication (URLLC). Specifically, unmanned aerial vehicles (UAVs) employed onboard RIS panels can act as repeaters to reflect the signal from macro base station (MBS) to all users in the networks. To overcome the dense networks' interference, we propose to use zero-forcing beamforming and time division multiplexing access (TDMA) scheme where each UAV can serve a number of users in its own cluster. We formulate a optimisation framework in terms of UAVs' deployment, power allocation at MBS, phase-shift of RIS, and blocklength of URLLC. Due to highly nonconvex and complex optimisation problem, we first consider to use a deep neural network (DNN) to solve the optimal UAVs' deployment. Then, the optimal resource allocation is proposed to provide the maximal reliability of the considered system with respect to the users' fairness. From the representative numerical results, our proposed scheme is shown to superior to other benchmarks which exhibits the positive impact of aerial RIS in supporting stringent demands of URLLC.INDEX TERMS Ultra-reliable low-latency communications (URLLC), Reconfigurable Intelligent Surface (RIS), Unmanned aerial vehicle (UAV).

Many of the devices used in Internet-of-Things (IoT) applications are energy-limited, and thus supplying energy while maintaining seamless connectivity for IoT devices is of considerable importance. In this context, we propose a simultaneous wireless power transfer and information transmission scheme for IoT devices with support from the reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) communications. In particular, in the first phase, IoT devices harvest energy from the UAV through wireless power transfer; and then in the second phase, the UAV collects data from the IoT devices through information transmission. To characterise the agility of the UAV, we consider two scenarios: a hovering UAV and a mobile UAV. Aiming at maximising the total network sum-rate, we jointly optimise the trajectory and the power allocation of the UAV, the energy harvesting scheduling of IoT devices, and the phase-shift matrix of the RIS. We formulate a Markov decision process and propose two deep reinforcement learning algorithms to solve the optimisation problem of maximising the total network sum-rate. Numerical results illustrate the effectiveness of the UAV's flying path optimisation and the network's throughput of our proposed techniques compared with other benchmark schemes. Given the strict requirements of the RIS and UAV, the significant improvement in processing time and throughput performance demonstrates that our proposed scheme is well applicable for practical IoT applications.

Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. However, due to the limitation of their on-board power and flight time, it is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT). In this paper, we design a new UAVassisted IoT system relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices. Then, a deep reinforcement learning-based technique is conceived for finding the optimal trajectory and throughput in a specific coverage area. After training, the UAV has the ability to autonomously collect all the data from user nodes at a significant total sum-rate improvement while minimising the associated resources used. Numerical results are provided to highlight how our techniques strike a balance between the throughput attained, trajectory, and the time spent. More explicitly, we characterise the attainable performance in terms of the UAV trajectory, the expected reward and the total sum-rate.