This paper investigates a symbiotic unmanned aerial vehicle (UAV)-assisted intelligent reflecting surface (IRS) radio system, where the UAV is leveraged to help the IRS reflect its own signals to the base station, and meanwhile enhance the UAV transmission by passive beamforming at the IRS. First, we consider the weighted sum bit error rate (BER) minimization problem among all IRSs by jointly optimizing the UAV trajectory, IRS phase shift matrix, and IRS scheduling, subject to the minimum primary rate requirements. To tackle this complicated problem, a relaxation-based algorithm is proposed. We prove that the converged relaxation scheduling variables are binary, which means that no reconstruct strategy is needed, and thus the UAV rate constraints are automatically satisfied. Second, we consider the fairness BER optimization problem. We find that the relaxation-based method cannot solve this fairness BER problem since the minimum primary rate requirements may not be satisfied by the binary reconstruction operation. To address this issue, we first transform the binary constraints into a series of equivalent equality constraints. Then, a penalty-based algorithm is proposed to obtain a suboptimal solution. Numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmarks.

The ongoing deployment of 5G network involves the Internet of Things (IoT) as a new technology for the development of mobile communication, where the Internet of Everything (IoE) as the expansion of IoT has catalyzed the explosion of data and can trigger new eras. However, the fundamental and key component of the IoE depends on the computational intelligence (CI), which may be utilized in the sixth generation mobile communication system (6G). The motivation of this article presents the 6G enabled network in box (NIB) architecture as a powerful integrated solution that can support comprehensive network management and operations. The 6G enabled NIB can be used as an alternative method to meet the needs of next-generation mobile networks by dynamically reconfiguring the deployment of network functions, providing a high degree of flexibility for connection services in various situations. Especially the CI technology such as evolutionary computing, neural computing and fuzzy systems utilized as a part of NIB have inherent capabilities to handle various uncertainties, which have unique advantages in processing the variability and diversity of large amounts of data. Finally, CI technology for NIB, which is widely used is also introduced such as distributed computing, fog computing, and mobile edge computing in order to achieve different levels of sustainable computing infrastructure. This article discusses the key technologies, advantages, industrial scenario applications of CI technology as NIB, typical use cases and development trends based on IoE, which provides directional guidance for the development of CI technology as NIB for 6G.

The unmanned aerial vehicle (UAV) assisted transmission can significantly improve the spectrum efficiency and coverage of the wireless communication network. The paper proposed a cooperative secrecy transmission mechanism, which can take advantage of transmitter signal of primary user (PU) as a dedicated radio frequency (RF) source with decode and forward (DF) UAV selection and energy harvesting (EH) under cognitive network. Specifically, the transmitting signal of PU in the cognitive network can be viewed and used as energy to drive secondary user (SU) nodes and multi-relay nodes to send signals. It was worth noting that the DF UAV relay may bring about the unexpected noise and decrease the received signal to noise ratio (SNR), therefore, the multiple UAV assisted relays selected with secrecy capacity maximization criteria under cognitive network was proposed and investigated in detail, where the destination node equipped with multiple antennas and adopted the optimal antenna selection receiver. Moreover, By using the function analysis method, the closed expressions of the system secrecy outage probability (SOP) and the probability of non-zero secrecy capacity were calculated and derived accurately. Furthermore, the asymptotic expressions of the UAV assisted relay system under the large SNR cases were analyzed in detail, where the expressions of secrecy diversity order and secrecy diversity gain of the UAV assisted relay cognitive system were obtained thoroughly. Simulation results verified the effectiveness of the proposed mechanism and the correctness of the calculation.

This letter investigates a power-efficient unmanned aerial vehicle (UAV)-based wireless sensor network, where the UAV is used as a flying base station to communicate with the sensor nodes (SNs) with flexible movement path. We aim at minimizing the total power consumption of the UAV with a guarantee of the required transmission rate of SNs by jointly optimizing the SNs-UAV scheduling scheme, power allocation strategy, and flight trajectory of the UAV. Due to the non-convex and mixed-integer nature of the original problem, block coordinate decent and successive convex optimization techniques are employed to decompose it into two sub-problems, which leads to an efficient iterative algorithm. Numerical results show the performance gain of our proposed scheme.

Nowadays, our society has been experiencing the development of Healthcare 4.0, focusing on digitalization by utilizing cloud/fog computing-based consumer Internet-of-Things (CIoT). Meanwhile, Industry 5.0 has been defined recently, targeting improving the current Industry 4.0. Inspired by Industry 5.0, we are among the first wave of researchers to propose Healthcare 5.0, specifically in the perspective of fog/cloud computing. Concretely, we perform a comprehensive vertical and horizontal summary of Healthcare 1.0 -4.0 and Industry 1.0 -4.0, which enables us to be among the first research efforts to provide a unique prediction of the corresponding upcoming Healthcare 5.0, based on Industry 5.0. In particular, we predict the potential development in Healthcare 5.0, specifically for fog/cloud computing, on top of a comprehensive overview of the role and state-of-the-art development in Healthcare 4.0. We also give our unique insight of the potential technologies of CIoT and fog/cloud computing in Healthcare 5.0, as well as the corresponding opportunities and challenges. Therefore, for the first time, this article provides deep and well-thought insight to open a new era towards the future fog/cloud computing for healthcare system development towards Healthcare 5.0.

In the context of the unmanned aerial vehicle (UAV)-assisted vehicular networking system, more network factors need to be considered to ensure the safe operation of connected vehicles. A large volume of delay-sensitive and computationally demanding tasks necessitate offloading to UAVs or roadside units for processing. And the efficient allocation of various network resources of vehicles, UAVs, and roadside units under constrained conditions determines the efficiency of task offloading. Deep reinforcement learning (DRL) has demonstrated its efficacy as an experienced approach for solving such problems. In this paper, we delve into the utilization of deep reinforcement learning to design an efficient UAV-assisted vehicular edge computing task offloading strategy. Under the constraints of limited network bandwidth and limited UAV power, the trajectory and the task offloading strategy of the UAV are jointly optimized. The primary objective of our proposed strategy is to achieve a notable reduction in the system delay of the edge computing network. Given the dynamic variability of tasks arrival, we employ a long short-term memory (LSTM) network with the attention mechanism and a deep deterministic policy gradient (DDPG) algorithm to effectively model the optimization problem as a Markov decision process. This approach can obtain the optimal policy through interactive learning from the UAV and the vehicle environment. The experiment results illustrate that this strategy outperforms other baseline strategies in terms of convergence speed, network delay, and task offloading ratio.