The unmanned aerial vehicle (UAV) aided mobile edge computing (MEC) network has attracted significant attention due to its enhanced computing power, reliable network connectivity, dynamic environment adaptability, which however still suffer from non-instantaneous channel reconstruction and wireless security threats. Therefore, this paper considers a reconfigurable intelligent surface (RIS) assisted UAV-MEC network, aiming to minimize UAV energy consumption by jointly optimizing task offloading rate, user scheduling coefficient, RIS phase and UAV trajectory with the constraints of secure offloading rate. Given the multi-variable coupling and non-convex nature of this optimization problem, we decouple it into three subproblems, which include the user scheduling and offloading ratio optimization, the RIS phase optimization, and the UAV trajectory optimization. For the fractional programming problem involving RIS phase optimization, the Dinkelbach algorithm is used to transform it into a parametric subtraction problem, thus obtaining a closed-form solution for the RIS phases. Furthermore, the successive convex approximation (SCA) algorithm is employed for the UAV trajectory optimization subproblem. Ultimately, a double-layer iterative optimization algorithm based on block coordinate descent (BCD) is proposed to solve the original non-convex optimization problem. Simulation results confirm its superior performance in saving UAV energy compared to other baseline schemes.

In this article, we investigate covert millimeter-wave (mmWave) communications with finite blocklength, where a multiantenna transmitter sends covert messages to a legitimate receiver in the presence of spatially random wardens. Both the phase array (PA) and linear frequency diverse array (LFDA) beamforming schemes, which are designed to maximize the antenna gain from the transmitter to the legitimate receiver, are investigated to improve the covert communication performance. First, the novel expressions of covert communication constraint and average effective covert throughput (AECT) are derived for both beamforming schemes. Then, taking into account the constraint of maximal available blocklength, the optimal transmit power and blocklength are determined for maximizing the AECT. Typically, comparing to the benchmark with fixed blocklength, the enhancement of AECT by utilizing the optimized blocklength enlarges as the density of wardens increases. In addition, it is observed that increasing the maximal available blocklength cannot always improve the maximum AECT due to the tradeoff between the transmit power and blocklength. Furthermore, it is shown that the maximum AECT varies for different directions of the legitimate receiver under both the beamforming schemes, and the transmitter can adaptively choose the PA or LFDA beamforming scheme to improve the covertness performance against spatially random wardens.

In this work, we investigate the status packet covert communication with time uncertainty in time-critical wireless networks. We model the packet generation as a Poisson process that concatenates the information timeliness and communication covertness, since the prior transmission probability is highly correlated with the packet generation. To balance between the timeliness and covertness of the status packet transmission, we propose two schemes, named random sub-slot selection (RSS) scheme and random channel use selection (RCUS) scheme, by exploiting the random transmission time to confuse a warden Willie’s binary detection on the covert communication. Subsequently, the average age of information (AoI) subject to the covertness constraint is derived for the proposed RSS and RCUS schemes. It is demonstrated that the symbol-length of the status packet introduces a non-trivial tradeoff between the covertness and timeliness. Inspired by this, further designs of the symbol-length and the transmit power for the proposed two schemes are formulated as optimization problems and solved optimally. Our numerical results demonstrate the superiority of proposed schemes over the existing covert communication strategies. In addition, our examination reveals that the RCUS scheme outperforms the RSS scheme when the covertness constraint is extremely strict. Otherwise, the RSS scheme generally outperforms the RCUS scheme in terms of achieving a lower AoI.

In this work, we aim to maximize the timeliness of data collection subject to a covertness constraint in unmanned aerial vehicle (UAV)-aided Internet of Things (IoT) networks, where a UAV periodically conducts wireless power transfer (WPT) to charge an energy-constrained IoT device and then the IoT device opportunistically sends its collected data to the UAV. To this end, we first derive a lower bound on the covertness constraint and an analytical expression for Age of Information (AoI) to characterize timeliness. Then, we jointly optimize the UAV’s transmit power for WPT, the WPT duration, and the data transmission duration by considering two practical scenarios. For the fixed total duration scenario, our analytical optimal solutions indicate that the total harvested energy at the IoT device is independent of the covertness constraint, although both the UAV’s transmit power and the WPT duration are significantly affected by the covertness constraint. With the optimized total duration scenario, we prove that the UAV’s optimal transmit power is always attained at its maximum value regardless of the existence of the covertness constraint, but the WPT and data transmission durations are sensitive to the required covertness. Overall, there exists a nontrivial tradeoff between the timeliness and covertness for data collection in the considered system, which is determined by the WPT design. Furthermore, our numerical results show that the optimal prior probability of the IoT device’s opportunistic transmission is generally not 0.5 for the fixed total duration, but it is indeed 0.5 for the optimized total duration.