Unmanned aerial vehicle (UAV)-assisted federated edge learning (FEL) has emerged as a promising paradigm for privacy-preserving data processing in resource-constrained environments. However, the reliance on open wireless communication inherently exposes the system to eavesdroppers, who can eavesdrop and exploit shared model updates to reconstruct sensitive data, posing serious threats to the privacy and security. To address this challenge, we propose a privacy-aware UAV-assisted FEL framework that integrates adaptive local differential privacy (DP) into the model upload process, where user-specific noise is injected into local updates to prevent eavesdroppers from reconstructing sensitive data. To further enhance security and privacy performance, an indicator named value of privacy and security (VoPS) is designed to characterize the combined connection between training cost and privacy leakage. Furthermore, limited system resources including bandwidth allocation, user CPU frequency, DP noise scale, and UAV CPU frequency are collaboratively optimized under considering leakage threshold and heterogeneous computing constraints. Then, a deep deterministic policy gradient (DDPG)-based resource collaboration and secure aggregation scheme is proposed to solve the problem, in which the continuous optimization strategy is intelligently generated through the interaction between the agent and the dynamic privacy-aware UAV-assisted FEL system. Simulation results validate the effectiveness of the proposed scheme in enhancing the security and privacy performance of the system.

Proactive wardens can generate interference to enhance the detection performance, posing severe security threats to the legitimate transmission. Fortunately, an intelligent reflecting surface (IRS) can facilitate the covert transmission towards the proactive wardens by reconfiguring wireless channels. In this paper, we propose an IRS-assisted covert communication scheme with finite blocklength, where a proactive warden is monitoring and jamming the transmission simultaneously. Based on the warden’s optimal threshold, we can minimize the detection error probability, which is the worst case for the covert transmission. We then derive two constraints on the average covertness utilizing the Gaussian-Chebyshev integral and upper bound scaling, respectively. Subsequently, we calculate the average decoding error probabilities for both the optimal and random IRS phases. To improve the effective throughput while ensuring the covertness, the transmit power, transmission rate, and blocklength are jointly optimized. After deriving the optimal transmit power and transmission rate, the blocklength can be obtained in a closed form or via the numerical searching based on its serving range. Simulation results are presented to demonstrate the superiority of the proposed scheme, and the tradeoff between the covertness and the effective throughput is revealed.

Reconfigurable intelligent surface (RIS) has been widely deployed to assist the covert transmission thanks to its ability of channel reconfiguration. Compared with the conventional RIS, simultaneous transmitting and reflecting RIS (STAR-RIS) can transmit and reflect the incident signal simultaneously. This paper investigates the STAR-RIS assisted covert multicasting with the hardware impairment. Specifically, Alice covertly transmits the common information to two users assisted by one STAR-RIS against two wardens. The covert rate is maximized via jointly optimizing the transmit beamforming, and the reflection and transmission phase shifts, satisfying the transmit power constraint, the covertness constraint and the protocol of STAR-RIS. Owing to the non-convexity, we propose an iterative algorithm based on the alternating optimization, successive convex approximation and penalty-based semi-definite relaxation to obtain a sub-optimal solution. Simulation results verify the effectiveness of STAR-RIS.

With the evolutional development of maritime networks, the explosive growth of maritime data has put forward elevated demands for the computing capabilities of maritime devices (MDs). Unmanned aerial vehicle (UAV) is able to alleviate the computing pressure of MDs by forwarding the computing tasks to the edge server on the coast. However, UAV relaying introduces a significant security challenge due to the vulnerability of line-of-sight (LoS) communication channels, which can be exploited for eavesdropping on computing tasks. In this paper, an efficient secure communication scheme is proposed for UAV-relay-assisted maritime mobile edge computing (MEC) with a flying eavesdropper. The secure computing capacity of MDs is maximized by jointly optimizing the transmit power, time slot allocation factor, computation optimization and UAV trajectory. Due to multi-variable coupling, the formulated optimization problem (OP) is non-convex. We first transform OP by introducing auxiliary variables. Then, the transformed OP is decomposed and solved in an iterative manner by applying block coordinate descent (BCD) and successive convex approximation (SCA). Numerical results show that the secure computing capability of the UAV-relay-assisted maritime MEC system of proposed secure communication scheme can be effectively improved compared with benchmarks.

Reconfigurable intelligent surface (RIS) has been widely deployed to assist the covert transmission thanks to its ability of channel reconfiguration. Compared with the conventional RIS, simultaneous transmitting and reflecting RIS (STAR-RIS) can transmit and reflect the incident signal simultaneously, which provides an opportunity for the full-space covert transmission. This paper investigates the robust covert multicasting aided by the STAR-RIS with the hardware impairment. Specifically, Alice covertly transmits the common information to two single-antenna users assisted by the STAR-RIS against two non-colluding multi-antenna wardens. Furthermore, both energy splitting (ES) and mode switching (MS) protocols of the STAR-RIS are considered. With perfect wiretap channel state information (CSI), the covert rate is maximized via jointly optimizing the transmit beamforming, the reflection and transmission coefficient matrices, satisfying the transmit power constraint, the covertness constraint and the protocol of the STAR-RIS. Moreover, we also investigate the covert rate maximization problem under the case of imperfect wiretap CSI. Due to the non-convexity of the problem, we propose iterative algorithms based on the alternating optimization, successive convex approximation and penalty-based semi-definite relaxation to obtain a near-optimal solution to each problem. Simulation results verify the effectiveness of the STAR-RIS, and show that the ES is superior to the MS.

The vigorous development of maritime networks and the explosive growth of maritime sampling data put forward more and more high demands on the computing and communication capability of maritime equipment. Unmanned aerial vehicle (UAV), as the mobile relay device guarantees the capability by transferring part of the computing tasks of maritime equipment to carrying mobile edge computing (MEC) servers on land. However, the transmitting data of the UAV's communication channel can be easily intercepted due to the line of sight (LoS) feature, which brings the secure data transmission issue. To solve this issue, we propose a secure data transmission scheme in the UAV-relay assisted maritime MEC system. Specifically, a malicious UAV attempts to intercept the transmission data while another UAV helps forward the offloading computational data to the maritime surface users. A ground jammer transmits jamming signals with the object to block data intercepting. To maximize the users' minimum secure calculation capacity, we jointly optimize the transmit power of users and the relay UAV, the time slot allocation factor, and the UAV flight trajectory with block coordinate descent (BCD) and successive convex approximation (SCA) techniques. Numerical findings demonstrate that the proposed scheme can effectively improve the secure calculation capability of the system compared with four benchmark schemes.

The integration of mobile edge computing (MEC) and unmanned aerial vehicles (UAVs) has created new opportunities for efficient data processing and calculating services within the Internet of Things. However, the presence of the active eavesdropper brings serious vulnerabilities to the security calculation of terminal users (TUs), which can eavesdrop on TUs’ confidential content and compromise the quality of offloading calculation. In this paper, we propose an efficient energy consumption minimization scheme for the considered secure UAV-enabled MEC network including an active UAV eavesdropper. While ensuring security calculation for all TUs’ data, the network’s weighted energy consumption is achieved through trajectory and resource optimization, including time, local calculation and offloading calculation allocation. Due to the coupling of multi-variables and the non-convexity of the constraints, the problem is highly challenging to solve directly. To address this, an auxiliary variable is introduced to transform the problem into a more tractable form. The optimizing solution is then obtained through iterative updates, allowing for the convergence towards an optimizing solution. Simulation results show that the proposed scheme exhibits superior performance of reducing the network’s energy consumption compared to the benchmark scheme.

Non-orthogonal multiple access (NOMA) allows multiple users to share link resource for higher spectrum efficiency. It can be applied to unmanned aerial vehicle (UAV) and mobile edge computing (MEC) networks to provide convenient offloading computing service for ground users (GUs) with large-scale access. However, due to the line-of-sight (LoS) of UAV transmission, the information can be easily eavesdropped in NOMA-based UAV-MEC networks. In this paper, we propose a secure communication scheme for the NOMA-based UAV-MEC system towards a flying eavesdropper. In the proposed scheme, the average security computation capacity of the system is maximized while guaranteeing a minimum security computation requirement for each GU. Due to the uncertainty of the eavesdropper’s position, the coupling of multi-variables and the non-convexity of the problem, we first study the worst security situation through mathematical derivation. Then, the problem is solved by utilizing successive convex approximation (SCA) and block coordinate descent (BCD) methods with respect to channel coefficient, transmit power, central processing unit (CPU) computation frequency, local computation and UAV trajectory. Simulation results show that the proposed scheme is superior to the benchmarks in terms of the system security computation performance.

With the maneuverability and mobility control of unmanned aerial vehicle (UAV), carrying mobile edge computing (MEC) servers on UAVs is able to effectively alleviate the explosive growth of data traffic pressure. However, UAV adopts line-of-sight transmission which has broadcasting characteristics. Malicious eavesdroppers can easily take advantage of the characteristics to eavesdrop information during the UAV edge computing. Therefore, the security of the UAV-MEC systems is a challenging problem. This article proposes a secure communication scheme for the dual-UAV-MEC system. In the proposed scheme, UAV server assists ground users in calculating the offloading tasks. In order to reduce the eavesdropping of offloading information by UAV eavesdropper, jammer sends interference signals on the ground. We aim to maximize the user's minimum secure calculation capacity by optimizing resources and trajectory of the UAV server. We first transform the optimization problem into a tractable form through mathematical methods and use successive convex approximation and block coordinate descent algorithms to solve it in an iterative manner. The final numerical results show that, compared with the benchmark schemes, the method proposed in this article effectively increases the secure calculation capacity of the system.

Robotic hands with anthropomorphism considerations are of prominent popularity in human-centered environment. Existing anthropomorphic robotic hands achieving part or most of human hand comparable dexterity have been applied as various robotic end-effectors and prosthetics. However, two deficiencies are evident that the design for a dexterous anthropomorphic hand is largely based on the intuition of designers and the dexterity of robotic hand is hard to evaluate. To tackle these two challenges, this paper summarizes 50 hand dexterity benchmarks (HD-marks) to evaluate hand dexterity comprehensively from three perspectives. Secondly, a novel 22-DOFs soft robotic hand (S-22) replicates human hand kinematics is used to demonstrate all the 50 HD-marks. Thirdly, 7 critical joint-based kinematic motions (K-motions) and their correlation with the 50 HD-marks are established. Therefore, a clear robotic hand design guideline is built by mapping the hand functional dexterity to the required joint kinematics.

In this paper we study interference reducing and resource allocation in Unmanned aerial vehicle (UAV) wireless powered communication system with two UAVs and two ground nodes (GNs). In existing scenarios interference exists at the receiver because multiple GNs transmit information at the same time. In order to reduce interference at the receiver, a new scenario is proposed in this paper. In the proposed scenario, one GN transmit information while another is receiving energy. Minimum uplink throughput is maximized by optimizing trajectory of UAVs and resource allocation. The optimization problem is decomposed into three subproblems which are approximated to convex optimization problems. Simulation results show that the new scenario achieves larger minimum uplink throughput than original scenario.

In order to improve the efficiency of information transmission, this paper proposes an energy trading method based on wireless power supply. In the proposed method, the system consists of a source node, a relay node, a destination node and several energy supply points. Since the relay node is selfish, it does not consume its own energy to help the source node forward information. So this paper designed a series of energy-reward pairs. Through the contract theory designed in this paper, we study how to optimize the rewards that the destination node pays to the ESP and the energy provided by the ESP to the relay node to maximize the utility of the social welfare. The simulation results show that the energy trading method proposed in this paper can make the utility of the destination node reach the optimal value and effectively improve the information transmission efficiency.

In this paper, we study energy efficiency optimization in OFDM based two-way DF relaying network with energy harvesting. Instead of time switching (TS) and power splitting (PS) schemes, we adopt OFDM modulation method, where subcarriers are divided into two groups to achieve information decoding (ID) and energy harvesting (EH) separately. The formulated EE optimization problem is non-convex constrained by the minimum information rate and the maximum transmission power. By exploiting fractional programming, an iterative resource allocation algorithm is proposed to solve the problem. Then we adopt dual decomposition and sub-gradient method to obtain the optimal variables, where subcarrier grouping and power allocation are jointly optimized to maximize the system energy efficiency.

The fast expanding 3C (Computer, Communication, and Consumer electronics) manufacturing leads to a high demand on the fabrication of USB cables. While several commercial machines have been developed to automate the process of stripping and soldering of USB cables, the operation of manipulating USB wires according to the color code is heavily dependent on manual works because of the deformation property of wires, probably resulting in the falling-off or the escape of wires during manipulation. In this paper, a new vision-based controller is proposed for robotic grasping and manipulation of USB wires. A novel two-level structure is developed and embedded into the controller, where Level-I is referred to as the grasping and manipulation of wires, and Level-II is referred to as the wire alignment by following the USB color code. The proposed formulation allows the robot to automatically grasp, manipulate, and align the wires in a sequential, simultaneous, and smooth manner, and hence to deal with the deformation of wires. The dynamic stability of the closed-loop system is rigorously proved with Lyapunov methods, and experiments are performed to validate the proposed controller.

To supporting the systematic requirements of higher spectrum efficiency and user deployment density in future Industrial Internet of Things (IIoT), traditional orthogonal multiple access schemes, such as orthogonal frequency-division multiplex, are quite limiting. Nonorthogonal multiple access (NoMA) has been recognized as one of the enabling technologies for IIoT in 5G network. In this paper, we have proposed an alternative NoMA scheme called interleave-grid multiple access (IGMA). Depending on interleaving and grid-mapping process, IGMA is capable to provide reliable block error rate performance, marvelous user multiplexing capability, as well as robustness against intercell interference. IGMA can apply various detection and decoding techniques at receiver sides to improve the detection performance with acceptable complexity. Both of link-level and system-level results show the promising benefits of IGMA, in particular that nearly seven times user multiplexing gain compared with orthogonal frequency-division multiple access has been observed in the system-level simulation. In addition, a hardware test bed has been implemented to verify the IGMA performance and the testing results proved the strong competitiveness of IGMA over orthogonal frequency-division multiple access in terms of block error rate performance in user overloading scenarios.