Unmanned aerial vehicle (UAV)-enabled backscatter communications (BackComm) is deemed to be a vital technique enabling the data transmission over massive battery-less devices for Internet of Things (IoT). However, the UAV-enabled Backcomm suffers from information leakage due to the broadcasting nature of wireless channels. To cope with the security issue, in this paper, a UAV-enabled multi-user secure BackComm system is developed using analog beamforming (ABF) and randomized continuous wave (RCW) techniques, where the multiple users are supported by the multi-carrier RCW over a single low-complexity radio frequency (RF) chain. By exploiting the RCW transmitted towards backscatter, the eavesdropping link can be eroded without any specific jamming signals. The closed-form of the secrecy rate is studied with the approximations, which is then maximized by jointly optimizing the beamforming together with the UAV’s location and the RCW settings. Simulation results are carried out to confirm the accuracy of the proposed approximation, while the convergence behavior of the optimization algorithm is analyzed. As a result, it can be shown that the secrecy rate can be significantly improved compared with the benchmark schemes.

In low-altitude Internet of Things (IoT) systems, uncrewed aerial vehicles (UAVs) are increasingly deployed as agile and distributed platforms. In scenarios where infrastructure support is limited or unavailable, UAVs can form ad hoc networks that enable flexible and self-organizing communication, making them well-suited for delivering real-time edge intelligence. A key challenge in such UAV ad hoc networks lies in edge server placement to ensure low-latency data transmission while balancing the computation load among servers. In this article, we provide insights into the design of UAV ad hoc network-assisted IoT systems by characterizing the tradeoff between data transmission latency and computational load. To this end, we formulate a bi-objective optimization problem that jointly minimizes the worst-case transmission latency and the load imbalance, subject to constraints on edge UAV server selection and data assignment. To solve this problem, we propose a directed-evolution nondominated sorting genetic algorithm (DNSGA) that removes conventional crossover operations and incorporates two problem-specific heuristics: 1) a $K$ -means-based task assignment module to reduce latency, and 2) an efficiency-driven load migration strategy to balance server-side workloads. Simulation results verify that the proposed DNSGA converges significantly faster than nondominated sorting genetic algorithm II (NSGA-II), while providing a more diverse set of nondominated solutions with up to 13.3% broader result range. Compared to multiobjective particle swarm optimization (MOPSO), DNSGA achieves up to 18.8% reduction in worst-case latency, and compared to NSGA-II, it reduces the load imbalance by up to 41.2%. These advantages highlight its effectiveness for high-efficiency UAV ad hoc network-assisted IoT systems.

Time modulated array (TMA)-based space division multiple access (SDMA) approach is studied and verified, including the performance analysis and experiment verification. It utilizes the multi-beam characteristic of TMA to implement the SDMA. For the uplink, the main power of signals with different incident angles would be redistributed to different harmonic components after time modulation. In this case, it plays a key role in this system to determine the harmonic order, which is selected to detect the received signal. A novel harmonic selection method for the uplink TMA-based SDMA systems is proposed by maximizing the received signal power. Further, a closed-form expression of the spectral efficiency is deduced to verify the advantage of the simple harmonic selection scheme. Numerical results show that the TMA-based SDMA system can significantly improve the spectral efficiency, compared to traditional single radio frequency (RF) chain systems. Meanwhile, it does not increase the number of RF chains. An L-band 8-element TMA is fabricated and tested to validate the feasibility of the TMA-based SDMA method.

A novel periodic phase modulation method is proposed and verified for the fast diagnosis of phased array. Taking the diagnosis of receiving paths as an example, its principle is as follows. Periodic phase modulations with different frequencies are added to each phase shifter of the phased array. Because of the periodic modulation, harmonic components are generated in all the elements. As modulation frequencies added to all the elements are different from each other, the generated harmonic components have different frequencies correspondingly. If some elements suffer failure, the corresponding harmonic components will vanish from the output of the power combiner. Therefore, the faulty elements can be located by analyzing the spectrum of received signal after the power combiner. The proposed method needs no the auxiliary hardware and its signal processing is concise. An 8-element S band phased array is fabricated to examine its effectiveness.

Physical-layer security techniques can be added on top of the cryptography-based security framework, mainly relying on the multi-antenna-based beamforming and artificial noise, where yet the high-cost multiple radio frequency (RF) chains are typically required. In this letter, we exploit the emerging technique of 1-bit low-cost time-modulated metasurface alternatively, for discriminating the eavesdropper and the legitimate user. Specifically, time modulation sequences on the phase shift of each element in the metasurface enables to generate the fundamental and harmonic components with different radiation patterns. As a benefit, this controllable directional signal aliasing can be used for inducing distortion in the direction of the eavesdropper, while maintaining the signal integrity for the legitimate. A particle swarm optimization (PSO)-based optimization algorithm is proposed to optimize both the fundamental and the harmonic radiation patterns, for discriminating the signal-to-interference-plus-noise ratio (SINR) between the legitimate user and the eavesdropper. Simulation results demonstrate that the scheme proposed is capable of ensuring the reliable reception of information at the legitimate user, while restricting the bit error rate (BER) of the eavesdropper to 0.5.

The task processing capabilities of mobile edge computing (MEC) networks can be significantly enhanced through the integration of device-to-device (D2D) technology, rendering them particularly suitable for computation-intensive applications in the Internet of Things (IoT). However, the opportunistic behaviors of computation-intensive users (CUs) will reduce the fairness of resource allocation, which will increase the task offloading delay for others inevitably. To cope with this issue, a three-layer collaborative offloading scheme including edge offloading, D2D offloading, and cloud offloading is proposed to provide abundant computing resources, thus improving the fairness. Built on this scheme, the offloading mode selection, channel selection, offloading side selection, offloading ratio allocation, and computing resource allocation are jointly optimized to minimize the task processing delay while ensuring high fairness. Such an optimization problem is a mixed-integer nonlinear programming (MINLP). Therefore, an evaluation-based distributed task offloading strategy selection (E-DTOSS) algorithm is designed, where the task offloading problem can be decomposed into three sub-problems. The offloading strategy selection problem, which is NP-hard, is proven to be an exact potential game and its pure-strategy Nash equilibrium point can be obtained by the E-DTOSS algorithm. The offloading ratio allocation problem and computing resources allocation problem are solved by dual ascent method. Simulation results verify that the E-DTOSS algorithm can converge to the Nash equilibrium point while ensuring high fairness. As a result, the task processing delay can be reduced compared with the benchmark schemes.