To address the challenges of information leakage, low energy efficiency, and the Doppler effect in mobile Internet of Vehicles (IoV), this paper proposes an enhanced IoV cooperation framework, where privacy information is forwarded by the untrusted relay assisted by unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS), which can improve security and energy efficiency. To meet the requirements of green communication, we formulate a secrecy energy efficiency maximization problem by jointly optimizing the transmit power allocation, the relay’s amplification factor, the two-hop RIS phase shift matrices, and the UAV trajectory. Given the non-convex nature of this problem, we introduce an iterative algorithm based on the convex-concave procedure and Dinkelbach’s method to optimize the transmit power and amplification factor. Additionally, we conceive the majorization-minimization (MM) algorithm to optimize the two-hop RIS phase shift matrices, and a designed firefly algorithm-deep deterministic policy gradient (FA-DDPG) algorithm is proposed to obtain the UAV trajectory. Simulation results demonstrate the effectiveness of the proposed scheme in enhancing secrecy energy efficiency. Specifically, compared to the DDPG-only and FA-based schemes, the proposed scheme achieves an improvement of 33.3% and 64.2%, respectively, in secrecy energy efficiency.

In this paper, we propose a novel physical-layer key generation (PKG) scheme for orthogonal frequency division multiplexing-orbital angular momentum (OFDM-OAM) systems to significantly enhance the confidentiality capacity (CC). In the proposed scheme, we first establish the OAM channel model under uniform circular array (UCA) misalignment in the line-of-sight (LoS) channel. Facing the risk of information leakage during key negotiation, we couple key generation with the OFDM communication process. Then, we analyze the CC of the OFDM-OAM system and derive its closed-form expression. Simulation results illustrate that the proposed OFDM-OAM PKG scheme has achieved high CC compared with existing works. In addition, as the offset angle of the eavesdropper’s UCA increases, the CC increases, and the bit error rate (BER) of the eavesdropper tends to be 0.5.

Ensuring reliable and secure emergency communications in post-disaster scenarios is challenging, particularly when mobile communication infrastructures are damaged. In response to challenges in post-disaster emergency communications (PDEComs), this article proposes a cooperative relaying system (CRS) enhanced with Internet of Things (IoT) technology. The system features an airship equipped with buffers that serves as an aerial relay, designed to improve data transmission performance and communication security. To achieve this, it incorporates physical layer security techniques. Additionally, we introduce a hybrid mechanism that combines nonorthogonal multiple access (NOMA) and orthogonal multiple access (OMA), facilitating flexible resource allocation in IoT-enhanced CRSs. To fully leverage the advantages of the proposed airship-and-buffer aided CRS, we formulate a weighted secure sum rate (WSSR) maximization problem, jointly considering the power control, mode selection, and information security. Initially, we address the formulated WSSR maximization problem using Lyapunov optimization. Subsequently, the primal problem is divided into four cases, from which optimal power control and mode selection policies can be derived. This process is constrained by the stability of buffer queues and privacy transmission requirements. Finally, the simulation results show that the proposed scheme outperforms state-of-the-art schemes in terms of the WSSR. By adopting the airship and the hybrid NOMA/OMA mechanism, the WSSR can be increased by 29.9% and 96.4%, respectively. Moreover, we explore the impact of network parameters (e.g., the distance between the eavesdropper and airship, and decoding thresholds) on information security.

Autonomous aerial vehicle (AAV)-enabled Internet of Things (IoT) exhibits great application potential with its wide coverage, flexible network topology, and diversified services. However, ensuring communication security and efficient spectrum resource utilization in multiuser access scenarios is challenging, given the open nature of AAV channels and the proliferation of communication devices in IoT. To address the above challenges, this article proposes a novel reconfigurable intelligent surface (RIS)-aided AAV collaborative communication framework, where RIS-equipped AAV flexibly serves multiple users. In this work, a rate splitting multiple access (RSMA)-based secure transmission scheme is proposed, where the split public information serves both as useful signals and noise to disrupt eavesdropping. For the proposed scheme, a sum secrecy rate maximization problem is formulated and solved by optimally deploying the AAV’s location, designing the RIS’s phase shift, and power allocation. For this nonconvex problem with a couple of variables, we decompose it and form three separate subissues. Specifically, leveraging the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques, we first exploit an iterative algorithm for optimizing beamforming vectors and phase-shift matrix of RIS, and the optimal position of the AAV is obtained according to the deep deterministic policy gradient (DDPG). Then, we design an alternating optimization (AO) framework for joint solving. Finally, simulation results validate the efficacy of the proposed scheme in enhancing security, e.g., relative to the nonorthogonal multiple access (NOMA) scheme and benchmark scheme, the secrecy rate of the proposed scheme increased by 29.7% and 71.9%, respectively.

In this paper, we investigate the secrecy performance of the UAV short-packet communication system assisted reconfigurable intelligent surface (RIS). In this system, based on the phase shift differences of the RIS, the Gamma and exponential distributions are used to match the received signal-to-noise ratio (SNR) at the link terminals. Closed-form expressions for both the probability density function (PDF) and the cumulative distribution function (CDF) are derived. Based on the above PDF and CDF, we derive closed-form expressions for the average achievable rate (ASR) and the average secure block-error rate (SBLER) to evaluate the system’s security and reliability performance. In addition, a novel analytical model is proposed, which can simplify the calculation of the secrecy outage probability (SOP). Furthermore, to explore the performance boundaries, we also derive closed-form expressions for the asymptotic SOP and the asymptotic probability of positive secrecy capacity (PPSC) in high-SNR regions. The accuracy of the derived expressions is validated through simulations and numerical results, which also demonstrate the effectiveness of the proposed SOP analysis framework. In the simulation, we investigate the impact of key system parameters, such as the number of RIS elements, finite block-length channels, UAV altitude, Rician factor, as well as reliability and confidentiality constraints, on the overall system performance. The simulation results show that the proposed system provides significant performance advantages in ensuring secure and reliable transmission under various operating conditions.

This paper investigates the integration of the non-orthogonal multiple access (NOMA) technique and autonomous aerial vehicles (AAVs) in Internet of Vehicles (IoV), aiming to provide flexible access and improve communication coverage for vehicle platooning. The goal is to accurately analyze performance and reasonably optimize network design for AAV-assisted vehicle platooning in NOMA-enhanced IoV. To achieve this, an analytical solution is derived for the average achievable rate from the lead vehicle to follower vehicles over Rician fading channels. Leveraging this analytical solution, the Gauss-Chebyshev integration is employed to obtain the approximate solution. Then, we formulate a problem of maximizing the sum of secure rates by optimizing the trajectory and spectrum allocation. The formulated problem is constrained by the security requirement and imperfect channel state information. Addressing the NP-hard nature of this problem, an iterative optimization algorithm is developed, incorporating Q-learning and the graph theory to alternately adjust the trajectory and spectrum allocation. Finally, the simulation results show that the approximate solution matches well with the analytical solution, and the gap is less than 6%. Moreover, the proposed scheme has a significant performance improvement in the sum of secure rates compared with the state-of-the-art schemes.

This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS’s phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV’s trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.

As an important part of Internet of Things (IoT), the Internet of Vehicles (IoV) has been widely used in traffic intersection control, automatic driving, intelligent navigation, etc. However, due to the dynamic topology and high mobility, IoV faces the challenge of frequent disconnections, which will lead to deterioration in the performance of data dissemination. Motivated by the above, air-to-ground (A2G) integrated IoV is used to bridge the communication gaps between terrestrial vehicles to achieve efficient information transmissions. This paper investigates the application of low altitude platform stations (LAPSs) and reconfigurable intelligent surface (RIS) in A2G integrated IoV, where multiple relaying LAPSs equipped with RISs are adopted to improve the spatial multiplexing gain and create the smart radio environment. To make full use of the advantages of LAPS-and-RIS enhanced transmissions, we formulate a weighted sum rate (WSR) maximization problem by jointly considering the location, power, and phase shift. To tackle this challenging non-convex problem, we design an iterative optimization scheme, where three optimization variables are processed in turn. Simulation results demonstrate that the proposed WSR maximization scheme can significantly improve the communication performance in comparison with other state-of-the-art schemes and the baseline scheme.

Unmanned aerial vehicle mounted base stations (UAV-BSs) are expected to become an integral component of future intelligent transportation systems, which can provide seamless coverage for vehicles on highways with poor cellular infrastructures. Motivated by the above, this paper proposes an aerial-ground integrated vehicular networking architecture, based on which a UAV-vehicle collaboration perspective is proposed. Specifically, an emerging vehicle-to-UAV (V2U) and vehicle-to-vehicle (V2V) collaboration framework is first presented to facilitate diverse vehicular applications. Next, we investigate the coverage radius maximization problem by optimizing the UAV-BS altitude. Meanwhile, by taking the channel state information (CSI) feedback delay into account, we formulate a V2U communication sum rate maximization problem by optimizing the power control and spectrum allocation, which is constrained by the capacity and reliability requirements. Then, we derive the closed-form expression of optimal UAV-BS altitude. Afterwards, we decouple the formulated sum rate maximization problem, and devise an efficient algorithm with polynomial complexity, where the optimal power control and spectrum sharing are solved. Finally, simulation results demonstrate that the maximum coverage radius and optimal UAV-BS altitude can be achieved by our proposed scheme in different urban environments. In addition, our designed scheme can effectively improve the V2U communication sum rate in comparison with the current works.

In the Internet of Connected Vehicles, caching content frequently requested by users on edge devices can reduce access latency. Particularly in high-traffic density areas, Unmanned Aerial Vehicles (UAVs) can integrate into future cellular networks to enhance the network capacity and meet increased requests. Therefore, we formulate a joint optimization problem of cooperative caching of Base Station (BS) and UAVs and UAV trajectory planning to minimize network latency while considering the limited energy and storage capacity and dynamic vehicles. First, we propose a Temporal-evolving Bipartite Graph Neural Networks (TBGN) model for traveling areas prediction of vehicles. Then, regarding the coupling of optimization variables, we propose an Energy-aware Monte-Carlo Tree Search algorithm to optimize the UAV’s service trajectory by predicted spatio-temporal vehicle density. Finally, the optimization problem degenerates into a monotonic submodular function to optimize caching decisions. We utilize real vehicle trajectories for simulations. The results show that the TBGN outperforms other advanced models in terms of mobility prediction accuracy by 7.4%, and the proposed scheme reduces average latency by 16% compared to other schemes.

The proposed study introduces a reconfigurable intelligent surface (RIS)-aided hybrid radio frequency (RF)/free space optical (FSO) system with an unmanned aerial vehicle (UAV) relay to enable an ultra-dense sixth-generation (6G) network. The channels for RF and FSO are represented by Rayleigh and Gamma-Gamma probability distributions, correspondingly. Additionally, the network includes a terrestrial eavesdropper that follows the Nakagami-m distribution, attempting to breach confidential information. To counter this threat, RIS technology is used to enhance the hybrid system's secrecy. The study conducts a closed-form analysis of the secrecy outage probability (SOP) and obtains its asymptotic expression for determining the diversity order and coding gain. Theoretical findings have been confirmed through thorough numerical simulations implemented with the Monte-Carlo approach. The findings demonstrate the RIS technology's effectiveness in enhancing the network's secrecy performance.

In this article, we present a drone-relayed vehicular networking architecture, which aims to improve the achievable data rate of cell-edge vehicles in rural highway scenarios. Specifically, we first incorporate the decode-and-forward (DF) relay protocol with the nonorthogonal multiple access (NOMA) and maximum ratio combining (MRC) techniques, based on which an NOMA- and MRC-Enabled framework is proposed. Next, to fully exploit the advantages of the proposed framework, we separately formulate the total achievable data rate maximization and energy consumption minimization problems by jointly considering the height and 2-D trajectory optimization of relaying drone. The formulated energy consumption minimization problem is transformed into a trajectory optimization problem with obstacle avoidance constraints. Then, for the total achievable data rate maximization problem, we utilize the golden section method to design a height optimization scheme with polynomial complexity. Afterward, we improve the particle swarm optimization (PSO) algorithm, and present an effective 2-D optimization scheme. In addition, the performance superiority of the proposed NOMA- and MRC-Enabled framework is analyzed theoretically. Finally, simulation results verify the efficacy of the proposed height and trajectory optimization schemes. For instance, by using the NOMA and MRC techniques, the total achievable data rate can be improved by 24.4%. Moreover, within the same running time, a shorter trajectory can be obtained by adopting our presented trajectory optimization scheme in comparison with the current works.

Space–air–aqua integrated network (SAAIN) is an emerging maritime network architecture to support reliable and timely communications. Considering the computation capability and information security in maritime transportation systems, this work proposes a double-edge secure offloading scheme, where both base-station (BS) and satellites provide secure mobile-edge computing services for delay-sensitive applications. Specifically, maritime mobile users may offload their computation tasks adaptively to the BS or satellites securely relayed by unmanned aerial vehicles (UAVs). To minimize offloading delay, we formulate an optimization problem to allocate the transmit power cooperatively via UAVs’ trajectory optimization. Moreover, jamming UAVs are deployed to protect the offloading process. For such a nonconvex optimization problem, two iterative algorithms are proposed to determine the transmit power and design the UAVs’ trajectories. Numerical results show the effectiveness of the proposed scheme in terms of offloading delay.

By shifting the requested content to the edge in the Internet of Vehicles (IoV), edge caching is expected to be an effective solution to satisfy the low latency and high-reliability requirements of IoV users for multimedia services. However, the edge node’s coverage area and storage space are limited. Moreover, since vehicles have high mobility and in-vehicle multimedia applications require sequential delivery for contents, we need to address two main issues: 1) how to optimize the proactive content caching decision (i.e., the placement of cached content chunks) among edge nodes (ENs) to provide better Quality of Services (QoS) for IoV users and 2) how to ensure that vehicles can download the required contents sequentially to improve Quality of Experience (QoE). In this article, we propose a mobility-aware proactive edge caching scheme (MSTPS), where the spatial and temporal prediction of vehicles are taken into account for content deployment and scheduling. Specifically, we optimize the caching decision based on predicting the vehicle’s driving trajectory and travel preference. The scheme learns the vehicle’s travel preferences to cope with mobility uncertainty by combining users with similar travel patterns. Meanwhile, the proposed scheme can support the sequential downloading of content chunks. Furthermore, in order to deal with the dynamic characteristics and unpredictable challenges of the IoV, we design a system recovery strategy, which can avoid the degradation of the proposed scheme due to the failure of prediction. Finally, by using real mobility data sets and scenarios, we explore the impact of the number of ENs deployed in advance for each vehicle’s request when the cache needs to be updated on system performance. In addition, we evaluate the effectiveness of the proposed scheme. Our proposed scheme can achieve the best cache hit ratio and decrease caching costs compared to the existing mobility-aware in-order caching schemes.

Medical-care sensor networks promote the rapid development of telemedicine applications. However, in poverty-struck, disaster-struck or remote areas with limited infrastructures, it is difficult to provide fast and timely medical-care services. To address this challenge, we propose an aerial base station (ABS)-assisted medical-care sensor network, based on which the data transmission problem is investigated by jointly considering the anti-interference and anti-collision requirements. Specifically, in order to reduce the bit error rate caused by electromagnetic interferences, we first design an anti-interference method based on M-ary spread spectrum and multi-carrier modulation. Then, by introducing a multi-frequency sensor identification mechanism, an anti-collision method based on time division multiple access and frequency division multiple access is presented. Finally, simulation results demonstrate that our proposed scheme has significant advantages in anti-collision and anti-interference compared with current schemes. In quad-interference scenarios, the anti-interference performance is improved by 5.3 dB. Moreover, the anti-collision performance is also increased by 17.2%. Furthermore, in scenarios with a large number of sensors, the successful sensor identification percentage is always greater than 50%.

Radio signals are electromagnetic waves that are propagated in freespace. This nature makes it vulnerable to be attacked from eavesdroppers. Fortunately, with the aid of the reconfigurable intelligent surface (RIS), which passively reflects the incident signal, the spatial distribution of the signal strength can be customized to benefit legitimate users. In this work, we propose a RIS aided non-orthogonal multiple access (NOMA) transmission scheme to provide secure links for two users, where the unmanned aerial vehicle (UAV) equipped with RIS serves as a relay to change radio coverage flexibly. In the proposed scheme, the NOMA transmit power of base-station (BS), the UAV's location, and the RIS phase shift are jointly optimized to maximize the secure transmission rate, which is a nonconvex optimization problem. For this non-convex problem, we first decompose it into three subproblems. Then an efficient iterative algorithm is proposed, where the transmit power and UAV's location are optimized through the successive convex approximation (SEA) method, and the phase shift is optimized through the semi-definite relaxation (SDR) strategy. Numerical results verify the secrecy superiority of the proposed scheme compared with the current schemes.

Medical-care Internet of Things enables rapid medical assistance by providing comprehensive and clear healthy information. However, due to the limited infrastructure, it is difficult to quickly and securely transmit medical-care information in poverty-stricken or disaster-stricken areas. To tackle the above situation, in this article, we propose a delay-sensitive secure nonorthogonal multiple access (NOMA) transmission scheme with the high-altitude platform (HAP) and low-altitude platforms (LAPs) cooperated to securely provide delay-sensitive medical-care services. In the proposed scheme, we first design a novel HAP–LAP secure transmission framework to provide NOMA communication services to multiple hotspots. Constrained by the limited power and spectrum, we formulate an optimization problem, such that the privacy information delay is minimized. For thisnonconvex optimization problem, we design an alternating optimization framework, where the power, spectrum, and LAPs’ location are tackled in turn. In addition, we theoretically analyze the performance superiority compared with the orthogonal multiple access scheme and derive the secrecy outage probability closed-form expression. Finally, numerical results show the performance superiority of the proposed scheme compared with the current works with respect to the secure information delay.

A non-orthogonal multiple access (NOMA)-enabled double-layer airborne access vehicular ad hoc networks (DLAA-VANETs) architecture is designed in this paper, which consists of a high-altitude platform (HAP), multiple unmanned aerial vehicles (UAVs) and vehicles. For the designed DLAA-VANETs, we investigate the UAV deployment and network optimization problems. In particular, a UAV deployment scheme based on particle swarm optimization is presented. Then, the NOMA technique is introduced into the designed architecture, which can improve the transmission rate. Afterward, we take the information security into account and formulate a downlink total transmission rate maximization problem by optimizing UAV height and subcarrier allocation. For tackling this non-convex problem, we decouple this downlink total transmission rate maximization problem as two subproblems, where UAV height and subcarrier allocation problems are solved in turn. Moreover, the transmission performance of the designed DLAA-VANETs is analyzed, based on which the security outage probability (SOP) is derived. Finally, simulation results demonstrate that the presented UAV deployment scheme can maximize the relay coverage ratio. In addition, the proposed can achieve a higher downlink total transmission rate in comparison with the current works.

In wireless mobile sensor networks, mobile sensors are usually composed of some mobile carriers equipped with sensors. In daily life, wireless mobile sensors need to be monitored, reconnaissance, and maintenance in hazardous areas. Because there is no specific infrastructure for centralized control in this network, in order to meet coverage requirements in a particular environment, wireless mobile sensors are often required to be moved to a specific location in a decentralized manner. How to design a mobile control coverage algorithm that controls the moving direction and moving position of each mobile sensor becomes a very important research direction. In this paper, based on the existing K-nearest neighbor rules, we propose a coverage algorithm of K-nearest neighbor based on communication beacon, which can be applied to wireless sensor networks to solve the coverage problem. We propose K neighbor node determination rules, establish a neighbor model, and give the determination principle of the neighbor node connection matrix. The simulation results show that the coverage algorithm is more efficient than the traditional K-nearest neighbor algorithm, and we find that improving the transmit power and reducing the transmission bit length can improve the coverage efficiency.